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53 Cognitive Style

Maria Kozhevnikov, Martinos Center, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, National University of Singapore, Singapore

• Published: 03 June 2013
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This chapter will review research on cognitive style from different traditions in order to revaluate previous and existing theoretical conceptions of cognitive style and to redefine cognitive style in accordance with current cognitive science and neuroscience theories. First, this chapter will review conventional and applied research on cognitive style that introduces the concept of cognitive style as patterns of adaptation to the external world and demonstrate that, although cognitive style develops on the basis of innate abilities, it is modified further as a result of changing environmental demands. Next, we will review the latest trends in cognitive style research that integrate different style dimensions into unifying models as well as recent findings in transcultural neuroscience that have documented the existence of culturally sensitive individual differences in cognition and suggested a close relationship between sociocultural environment and specific neural and cognitive patterns of information processing. Finally, based on our review, we will redefine cognitive style as ontogenetically flexible individual differences representing an individual’s adaptation of innate predisposition to external physical and sociocultural environments and expressing themselves as environmentally and culturally sensitive neural and/or cognitive patterns of information processing.

Historically, the term “cognitive style” refers to consistencies in an individual’s manner of cognitive functioning, particularly in acquiring and processing information (Ausburn & Ausburn, 1978 ). Messick ( 1976 ) defines cognitive styles as stable attitudes, preferences, or habitual strategies that determine individuals’ modes of perception, memory, thought, and problem solving. Witkin, Moore, Goodenough, and Cox ( 1977 ) characterize cognitive style as individual differences in the way people perceive, think, solve problems, learn, and relate to others.

While it seems obvious that there are differences among individuals’ preferred ways of processing information, what these differences mean and how they might be captured is less apparent. Despite being extremely popular throughout the 1950s–1970s, research on cognitive style has lost much of its appeal and has been seriously questioned in recent decades, and currently, many cognitive scientists are on the verge of accepting that cognitive style research has reached a standstill. The main reasons for this decline of interest in cognitive style seem to be the lack of a coherent organizing framework, and the lack of understanding of how cognitive style maps onto other psychological concepts and theories (see Kozhevnikov, 2007 , for a review). According to its definition, cognitive style should refer to the way individuals process information; however, since the vast majority of cognitive style studies were conducted before the rise of cognitive science, the concept of cognitive style has not been integrated with contemporary cognitive science theories, and the relationship between cognitive style’s and cognitive psychology’s approaches to individual differences in cognition has not been established (Kozhevnikov, 2007 , for a review).

Cognitive psychologists and neuroscientists researching individual differences in cognitive functioning have often focused on such basic dimensions of individual differences as speed of processing, working memory capacity (WMC), and general fluid intelligence ( Gf ). Overall, these reflect stationary individual differences in cognition, in the sense that these individual differences are largely genetically predetermined (Ando, Ono, & Wright, 2001 ; Deary, Penke, & Johnson, 2010 ; Friedman et al., 2008 ) and exhibit only limited ontogenetic sensitivity and training-induced plasticity (e.g., Sayala, Sala, & Courtney, 2005 ). Cognitive style researchers, in contrast, originally introduced the concept of cognitive style as specific modes of adjustment to the external world (Klein, 1951 ; Witkin, Dyk, Faterson, Goodenough, & Karp, 1962 ) modifiable by sociocultural and life experiences, and they have been primarily interested in more flexible , ontogenetically malleable individual differences that are shaped as a result of physical and sociocultural influences.

The goal of the current chapter is to incorporate the concept of cognitive style into current cognitive science theories of individual differences by integrating research findings on individual differences in cognition and cognitive styles from three different research perspectives: (1) cognitive style, (2) cognitive psychology and neuroscience, and (3) transcultural psychology and neuroscience. First, this chapter will review conventional research on cognitive style that introduces the concept of cognitive style as patterns of adaptation or specific modes of adjustment to the external world. Next, the chapter will review cognitive style research in applied fields (education, management) demonstrating that, although cognitive style develops on the basis of innate abilities, it is modified further as a result of changing environmental demands and life experiences, and it must thus be thought of not only in terms of innate predispositions but as a flexible construct, in terms of sociocultural interactions regulating an individual’s behavior. Third, we will summarize the latest trends in cognitive style research that have attempted to integrate the variety of cognitive style dimensions into unifying hierarchical models, and we relate these models to information processing theories. Fourth, we will review recent findings in transcultural psychology and neuroscience that have documented the existence of culturally sensitive individual differences in cognition and suggested a close relationship between sociocultural environment and specific neural and cognitive patterns of information processing.

Finally, based on our review, we will suggest a dissociation between (1) stationary individual differences that are determined primarily by genetic factors and exhibit only limited sensitivity to ontogenetic (environmental and sociocultural) factors; and (2) flexible individual differences or cognitive styles , whose formation, although affected by genetic factors, is largely influenced by environmental and sociocultural factors during ontogenetic development. According to the aforementioned approach, we will redefine the concept of cognitive style as ontogenetically flexible individual differences representing an individual’s adaptation of innate predisposition to external physical and sociocultural environments and expressing themselves as environmentally and culturally sensitive neural and/or cognitive patterns of information processing .

“Conventional” Cognitive Style

“Conventional” cognitive style research began in the late 1940s, with experimental research (e.g., Hanfmann, 1941 ; Klein, 1951 ; Klein & Schlesinger, 1951 ; Witkin & Ash, 1948 ) that focused on identifying the existence of consistent individual differences in performance on lower order cognitive tasks (e.g., perception, simple categorization). For example, Hanfmann ( 1941 ) identified two groups of individuals: those who preferred a perceptual approach when grouping blocks, and others who preferred a more conceptual approach. Klein ( 1951 ) identified “sharpeners,” who tended to notice differences between visual stimuli, and “levelers,” who tended to notice similarities.

These individual differences were first conceptualized as cognitive styles in the early 1950s, with Klein ( 1951 ) terming them as “perceptual attitudes.” These perceptual attitudes were defined as patterns of adaptation to the external world that regulate an individual’s cognitive functioning. According to Klein, adaptation requires balancing one’s inner needs with the requirements of the external environment. Klein also reported a relationship between cognitive style and personality; levelers exhibited a “self-inwardness” pattern characterized by “a retreat from objects, avoidance of competition,” while sharpeners were more manipulative and active (Klein, 1951 , p. 339). Klein considered both poles of the leveling/sharpening dimension as equally valid ways for individuals to achieve a satisfactory equilibrium between their inner needs and outer requirements, but different in their repertoire of psychological functions. Several years later, based on Klein’s findings, Holzman and Klein ( 1954 , p. 105) defined cognitive styles as “generic regulatory principles” or “preferred forms of cognitive regulation” in the sense that cognitive styles are an “ organism’s typical means of resolving adaptive requirements posed by certain types of cognitive problems ,” emphasizing the adaptive and flexible nature of cognitive style.

Around the same time, Witkin et al. ( 1954 ) carried out his large-scale experimental study on field dependence/independence, which was central to the further development of cognitive style research. The goal of this study was to investigate individual differences in perception and to associate these differences with particular trends in personality. Subjects were presented with a number of orientation tests aimed at examining their perceptual skills (e.g., Rod-and-Frame Test, in which the subjects determined the upright position of a rod, or Embedded Figure Test [EFT], in which the subjects were asked to find a simple figure inside a complex one) along with various personality measures. Two main groups of subjects were distinguished: field dependent (FD), those who exhibited high dependency on the surrounding field; and field independent (FI), those who displayed low dependency on the field. There was also a significant relationship between subjects’ performance on perceptual tests and their personality characteristics: FD individuals were more attentive to social cues than FI individuals. In contrast, the FI group had a more impersonal orientation than the FD group, exhibiting psychological and physical distancing from others. Witkin concluded that that the “core” of cognitive style is rooted in an individual’s innate predispositions, such as abilities or personality. Furthermore, Witkin explained individual differences in perception as outcomes of different modes of adjustment to the world, concluding that both FD and FI groups have specific components that are adaptive to particular situations. According to Witkin, Dyk, Paterson, Goodenough, and Karp ( 1962 ), field dependence reflects an earlier and less differentiated mode of adjustment to the world, and field independence reflects a later and more differentiated mode. However, although a highly differentiated FI individual could be highly efficient in perceptual and cognitive tasks, he or she may exhibit inappropriate responses to certain situational requirements and be in disharmony with his or her surroundings. Thus, both Klein and Witkin introduced the notion of cognitive style as patterns or modes of adjustment to the world, which appeared to be equal in their adaptive value but different in their level and repertoire of psychological and/or perceptual functions.

In the late 1950s, Klein’s and Witkin’s idea of bipolarity (value-equal poles of cognitive style dimensions in terms of adaptive nature) spawned a great deal of interest. As a result, a tremendous number of studies on “style types” appeared in the literature. The most commonly studied cognitive styles of this period are impulsivity/reflectivity (Kagan, 1966 ), tolerance for instability (Klein & Schlesinger, 1951 ), breadth of categorization (Pettigrew, 1958 ), field articulation (Messick & Fritzky, 1963 ), conceptual articulation (Messick, 1976 ), conceptual complexity (Harvey, Hunt, & Schroder, 1961 ), range of scanning, constricted/flexible controls (Gardner, Holzman, Klein, Linton, & Spence, 1959 ), holist/serialist (Pask, 1972 ), verbalizer/visualizer (Paivio, 1971 ), and locus of control (Rotter, 1966 ). Attempting to organize these numerous dimensions, Messick ( 1976 ) proposed a list of 19 cognitive styles; Keefe ( 1988 ) synthesized a list of 40 separate styles.

One of the serious limitations of conventional cognitive style research was its narrow focus on lower order cognitive tasks, often assessed by performance ability measures (error rate and response time) with simple “right” and “wrong” answers, which is hypothetically more relevant in testing abilities , not styles. Most of the perceptual tasks used as measures of cognitive style were tapping relatively stationary individual differences related to personality or intelligence. Ironically, this fact appears especially clear in the most commonly used instruments to measure cognitive styles, such as Witkin’s EFT (Witkin et al., 1954 ) and Kagan’s Matching Familiar Figures Test (MFFT; Kagan, Rosman, Day, Albert, & Phillips, 1964 ). While these instruments were supposed to measure bipolar dimensions representing two equally efficient ways of solving a task, in reality, one strategy was usually more effective than the other (e.g., FI subjects usually perform better than FD on many spatial tasks). It is not surprising then that many researchers who have investigated the correlation between intelligence tests and conventional measures of field dependence such as the Rod-and-Frame or EFT (e.g., Cooperman, 1980 ; Goodenough & Karp, 1961 ; McKenna, 1984 ) consistently report higher intelligence among individuals with an FI style than among those with an FD style.

Thus, despite that literature of that period has suggested the adaptive nature of cognitive style, and proposed that cognitive style refers to specific modes of adjustment to the external world (Klein, 1951 ; Witkin et al., 1962 ), early research on cognitive styles often used measures of individual differences sensitive mostly to genetic factors, and it did not clearly distinguish those from adaptive, ontogenetically malleable traits. This caused a situation in which the cognitive styles under study closely resembled genetically predetermined cognitive abilities, sparking later debates as to whether cognitive style and ability were indeed the same. Furthermore, since the majority of the aforementioned studies were conducted before the advent of cognitive science, their main problem was the lack of a unifying theoretical approach to information processing, which could lay the foundation for systematizing numerous overlapping cognitive style dimensions (see Kozhevnikov, 2007 for a review). Consequently, the promising benefits of studying cognitive styles were lost amidst the chaos, and the amount of work devoted to the cognitive style construct declined dramatically by the end of the 1970s, ironically, only a few years before information processing and cognitive science stepped into the forefront of contemporary psychology. Thus, although cognitive style refers to ways of processing information, since the majority of interest in cognitive style was abandoned before the rise of the information processing approach, a close relationship between cognitive style and other psychological concepts from contemporary information processing theories was never properly established.

Research in Applied Fields: Sociocultural Components of Cognitive Styles

Despite declining theoretical interest in conventional cognitive styles toward the end of the 1970s, the number of publications on cognitive styles in applied fields has continued to increase, reflecting an assumption of a practical necessity of understanding cognitive styles and their important role in real-life activities. Applied research on cognitive style focused on the existence of styles related to higher order cognitive functioning, such as problem solving, decision making, learning, and explanation of causality, as reviewed next.

Kirton ( 1976 , 1989 ) was the first to consider “decision-making styles” within the cognitive style framework by introducing the adaptor/innovator dimension of managerial style. Kirton defined adaptors as preferring to accept generally recognized policies while proposing ways of “doing things better,” and innovators as those who question the problem itself and propose ways “for doing things differently”; Kirton proposed that these differences were evident in personality as well as creativity and problem-solving strategies. Kirton ( 1989 ) investigated the adaptation/innovation dimension in organizational settings, widening the concept of cognitive style to characterize not only individuals but also the prevailing style in a group situation (called “organizational cognitive climate”). Kirton argued that overall cognitive climate stems from members of a workgroup sharing similar cognitive styles, that is, with all members within one-half standard deviation around the mean for the workgroup. Other studies on managerial decision-making styles were conducted by Agor ( 1984 ), who introduced three broad types of management styles in decision making: intuitive, analytical, and integrated problem-solving styles. Agor ( 1984 ) surveyed 2,000 managers of various occupations and managerial levels, and cultural backgrounds, and although it is not clear whether the differences cited are indeed statistically significant, Agor states that the data showed variation in executives’ dominant styles of management practice by organizational level, service level, and gender and ethnic background (e.g., women are more intuitive than men, managers of Asian background are more intuitive than the average manager). As did Kirton, Agor pointed out that one’s decision-making style not only includes stable individual characteristics but also applies to interpersonal communications and group behavior.

Rowe and Mason ( 1987 ) proposed a model of decision-making styles based on cognitive complexity (i.e., an individual’s tolerance for ambiguity) and environmental complexity (people-oriented vs. task-oriented work environment). The four styles derived from this model are directive (practical, power-oriented), analytical (logical, task-oriented), conceptual (creative, intuitive), and behavioral (people-oriented, supportive). Rowe and Mason stressed the importance of cognitive style in career success. More recent studies on styles in managerial fields have supported similar ideas. First, that cognitive style is a key “determinant of individual and organizational behavior, which manifests itself in both individual workplace actions and in organizational systems, processes, and routines” (Sadler-Smith & Badger, 1998 , p. 247). Second, although tending to be relatively stable, cognitive styles interact with the external environment and can be modified in response to changing situational demands, as well as influenced by life experiences (Allison & Hayes, 1996 ; Hayes & Allinson, 1998 ; Leonard & Straus, 1997 ).

At the same time, by the end of 1970s, a large number of “personal cognitive styles” have arisen in psychotherapy, such as optimistic/pessimistic, explanatory, anxiety-prone, and others (Haeffel et al., 2003 ; Peterson et al., 1982 ; Seligman, Abramson, Semmel, & von Baeyer, 1979 ). One of the first and most elaborated personality-related styles to be used widely in psychotherapy was the explanatory (attributional) style that reflects differences in the manner in which people habitually explain the causes of uncontrollable events (attributing the cause to internal vs. external circumstances). The cognitive component, according to this theory, refers to the ways in which people perceive, explain, and extrapolate events in their lives. Furthermore, the attribution theory suggests that styles are not always inherent to one’s personality and intelligence and, although relatively stable, can be acquired as a result of an individual’s interaction with the external environment (Peterson, Maier, & Seligman, 1993 ). It requires some amount of repetition of life events or observing other people’s behavior to reinforce or inhibit a certain style.

The Myers-Briggs Type Indicator (MBTI) remains the major tool for describing personal styles (Myers, 1976 ; Myers & McCaulley, 1985 ). The MBTI is a self-report instrument, which was developed based on four of Jung’s ( 1923 ) personality dimensions, extraversion/introversion (EI), sensing/intuition (SI), thinking/feeling (TF), and judging/perceiving (JP). Permutations of the four dimensions form 16 psychological types identified by the MBTI. Although evidence supporting the MBTI as a valid measurement of style is inconclusive (see Coffield, Moseley, Hall, & Ecclestone, 2004 , for a review), and there has been considerable controversy regarding its measurement characteristics (Carlson, 1989 ; Healy, 1989 ; McCaulley, 1991 ) and construct validity (e.g., Bess & Harvey, 2002 ; Girelli & Stake, 1993 ), similar to other “applied” approaches, the MBTI assumes close connections between one’s style and professional specialization and that certain professional settings are suited to individuals with different personality profiles.

The applied field that generated the greatest number of studies on styles was education. In education, research on style was aimed at understanding individual differences (preferences) in learning processes, and thus they were called “learning styles.” One of the first models of learning styles was proposed by Kolb ( 1974 , 1984 ), who suggested that the “cycle of learning” involves four adaptive learning modes—two opposing modes of grasping experience: concrete experience (CE) and abstract conceptualization (AC); and two opposing modes of transforming experience: reflective observation (RO) and active experimentation (AE). Kolb suggested the relationship between learning styles and educational or professional specialization, showing that different job requirements might cause changes in learning styles. Other research on learning styles has focused on the development of psychological instruments to assess individual differences in complex classroom situations (e.g., Dunn, Dunn, & Price, 1989 ; Entwistle, 1981 ; Schmeck, 1988 ). These studies all showed a close connection between educational environment and cognitive style. Overall, learning style research establishes the importance of how education might affect cognitive style and also how cognitive style may affect an individual’s preference for certain educational environments.

The main problem with applied research on cognitive style is similar to the problem with conventional cognitive style research: If in the conventional cognitive style research the number of styles was defined by the number of cognitive tasks used as assessors, here the number of styles was defined by the number of applied fields in which styles were studied. As a consequence, the cognitive style construct multiplied, and in addition to conventional cognitive styles, the new terminologies of decision-making styles, learning styles, and personal styles were introduced in the mid-1970s, without clear definitions of what they were or how they differed from conventional cognitive styles. Despite their problems, one of the most significant contributions of the applied studies to cognitive style research is the examination of how external, social-environmental factors affect the formation of cognitive style. Most of the applied studies on cognitive style converged on the conclusion that cognitive styles, although relatively stable, adapt to changing environmental and situational demands and can be modified by life experiences. Furthermore, evidence has accumulated regarding the connection between an individual’s cognitive style and the requirements of different social groups—including parent–child relationships, educational and professional societies, and sociocultural environment. Thus, the early definition of cognitive styles as patterns of adjustment to the world was further specified to include descriptions of particular requirements of social and professional groups on an individual’s cognitive functioning. Cognitive styles became related not only to personality, ability, or cognition but also to social interactions regulating beliefs and value systems.

Another significant contribution by this line of research is that it expanded the concept of cognitive style to include constructs that might operate not only at perceptual or simple cognitive levels but also on complex, higher order cognitive levels (decision making, learning preferences). Overall, the applied studies on cognitive style seem to more apparently reflect the flexible nature of cognitive styles (e.g., interaction and development within professional and educational settings; relevance for sociocultural interactions and overall sociocultural context).

Recent Developments in Cognitive Style Research

Since the 1970s, conventional cognitive style and applied cognitive style studies have been joined by new trends in cognitive style research, which can be divided into three rough categories. The first includes studies that suggest the existence of cognitive styles (e.g., mobility-fixity), or “metastyles,” that operate on the metacognitive level. The second category contains studies that attempt to unite existing models of cognitive style into a unifying theory with a limited number of central dimensions, culminating in a few theoretical studies that aim to build multilevel hierarchical models of cognitive styles.

The Mobility/Fixity Dimension: “Metastyle”

Studies of the mobility/fixity (also called flexibility/rigidity) dimension attempted to address contradictory results from previous research on conventional cognitive styles, namely, the mobility of cognitive style. Witkin was the first to point out that there might be “mobile” individuals who possess both FD and FI characteristics, and can employ one style or the other depending on the situation (Witkin, 1965 ; Witkin et al., 1962 ). According to Witkin, while FI individuals as a group tend to be creative, FI individuals who also possess FD characteristics may be even more creative, since such mobility signifies greater diversity in functioning and is more adaptive than fixed use of a single style.

Furthermore, Niaz ( 1987 ) administered the ETF and Raven’s Standard Progressive Matrices Test to a group of college freshmen to assess their field dependence/independence and intelligence level. In addition, participants received the Figural Intersection Test to measure mobility/fixity. According to their results on the EFT and Figural Intersection Test, four groups of participants were identified: mobile FI, mobile FD, fixed FI, and fixed FD. Niaz reported that the fixed FI group of students received the highest intelligence scores among all the groups on the Raven’s Matrices Test, while mobile individuals (both FD and FI) performed significantly better than all other groups in college chemistry, mathematics, and biology classes. Niaz ( 1987 , p. 755) concluded that “mobile subjects are those who have available to them both a developmentally advanced mode of functioning (field-independence) and a developmentally earlier mode (field-dependence).” Furthermore, she also concluded that in mature individuals, fixed functioning implies a certain degree of inflexibility and inability to regress to earlier perceptual modes.

Furthermore, Russian psychologist Kholodnaya ( 2002 ) suggested a quadripolar model of several conventional cognitive styles: field dependence/independence, wide/narrow categorization, constricted/flexible cognitive control, and impulsivity/reflectivity. Participants were administered a number of different cognitive style and intelligence tests (EFT, the MFFT, Raven’s Matrices, Stroop task) and a word sorting task. Using cluster analysis, four clusters in the field-dependency/independency dimension were identified. One seemed to represent fixed FI individuals, who demonstrated high scores on EFT; however, they showed high interference and longer response times in the Stroop task, as well as low concept formation ability (as measured by the word sorting task). In contrast, another cluster, representing mobile field independence , included individuals who, along with high EFT scores, showed relatively high performance on the word sorting task, lower conflict on the Stroop task, and higher ability to integrate sensory information with context. The other two clusters, fixed FD and mobile FD , were similar in their relatively poor response on the EFT. However, in contrast to fixed FD, mobile FD individuals exhibited low cognitive conflict in the Stroop task and better ability to coordinate their verbal responses with presented sensory information. Kholodnaya found similar patterns for each of the following dimensions: constricted/flexible cognitive control, impulsivity/reflexivity, and narrow/wide range of equivalence. She concluded that mobile individuals can spontaneously regulate their intellectual activities and effectively resolve cognitive conflicts. In contrast, fixed individuals are unable to adapt their strategies to the situation and exhibit difficulties in monitoring their intellectual activity. Thus, according to Kholodnaya, cognitive style represents the extent to which the metacognitive self-monitoring mechanisms are formed in a particular individual, and in the case of a fixed individual, it is more appropriate to talk about a cognitive deficit rather than a cognitive style.

The important contribution of this research is that it introduces the notion of metacognition to the field of cognitive style. However, there is little support for the conclusions that fixed individuals exhibit cognitive deficits, or that cognitive style can be reduced to metacognition, or that the majority of the cognitive styles are quadripolar dimensions. Rather, the results seem to suggest that individuals are different in the extent to which they exhibit flexibility and their degrees of self-monitoring in their choices of different cognitive styles. Mobility/fixity may be better viewed as a metastyle representing the level of flexibility with which an individual applies a particular style in a particular situation. More recently, Kozhevnikov ( 2007 ) suggested that the mobility/fixity dimension represents a superordinate metastyle dimension, which serves as a control structure for other subordinate cognitive styles. That is, metastyle represents the developmental level of an individual’s metacognitive mechanisms—the ability to consciously control and situationally adapt one’s own active problem-solving strategies to the degree that he or she has a number of available potential solutions and strategies (which may involve opposing cognitive styles) and can select the most appropriate one for any given task.

Toward Hierarchical Models of Cognitive Style

The unifying trend emerged in the 1990s as a general response to vagueness in the cognitive style field, and it aimed to unite and systematize multiple cognitive style dimensions. For example, Hayes and Allinson ( 1994 ) proposed that the different dimensions of cognitive style can be considered as variations of an overarching analytical-intuitive dimension. Others characterize cognitive style as consisting of two orthogonal dimensions, such as holistic-analytical versus visualizer-verbalizer (Hodgkinson & Sadler-Smith, 2003 ; Riding & Cheema, 1991 ). However, these models of cognitive style were too simplistic; they tried to reduce cognitive style to a limited number of dimensions, rather than build a theory that systematizes known styles into a multidimensional structure. Generally, models of cognitive style do not consider cognitive style in the context of information processing theories; they neither attempt to relate cognitive styles to other psychological theories, nor do they fully account for the complexity of the cognitive style construct. Miller ( 1987 ) was the first to consider cognitive style in the context of information processing and proposed that cognitive style consisted of a horizontal analytical-holistic dimension and a vertical dimension representing different stages and levels of information processing, such as perception, memory (representation, organization, and retrieval), and thought. However, Miller’s model has been criticized for its lack of empirical support, and that the placement of cognitive style dimensions into the model was based more on convenience than research evidence or theoretical framework (Messick, 1994 ; Zhang & Sternberg, 2006 ).

There have been few empirical attempts to systematize cognitive style dimensions (e.g., Leonard, Scholl, & Kowalski, 1999 ; Bokoros, Goldstein, & Sweeney, 1992 ). For example, Bokoros et al. ( 1992 ) conducted an empirical study based on the factor analysis of correlations between the various subscales of widely used cognitive style instruments. They identified three factors, to which a variety of cognitive style dimensions could be reduced, which were dubbed as the “information-processing domain,” the “thinking-feeling dimension,” and the “attentional focus dimension.” It is interesting to note that the first and second factors identified empirically by Bokoros et al. closely resemble “conventional” and “applied” styles, respectively. While the first factor comprised cognitive style dimensions that operate at perceptual and low-order cognitive levels, the second factor comprised styles related to individual differences in more complex, higher order cognitive activities. As for the third factor, which is described by Bokoros et al. as “internal and external application of the executive cognitive function,” it closely resembles the mobility/fixity dimension, or metastyle, described in the mobility/fixity lines of research.

Finally, Nosal ( 1990 ) proposed a multidimensional hierarchical model of cognitive style that systematized cognitive style dimensions based on cognitive science theories. Specifically, the model proposes that the variety of cognitive styles can be arranged into a matrix (see Fig. 53.1 ). The horizontal axis of the matrix represents four hierarchical levels of information processing: perception (processing of primary/early perceptual information), concept formation (formation of conceptual representations in the form of symbolic, semantic, and abstract structures), modeling (organizing personal experiences into “schemas,” “models,” or “theories”), and program (goal-directed activity and metacognitive approaches used for complex decision-making tasks).

Cognitive styles in relation to levels of information processing and cross-dimensions, according to Nosal’s theory. 1 = field dependence-independence; 2 = field articulation; 3 = breadth of categorization; 4 = range of equivalence; 5 = articulation of conceptual structure; 6 = tolerance for unrealistic experience; 7 = leveling-sharpening; 8 = range of scanning; 9 = reflectivity-impulsivity; 10 = rigidity-flexibility; 11 = locus of control; 12 = time orientation.

After positioning different conventional styles into these four levels of information processing, Nosal identified a number of vertical cross-dimensions, which he described as “modules of information processing” that encompass all the variety of cognitive styles. These stylistic cross-dimensions, according to Nosal, reflect regulatory mechanisms responsible for generating four qualitatively different bipolar cognitive style dimensions: (1) field structuring ( context dependent vs. context independent ), which describes a tendency to shift attention to perceiving events as separate versus inseparable from their context; (2) field scanning ( rule-driven vs. intuitive ), which describes a tendency for directed, driven by rules, versus aleatoric, driven by salient stimuli, information scanning; (3) control allocation ( internal vs. external locus of processing ), which describes ways of locating criteria for processing at the internal versus external center; and (4) equivalence range ( compartmentalization vs. integration ), which represents a tendency to process and output information globally versus sequentially. The model allows detecting some gaps in the area of cognitive style and identifying yet unknown cognitive styles dimensions in the cells of the matrix. For instance, field-structuring cross-dimension has been studied so far on the basis of Witkin’s field dependence/independence cognitive style, which operates mostly on the perceptual level, so context-dependent/independent styles operating at higher levels of cognitive processing have yet to be identified.

It is interesting to note that the cross-dimensions that Nosal derived from his model resemble the metacomponents suggested by Sternberg’s componential theory of intelligence (selection of low-order components, selection of representation or organization of information, and selection of a strategy for combining lower order components), which are defined as the “specific realization of control processes…sometimes collectively (and loosely) referred to as the executive” (Sternberg, 1985 , p. 99). Thus, the four major cross-dimensions identified by Nosal seem to reflect four different types of executive functions or cognitive control processes that regulate an individual’s perception, thoughts, and actions, and generate four qualitatively different bipolar cognitive style dimensions (i.e., context dependent vs. context independent; rule-driven vs. stimulus-driven information scanning; internal vs. external locus of control; and holistic vs. sequential processing). In this view, any given cognitive style can be viewed as an expression of a particular executive function from these four cross-dimensions, operating at a particular level of information processing. Thus, according to Nosal’s categorization, the number of cognitive styles is finite and unknown styles could be predicted and placed into the cells of the matrix.

In summary, the recent studies on cognitive style endeavored to systematize the variety of cognitive styles and establish a possible structural relationship among them. These studies cast serious doubt on the unitary nature of cognitive style and provided evidence for the hierarchical organization of cognitive style dimensions operating at different levels of information processing (from perceptual to metacognitive). Furthermore, Nosal’s model allows for mapping existing models of cognitive style onto information processing theories, taking into account the complex structure and multidimensionality of the cognitive style construct. Furthermore, many of the aforementioned studies pointed out the regulatory function of cognitive styles. Nosal’s model, in particular, suggested that all the variety of cognitive style dimensions might be clustered around a limited number of stylistic cross-dimensions, related to specific executive functions.

Perspectives From Cognitive Sciences and Neuroscience

Recent cognitive science and neuroscience studies provided new evidence that shed light on the nature of cognitive style and its relation to other basic psychological constructs and processes. In this section, we will review two categories of recent cognitive science and neuroscience studies.

The first line of research contains a few recent cognitive neuroscience studies that attempted to demonstrate that cognitive style may be more accurately represented by specific patterns of neural activity, and not only by differences in performance on behavioral measures. Gevins and Smith ( 2000 ) examined differences between subjects with verbal versus nonverbal cognitive styles by recoding their electroencephalograms (EEGs) while the subjects performed a spatial working memory task. The results showed that although the subjects did not significantly differ in task performance, subjects with a verbal cognitive style tended to make greater use of the left parietal region, whereas subjects with a nonverbal style tended to make greater use of the right parietal region. Furthermore, functional magnetic resonance imaging (fMRI) experiments have revealed that, while performing the EFT (which can be solved with either visual-object or visual-spatial strategies), in the absence of significant behavioral differences in task performance, spatial visualizers (i.e., individuals who prefer to process information spatially in terms of spatial relations and locations) showed greater activation in left occipito-temporal areas, while the object visualizers (i.e., individuals who prefer to process information visually in terms of color, shape, and detail) showed greater activation in the bilateral occipito-parietal junction (Motes, Malach, & Kozhevnikov, 2008 ), supporting the relationship between individual differences in visual cognitive style and differential use of regions in the dorsal and ventral visual processing streams. The importance of these studies is that they indicate that individuals with different cognitive styles may exhibit different patterns of neural activity, even though their behavioral performance may not significantly differ. That is, the findings imply that the differences underlying individuals’ cognitive styles can be associated with different patterns of neural activity in the brain, in addition to the ability to perform a particular task.

The second line of studies is related to the most recent cognitive and cultural neuroscience studies that demonstrated that culture-specific experiences may afford distinct patterns of information processing. Surprisingly, these culture-sensitive patterns of information processing were indentified not only at cognitive levels but also at the neural and perceptual levels, suggesting that sociocultural experiences may affect neural pathways and also shape perception (e.g., Han & Northoff, 2008 ). For instance, several studies have shown that members of Eastern cultures exhibit more holistic and field-dependent rather than analytic and field-independent perceptual affordances (e.g., Miyamoto, Nisbett, & Masuda, 2006 ;Nisbett & Masuda, 2003 ). On a change blindness task, East Asians detected more changes in background context, whereas North Americans detected more changes in foreground objects (Nisbett & Masuda, 2003 ). Kitayama, Duffy, Kawamura, and Larsen ( 2003 ) also found that while North Americans were more accurate in an “absolute task” (drawing a line that was identical to the first line in absolute length), Japanese people were more accurate in a “relative task” (drawing a line that was identical to the first in relation to the surrounding frame), suggesting that the Japanese participants paid more attention to the frame (context) than did the North Americans and were more field dependent. Other studies reported that North Americans recognized previously seen objects in changed contexts better than Asians did, due to their increased focus on objects’ features independent of context (Chua, Boland, & Nisbett, 2005 ). Gutchess, Welsh, Boduroglu, and Park ( 2006 ) evaluated neural bases for these cultural differences in fMRI, concluding that cultural experiences subtly direct neural activity, particularly for focal objects in early visual processing.

Differences between members of different cultures were also reported on lower order and higher order cognitive tasks, as well as on tasks that require metacognitive processing. For instance, Chinese participants organized objects more relationally (e.g., grouping “monkey” and “banana” together because monkeys eat bananas) and less categorically (e.g., grouping “panda” and “monkey” together because both are animals) than Westerners (Nisbett, 2003 ), reflecting differences in field scanning. Significant differences between Easterners and Westerners have also been found in decision making. Kume ( 1985 ) discovered that, when making decisions, Easterners adopt an indirect, agreement-centered approach, based on intuition, while Westerners favor a direct, confrontational strategy using rational criteria. There are also cross-cultural differences at the neural level in language processing; English speakers reading English words activate superior temporal gyrus, but Chinese speakers reading Chinese characters activate inferior parietal lobe (Tan, Laird, Li, & Fox, 2005 ), which might indicate differences in global versus sequential processing.

Furthermore, research also identified self-construal differences: Westerners characterize the self as independent and have self-focused attention, while East Asians emphasize interdependence and social context (Nisbett, Choi, Peng, & Norenzayan, 2001 ). Also, Americans believe that they have control over events to the extent that they often fail to distinguish between objectively controllable events and uncontrolled ones. In contrast, East Asians are not susceptible to this illusion (Glass & Singer, 1973 ), reflecting differences in the locus of control dimension.

Overall, the reported cultural differences were identified at all levels of information processing (from perceptual to higher order cognitive reasoning) and can be generally described as tendencies of East Asian people (1) to engage in context-dependent cognitive processes, while Westerners, who tend to think about the environment analytically, engage in context-independent cognitive processes (Goh et al., 2007 ; Miyamoto et al., 2006 ); (2) to seek intuitive instantaneous understanding through direct perception, while Westerners favor more logic and abstract principles (Nakamura, 1985 ); (3) to exhibit more external locus of control in contrast to Westerners, who have stronger internal locus (Glass & Singer, 1973 ; Nisbett et al., 2001 ); and (4) to have tendencies to perceive and think about the environment more holistically and globally, in contrast to Westerners, who engage in more sequential processing (Goh et al., 2007 ).

Interestingly, all the reported culture-sensitive individual difference can be described by Nosal’s four style cross-dimensions (executive functions), field scanning, organization, locus of control, and equivalence range, and can therefore be positioned into the cells of Nosal’s matrix of cognitive style dimensions, according to the specific executive function they perform and the dominant level of information processing involved in a given task. Thus, culture-sensitive individual differences reported in transcultural psychology and neuroscience seem to represent different dimensions of cognitive style described in the cognitive style literature, and yet unidentified, culture-sensitive individual differences in cognition might be predicted from the Nosal’s matrix.

While cognitive psychology research provides evidence that some components of executive functions (e.g., updating working memory representations, shifting between task sets) are entirely genetic in origin (Friedman et al., 2008 ), social-constructivist research argues for the sociocultural origin of executive functioning, suggesting its flexible nature (e.g., Ardila, 2008 ; Vygotsky, 1984 ). In light of the proposed framework distinguishing between stationary and flexible individual differences, as well as on the basis of the review of culture-sensitive individual differences, we suggest that the four executive functions derived from Nosal’s theory represent “flexible” components of executive functioning, which are shaped and mediated by sociocultural environment. On the basis of this approach, cognitive style research can contribute to transcultural psychology and neuroscience research by helping to organize and predict different dimensions of culture-sensitive individual differences.

Conclusions and Future Directions

The current review attempts to bridge the gap between the large body of traditional cognitive style concepts, cognitive neuroscience, and transcultural psychology and neuroscience research, using an organizing framework that distinguishes between relatively stationary (i.e., abilities, personality traits) and ontogenetically flexible (cognitive styles) individual differences in cognition. As demonstrated throughout the review, the lack of discrimination between stationary and flexible individual differences, as well as the absence of a common theoretical framework for mapping the cognitive style concept onto existing cognitive science and neuroscience research, has led to misinterpretation and underestimation of the cognitive style concept.

The reviewed literature on the state of affairs in the aforementioned three research traditions suggests that the concept of cognitive style has a place in, and should be integrated into, mainstream current cognitive science and neuroscience theories. One of the possible approaches to integrate cognitive style into contemporary cognitive science theories can be based on Nosal’s ( 1990 ) model, which proposes that the variety of cognitive styles could be structuralized as the elements of a matrix, with the horizontal axis representing different levels of information processing (from perception to metacognition) and the vertical axis representing four major types of stylistic cross-dimensions that reflect specific executive functions responsible for generating four qualitatively different bipolar cognitive style dimensions (i.e., context dependent vs. context independent; rule-driven vs. stimulus-driven information scanning; internal vs. external locus of control; holistic vs. sequential processing). Nosal’s model takes into account the complex structure and multidimensionality of the cognitive style construct, and it allows for predicting the existence of other, yet unidentified, styles.

Based on our review, we suggest redefining cognitive style as ontogenetically flexible individual differences representing an individual’s adaptation of innate predisposition to external physical and sociocultural environments and expressing themselves as environmentally and culturally sensitive neural and/or cognitive patterns of information processing. To an extent far greater than that seen in other animals, who are born in a given environment and bound for generations to specific environmental conditions and thus might exhibit numerous fixed inborn patterns of behavior that result from long-term evolutionary processes, humans are much less restricted by fixed innate mechanisms suited for specific environmental conditions. This places more importance on the role of postnatal development, which is largely based on social interactions, concepts, and cultural means of learning, and takes place in ever-expanding and changing environments throughout the life span. Thus, the inborn capacities of humans allow for a wide range of possibilities for their future expression and development. Recent evidence from neuroscience indicates that neurogenesis and neural plasticity are affected by social environments (Lu et al., 2003 ). Research in evolutionary genetics consistently shows evidence of the neural plasticity of human behavior in relation to sociocultural environment, and the coevolution of genes, cognition, and culture (see Li, 2003 for review).

The proposed view on individual differences in cognition is reflected in the model presented in Figure 53.2 . The core is formed by the individual’s innate predispositions and personality traits, which reflect stationary individual differences. This core

Layers constituting individual differences in cognition. (See color insert.)

is surrounded by cognitive style, reflecting flexible individual differences. The development of cognitive style occurs on the basis of these innate core traits and is shaped through interaction with the surrounding environment. The first environmental layer represents the individual’s immediate familial and physical environment, which influences early cognitive development and reinforces certain innate characteristics, while suppressing others. At the next level lies the educational layer, in which the individual progresses through school systems and develops certain problem-solving strategies. The next layer is the professional layer, in which individuals’ ways of thinking are sharpened and become more distinct. In the professional layer, an individual’s cognitive style is affected by both mediated information contained in the professional media, as well as personal interactions with peers. Surrounding all of these is the final, cultural layer, reflecting mental, behavioral and cognitive processing patterns common to a specific cultural group. All these sociocultural layers affect each other and together shape the different layers of information processing and behavioral patterns of an individual. Metacognitive processes can possibly affect all the subordinate layers of information processing; a person with highly developed metacognitive processes would be aware of his or her preferred style, and, when presented with tasks or situations that require use of a different style, would flexibly adapt his or her strategies. The development of flexible metatstyles would allow an individual to switch between preferred styles. Possible reasons for the formation of flexible metastyles could be experiencing different situational contexts (such as different professional, educational, and cultural settings), changing professional field, or changing cultural context (native language, traditions). Indeed, Bagley and Mallick ( 1998 ) indicated malleability of cognitive styles in migrant children and suggested that concept of cognitive style can be deployed as an indicator of process and change in migration and multicultural education, rather than as a description of basic cognitive processes.

The current organizing framework that distinguishes between stationary and ontogenetically flexible individual differences in cognition helps to bridge the gap between the large body of traditional cognitive style concepts, cognitive neuroscience, and transcultural psychology and neuroscience research. We argue for the importance of such a framework for cognitive psychology and neuroscience, which still lacks a coherent framework of individual differences. Moreover, such an organizing framework will be crucial for helping transcultural psychology and neuroscience identify the causes of found cross-cultural individual differences (e.g., whether the identified differences are due to long-term evolutionary processes or ontogenetic development) and assign relative weight to such causes (e.g. whether within-culture differences, such as in educational and/or professional contexts, might overshadow the global cultural effect). Finally, such a framework will aid in understanding the relation between cognitive style and other cognitive science concepts, suggesting that cognitive style can be a valuable concept beyond the largely abandoned filed of cognitive style research, and can bring new insights in understanding the individual differences in humans’ cognitive functioning.

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Overview of the Problem-Solving Mental Process

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Rachel Goldman, PhD FTOS, is a licensed psychologist, clinical assistant professor, speaker, wellness expert specializing in eating behaviors, stress management, and health behavior change.

• Identify the Problem
• Define the Problem
• Form a Strategy
• Organize Information
• Allocate Resources
• Monitor Progress
• Evaluate the Results

Frequently Asked Questions

Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue.

The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything they can about the issue and then using factual knowledge to come up with a solution. In other instances, creativity and insight are the best options.

It is not necessary to follow problem-solving steps sequentially, It is common to skip steps or even go back through steps multiple times until the desired solution is reached.

In order to correctly solve a problem, it is often important to follow a series of steps. Researchers sometimes refer to this as the problem-solving cycle. While this cycle is portrayed sequentially, people rarely follow a rigid series of steps to find a solution.

The following steps include developing strategies and organizing knowledge.

1. Identifying the Problem

While it may seem like an obvious step, identifying the problem is not always as simple as it sounds. In some cases, people might mistakenly identify the wrong source of a problem, which will make attempts to solve it inefficient or even useless.

Some strategies that you might use to figure out the source of a problem include :

• Asking questions about the problem
• Breaking the problem down into smaller pieces
• Looking at the problem from different perspectives
• Conducting research to figure out what relationships exist between different variables

2. Defining the Problem

After the problem has been identified, it is important to fully define the problem so that it can be solved. You can define a problem by operationally defining each aspect of the problem and setting goals for what aspects of the problem you will address

At this point, you should focus on figuring out which aspects of the problems are facts and which are opinions. State the problem clearly and identify the scope of the solution.

3. Forming a Strategy

After the problem has been identified, it is time to start brainstorming potential solutions. This step usually involves generating as many ideas as possible without judging their quality. Once several possibilities have been generated, they can be evaluated and narrowed down.

The next step is to develop a strategy to solve the problem. The approach used will vary depending upon the situation and the individual's unique preferences. Common problem-solving strategies include heuristics and algorithms.

• Heuristics are mental shortcuts that are often based on solutions that have worked in the past. They can work well if the problem is similar to something you have encountered before and are often the best choice if you need a fast solution.
• Algorithms are step-by-step strategies that are guaranteed to produce a correct result. While this approach is great for accuracy, it can also consume time and resources.

Heuristics are often best used when time is of the essence, while algorithms are a better choice when a decision needs to be as accurate as possible.

4. Organizing Information

Before coming up with a solution, you need to first organize the available information. What do you know about the problem? What do you not know? The more information that is available the better prepared you will be to come up with an accurate solution.

When approaching a problem, it is important to make sure that you have all the data you need. Making a decision without adequate information can lead to biased or inaccurate results.

5. Allocating Resources

Of course, we don't always have unlimited money, time, and other resources to solve a problem. Before you begin to solve a problem, you need to determine how high priority it is.

If it is an important problem, it is probably worth allocating more resources to solving it. If, however, it is a fairly unimportant problem, then you do not want to spend too much of your available resources on coming up with a solution.

At this stage, it is important to consider all of the factors that might affect the problem at hand. This includes looking at the available resources, deadlines that need to be met, and any possible risks involved in each solution. After careful evaluation, a decision can be made about which solution to pursue.

6. Monitoring Progress

After selecting a problem-solving strategy, it is time to put the plan into action and see if it works. This step might involve trying out different solutions to see which one is the most effective.

It is also important to monitor the situation after implementing a solution to ensure that the problem has been solved and that no new problems have arisen as a result of the proposed solution.

Effective problem-solvers tend to monitor their progress as they work towards a solution. If they are not making good progress toward reaching their goal, they will reevaluate their approach or look for new strategies .

7. Evaluating the Results

After a solution has been reached, it is important to evaluate the results to determine if it is the best possible solution to the problem. This evaluation might be immediate, such as checking the results of a math problem to ensure the answer is correct, or it can be delayed, such as evaluating the success of a therapy program after several months of treatment.

Once a problem has been solved, it is important to take some time to reflect on the process that was used and evaluate the results. This will help you to improve your problem-solving skills and become more efficient at solving future problems.

A Word From Verywell​

It is important to remember that there are many different problem-solving processes with different steps, and this is just one example. Problem-solving in real-world situations requires a great deal of resourcefulness, flexibility, resilience, and continuous interaction with the environment.

Get Advice From The Verywell Mind Podcast

Hosted by therapist Amy Morin, LCSW, this episode of The Verywell Mind Podcast shares how you can stop dwelling in a negative mindset.

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You can become a better problem solving by:

• Practicing brainstorming and coming up with multiple potential solutions to problems
• Being open-minded and considering all possible options before making a decision
• Breaking down problems into smaller, more manageable pieces
• Asking for help when needed
• Researching different problem-solving techniques and trying out new ones
• Learning from mistakes and using them as opportunities to grow

It's important to communicate openly and honestly with your partner about what's going on. Try to see things from their perspective as well as your own. Work together to find a resolution that works for both of you. Be willing to compromise and accept that there may not be a perfect solution.

Take breaks if things are getting too heated, and come back to the problem when you feel calm and collected. Don't try to fix every problem on your own—consider asking a therapist or counselor for help and insight.

If you've tried everything and there doesn't seem to be a way to fix the problem, you may have to learn to accept it. This can be difficult, but try to focus on the positive aspects of your life and remember that every situation is temporary. Don't dwell on what's going wrong—instead, think about what's going right. Find support by talking to friends or family. Seek professional help if you're having trouble coping.

Davidson JE, Sternberg RJ, editors.  The Psychology of Problem Solving .  Cambridge University Press; 2003. doi:10.1017/CBO9780511615771

Sarathy V. Real world problem-solving .  Front Hum Neurosci . 2018;12:261. Published 2018 Jun 26. doi:10.3389/fnhum.2018.00261

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

ORIGINAL RESEARCH article

Understanding students’ problem-solving patterns: evidence from an allotted response time in a pisa 2012 item.

A correction has been applied to this article in:

Corrigendum: Understanding students' problem-solving patterns: evidence from an allotted response time in a PISA 2012 item

• Read correction

• Department of Education, Seoul National University, Seoul, Republic of Korea

Understanding students’ learning characteristics is central to successfully designing student-centered learning. Particularly in the problem-solving area, it is vital to know that students can possess their styles to solve problems, which should be considered central to addressing adaptive learning. To date, analyzing students’ learning characteristics has been mainly based on their final answers. However, there is a limit to understanding the thinking process of students with the correct answer, because their responses are, de facto , singular and identical. With this background, we propose an approach for investigating students’ cognitive behavior in problem-solving using response time in the process data. In this paper, we analyzed an item in Programme for International Student Assessment 2012 Creative Problem Solving (CP038q1). We analyzed log data from the PISA CPS item Ticket encompassing 30,453 students (7,196 students with the correct answer and 23,257 students with incorrect answers) from 42 countries. We found that students with the correct answer are categorized into four clusters, and the problem-solving patterns of each cluster are distinguishable. We also showed the internal validity of this approach by confirming that students with incorrect answers can also be similarly classified. Our results indicate that allotted response time in an item can shed light on several distinguished problem-solving patterns, which implies that adaptive learning and feedback are vital for them.

Introduction

One of the important purposes of educational evaluation is to establish effective teaching strategies and provide productive feedback to students based on reliable and valid estimates of students’ abilities, thereby improving the quality of subsequent education ( Baek, 2019 ). For this purpose, educational assessments are carried out in various ways as technology is increasingly highly developed. Particularly, in computer-based tests, every mouse click and keystroke during the problem-solving process is recorded in log files with timestamps, and collecting these data has facilitated novel forms of assessment ( Wang, 2021 ). In other words, by using computers in the educational field, it becomes possible to record the problem-solving process of taking tests and determine more about students’ particular problem-solving patterns and deployed strategies ( Shin, 2021 ). Process data have enriched educational assessment and evaluation beyond simply providing information on what are correct or incorrect answers. For example, He et al. (2019) analyzed students’ problem-solving proficiency using process data from the Programme for the International Assessment of Adult Competencies and found that problem-solving patterns and strategies are closely related to problem-solving proficiency. This study showcases the potential that log data analyses have for utilization in terms of learning analytics through the visualization of problem-solving patterns using log data. Therefore, using time-relative variables derived from log data, our study seeks to identify different problem-solving patterns.

Previous studies on problem-solving strategies using PISA 2012 process data

Following significant advances in educational assessment, PISA has implemented computer-based assessment (CBA) since 2006. In 2012, three areas of “digital reading,” “mathematics,” and “problem-solving” were designed as within the purview of CBA ( OECD, 2014a ). Problem-solving was one of the core components of PISA 2012, in that assessment using computers is suitable for interactive items, where some exploration is required to uncover undisclosed information ( Ramalingam et al., 2014 ; OECD, 2014a ). PISA 2012 defined complex problem-solving skill as “an individual’s capacity to engage in cognitive processing to understand and resolve problem situations where a method of solution is not immediately obvious. It includes the willingness to engage with such situations to achieve one’s potential as a constructive and reflective citizen” ( OECD, 2014a , p. 30). After PISA 2012 released the process data, it created an opportunity to expand the depth of assessment, and various studies on students’ problem-solving processes and strategies were conducted. For example, Greiff et al. (2015) defined vary-one-thing-at-a-time (VOTAT) as an important problem-solving strategy that can be applied to PISA 2012 Climate Control items, which require students to find the function of the three buttons on the climate control, humidity and temperature, and identified the relationship between VOTAT usage and item correctness. They then discussed the use of log data in educational assessment. Also, some studies proposed new methods of utilizing process data. For instance, Han et al. (2019) adapted n-grams to generate action sequence features based on VOTAT. Then they selected features using random forest and backward elimination, showing n-grams can be translated into mini-sequences along with their frequencies. Ren et al. (2019) showed the usability of students’ goal pursuit in an item context to analyze log data. They defined three possible problem-solving goals in a PISA 2012 Ticket item and clustered students based on their goal pursuit to identify the relationship with CPS proficiency. Meanwhile, other studies showed that process data can reveal the differences in problem-solving ability between boys and girls ( Wüstenberg et al., 2014 ), students with and without a migration background ( Sonnleitner et al., 2014 ), and interaction effects of gender and migration background ( Eichmann et al., 2020 ).

Learning analytics

The problem-solving pattern can be defined as behavioral characteristics captured in the process of problem-solving that reflects strategies, time, and orders of problem-solving. Each learner has a different problem-solving pattern, and it is necessary to deploy suitable teaching strategies to learners’ characteristics to facilitate effective learning ( Beck, 2001 ). Kolb and Kolb (2005) also suggested a few types of learning styles and emphasized the necessity of providing students with a learning experience that is appropriate to their characteristics. Indeed, learning analytics can be helpful for teachers and students in exploring unobserved patterns and underlying information in learning processes ( Agudo-Peregrina et al., 2014 ; Yin et al., 2019 ). Problem-solving pattern analysis is a significant issue in learning analysis. Moreover, in paper-based assessment, it is almost impossible to obtain detailed information about students’ problem-solving processes or patterns, so there is a de facto limit to understanding their problem-solving characteristics. To overcome this, many studies have approached the students’ problem-solving strategies through self-report questionnaires (e.g., Pereira-Laird and Deane, 1997 ; Danek et al., 2014 ). However, the results of self-reporting questionnaires can be biased because they can reflect “what students think of themselves” and not “what they really are.” Recently, with the introduction of CBA, it has become possible to access and analyze students’ problem-solving processes and strategies directly through the process data (e.g., Greiff et al., 2015 ; Han et al., 2019 ; Ren et al., 2019 ). Many studies that analyzed the students’ problem-solving using the process data identified students’ specific problem-solving strategies that were applied to specific items. However, studies on comprehensive patterns and strategies are somewhat scant.

Response time

The concept of response time is drawing the most attention within the wider process data paradigm. By analyzing response time, not only can we confirm whether the test was conducted properly (e.g., Yamamoto and Lennon, 2018 ) but also infer how much the students were engaged in the test (e.g., Goldhammer et al., 2016 ). Also, the response time provides useful information for an in-depth understanding of students’ various ways to approach and solve problems ( He et al., 2018 ; Shin, 2021 ).

In general, response time can be examined from various aspects. First, different explanations are possible from the perspective of speed. For example, a long response time by a student can be interpreted positively in that they addressed the item carefully, whereas it can be interpreted negatively in exams where speed is an important factor. Many studies have analyzed the relationships between response speed and students’ accuracy or ability ( Swanson et al., 2001 ; Lee, 2007 ; Goldhammer et al., 2015 ; Shin, 2021 ; Ulitzsch et al., 2021 ).

Second, response time can be analyzed at either the test-level or item-level. Particularly, in large-scale, low-stakes assessments like PISA, students can determine their problem-solving speed and spend a given amount of time on each item as they wish. In this case, the response time can be a real indicator of students’ problem-solving behavior. Studies that consider response time at the test-level, which analyzes how much time is spent on each item in an entire test, can show how well students behaved adaptively according to the cognitive loading each item requires. This is also called the “time-on-task effect” and many studies have been carried out to identify the relationship between both item difficulty and students’ learning characteristics (e.g., Goldhammer et al., 2015 ; Naumann and Goldhammer, 2017 ; Naumann, 2019 ).

Conversely, if the response time is analyzed at the item-level, by subdividing the total response time into specific steps, we can identify students’ cognitive processes and strategies to address the item (e.g., Hahnel et al., 2022 ). In particular, if an item consists of several decision-making steps, like those items in the PISA 2012 CPS task, the time-related variables for each step of addressing the item can be identified. In this way, it is possible to recognize which step the student spent a long time on, or focused on. Also, the student’s problem-solving behavior or pattern can be inferred from the configuration of time spent on the item ( Whimbey and Lochhead, 1991 ; Zoanetti, 2010 ; Eichmann et al., 2019 ). For example, even if the total amount of time that students spent on an item are the same, the student who spends a long time reading around questions of items and then shaping a problem-solving strategy, and the student who spends most time clicking and exploring the problem, has different problem-solving strategies ( Goldhammer et al., 2014 ). Thus, item-level analysis can reveal the students’ particular cognitive processes and patterns.

In short, studies related to response time have analyzed response speed or the time allotted to the either test-level (e.g., Engelhardt and Goldhammer, 2019 ) or item-level (e.g., Ren et al., 2019 ; Hahnel et al., 2022 ). Still, with test-level analysis, it is difficult to identify the precise step in which students spend a lot of time on an item. The whole problem-solving process can be broken down into detailed steps, and how much time each step takes can be treated as a piece of evidence that shows the student’s problem-solving pattern and strategy. Therefore, analyzing the time allotted to each step in an item can be a credible way to check how the students addressed the problem or what problem-solving pattern they generally possess.

The purpose of this study and research questions

The purpose of this paper is to identify and understand students’ problem-solving processes. To this end, we analyzed an item in PISA 2012 CPS (CP038q1) and determined the various problem-solving patterns among students who addressed this item correctly. We propose a method to identify problem-solving patterns using response time as the key variable. This study can evidence the necessity of devising effective teaching methods according to the cognitive styles of students and provide productive feedback not only for students with incorrect answers but also for students with the correct answer. Furthermore, based on the findings, we would like to suggest the importance of understanding students with various problem-solving patterns and implementing adaptive teaching methods adapted to the learning characteristics of students.

Based on the objectives, the research questions are as follows:

• RQ1. What time-related variables predict the problem-solving ability of students who provide the correct answer?

• RQ2. What are the differences in the problem-solving patterns of students clustered with selected time-relative variables?

• RQ3. How does the problem-solving pattern of students with incorrect answers differ from those of students with the correct answer?

Materials and methods

We analyzed log data from the PISA CPS item Ticket (CP038q1) encompassing 30,453 15-year-old students (7,196 students with the correct answer and 23,257 students with incorrect answers, 15,152 boys and 15,301 girls) from 42 countries. For the students, we analyzed how much time is spent on each problem-solving step, and how the students are then clustered based on the time spent. Also, we present the differences between the clusters using descriptive statistics and process maps. A process map is a tool that has been mostly used in the business field to date ( van der Aalst, 2016 ). We take advantage of it here to visualize how students in each cluster addressed the item and precisely how much time they spent on each action. Specifically, as in the PISA 2012 CPS, where the items consist of several stages of initial exploration and final decision-making, various paths can be explored in the process of solving a problem. In this item, the process map can be useful to visualize the problem-solving process and play a complementary role in descriptive statistics. For example, if a student took a long time over a particular sequence related to problem-solving, this could be interpreted in two ways. One is the case in which many types of sequences are explored, and another is the case in which only a few sequences are explored but very carefully for a long time. In these cases, it is impossible to identify the problem-solving patterns of the students only by using descriptive statistics. However, the process map can provide information about the time spent on each step and the percentage of students who clicked a specific button at each step which would not have been elicited from descriptive statistics alone. Therefore, we adopted the process map for a more accurate and richer interpretation of the students’ problem-solving behavior analysis. In addition, when we clustered the students, we tried not to be limited to the boundaries of countries or cultures, but focus instead on students’ characteristics. After clustering, we identified those demographic factors such as which countries’ students mostly belong to each cluster. By doing this, we tried to capture the characteristics of countries with a low percentage of students with the correct answer, which had not been covered much so far. We used R software version 4.0.3 ( R Core Team, 2020 ) for variable selection [glmnet package ( Hastie and Qian, 2016 )] and the visualizing process map [processmapR package ( Janssenswillen et al., 2022 )] and used Python software (version 3.10.2) for clustering.

Item description and data processing

The item used in this study was CP038q01 in PISA 2012 CPS. The PISA 2012 CPS included 16 units with a total of 42 items. The OECD has provided sample items, such as a Vending Machine, a Vacuum Cleaner, and Climate Control. We decided to use one item from the CPS unit Vending Machine for our analysis. This is because it fits well with the theoretical concept of CPS and constitutes an appropriate selection, representing an important part of the CPS framework at PISA 2012. As shown in Figure 1 , students are required to click the button on a ticket vending machine and buy the cheapest one offered among the tickets that satisfy the given conditions. The available buttons on the vending machine are, sequentially, “CITY SUBWAY” (hereinafter “CITY”) or “COUNTRY TRAINS” (hereinafter “COUNTRY”), then “FULL FARE” or “CONCESSION,” then “DAILY” or “INDIVIDUAL.” If the student clicks “INDIVIDUAL” in the third step, as illustrated in Figure 2 , a screen shows the selected conditions of the ticket and asks for the number of tickets from 1 to 5. The final price depends on the number of tickets purchased. After determining the number of tickets, the student has the choice of clicking “BUY” to complete the item by buying the ticket(s) or clicking “CANCEL” to explore other tickets further. Meanwhile, if you click “DAILY” at the third step, the next screen shows the price of the ticket that satisfies the conditions selected so far, and the student is asked to choose whether to complete the item by clicking “BUY” or instead, to explore other tickets with different conditions by clicking “CANCEL.” Except for the button that determines the number of tickets, clicking any button in a specific step automatically advances one to the next step, and there is no way to go back to the previous step. The only way to go back to the previous step is to click “CANCEL,” which is in each step, and if “CANCEL” is clicked, the ticket conditions set so far will be reset and the student will be returned to the first step at the same time. Eight combinations of ticket conditions can be set in this item (excluding the ticket number condition), and the total combination of tickets including the number of tickets is 24. Two tickets satisfy the conditions given in the item: “CITY”–“CONCESSION”–“INDIVIDUAL”–“4 tickets” and “CITY”–“CONCESSION”–“DAILY.” To address this item, the student needs to compare the prices of the tickets with two conditions, the former ticket price of 8 Zed, and the latter is 9 Zed. Since the former ticket price is cheaper, the correct answer is to select “CITY”–“CONCESSION”–“INDIVIDUAL”–“4 tickets” and then “BUY”.

Figure 1 . A snapshot of the first page of the problem- solving item (CP038q1) in Programme for International Student Assessment, 2012. Reproduced with permission from OECD, Tickets, PISA Test 2012 © OECD, 2012, https://www.oecd.org/pisa/test-2012/testquestions/question5/ , Accessed on (05.04.22).

Figure 2 . A snapshot of the last page of the problem- solving item (CP038q1) in Programme for International Student Assessment, 2012. Reproduced with permission from OECD, Tickets, PISA Test 2012 © OECD, 2012, https://www.oecd.org/pisa/test-2012/testquestions/question5/ , Accessed on (05.04.22).

In PISA 2012, a partial scoring system—0 for incorrect, 1 for partially correct, and 2 for correct—is applied for some items, and the Ticket item is one of them. With this item, students who purchase the cheapest ticket after comparing the prices of two tickets that otherwise satisfy all the given conditions would then receive 2 points (full credit). Other students who only check one of the two tickets and purchase the ticket without comparison would receive 1 point (partial credit). All other cases are scored as 0 points (no credit). Of a total of 30,452 students who addressed this item, 7,195 got full credit. The subjects of this study are those students with the correct answer (i.e., the students who received full credit). 1

In addition, in the case of PISA 2012 problem-solving, there was no limit on the time spent addressing each item; however, a 20-min limit was set for the whole test. Either one or two clusters were randomly assigned to students depending on different assessment designs ( OECD, 2014a ). For the Ticket item (CP038q1), the students’ average time spent was 54.8 s, and the median value was 50.2 s, while the average time spent by students with the correct answer was 67.3 s, and the median value was 62.2 s.

Variable generation

In order to analyze the problem-solving patterns of the students with the correct answer, variables needed to be generated in light of the number of possible cases in the problem-solving process. Before generating variables, of the 7,195 students who received full credit in this item, 7,191 students remained, excluding four students who did not have a start (“START_ITEM”) or end (“END_ITEM”) of problem-solving in the raw data. After this, all actions of each student are sorted in chronological order. Regarding the number of tickets, 4 tickets are coded as “trip_4,” and all other buttons (1, 2, 3, 5) are coded as “trip_n.” This is because the number of tickets other than “trip_4” has the same importance in terms of correct-answer-related actions and strategies. By combining them into “trip_n,” we reduced the logical number of possible action sequences. Also, when “trip_n” is repeated consecutively or the same action is recorded several times in a row, only the first action is left (e.g., trip_n, trip_n, trip_n, trip_n → trip_n). Using clean data, two sequences that satisfy the condition given in the item are defined and divided into “a” and “b” (b1, b2, b3, b4), respectively, as shown in Table 1 . Specifically, the “b” (b1 ~ b4) sequence, which is directly related to the correct answer, is divided according to whether the intended purpose is “decision-making” or “exploring various tickets,” and whether it includes only “trip_4” or both “trip_4” and “trip_n.” The number of possible cases is defined as “b1,” “b2,” “b3,” and “b4,” respectively. Among them, “b1” and “b2” are the sequences of “decision-making,” ending with “BUY.” They mean that the item is addressed by purchasing the ticket with the answer-related conditions. To be specific, “b1” is the most efficient sequence that selects “trip_4” without exploring any other combination of tickets, while “b2” includes both “trip_n” and “trip_4” but ends with “trip_4” and “BUY.”

Table 1 . Lists of answer-related sequences.

Meanwhile, “a,” “b3,” and “b4” are sequences that explore answer-related tickets but end with “CANCEL.” Among them, “b3” is the most efficient sequence where “CANCEL” is clicked to thereby explore other tickets after checking only the answer-related 4 tickets(“trip_4”). “b4” is the sequence that includes both “trip_4” and “trip_n” before clicking “CANCEL.” In the case of “b2” and “b4,” it is confirmed that the ticket number selection (“trip_4,” “trip_n”) is repeated a maximum of four times, i.e., only the sequences where “trip_n” and “trip_4” are repeated up to four times and are included in the clean data. After defining the sequences as shown in Table 1 , various problem-solving steps that can be identified in the item are defined. Based on the steps, variables are generated as shown in Table 2 . Figure 3 illustrates the relationships between the variables.

Table 2 . List of variables and their explanations.

Figure 3 . Structure of variables.

Variable selection method

Among the generated variables, the variable selection method was used to select variables that are important to explain the problem-solving ability of students with the correct answer. Then, selected variables were used for clustering. The method used in this study was the penalized regression method, which is one of the widely used methods in variable selection ( Yoo, 2018 ). Penalized regression methods, also called shrinkage methods, are methods of selecting variables by reducing regression coefficients using a penalty function ( Tibshirani, 1996 ). By continuously penalizing coefficients with a regularization parameter, penalized regression methods are known to produce more stable models than discrete methods such as forward selection or backward elimination ( Yoo, 2018 ). The most widely used penalized regression methods are Ridge ( Hoerl and Kennard, 1970a ), LASSO (Least Absolute Shrinkage and Selection Operator, Tibshirani, 1996 ), and Elastic Net ( Zou and Hastie, 2005 ). In this study, Elastic Net is applied, taking into account the characteristics of the generated time-related variables.

Elastic Net is a regularization and variable selection technique developed by Zou and Hastie (2005) . Elastic Net uses both L 1 and L 2 penalties by combining Ridge ( Hoerl and Kennard, 1970b ) with LASSO, and has the advantage of being able to address both variable selection, which is the strength of LASSO, and multicollinearity, which is the strength of Ridge. Specifically, it is characterized by better performance than variable selection and LASSO on collinear data ( Zou and Hastie, 2005 ). Time-related variables are generated by dividing the total time according to the detailed steps of problem-solving or by combining steps with similar characteristics. Due to the nature of time-related variables, the multicollinearity problem inevitably arises. Since all generated variables have their meaning in terms of addressing this item, we try to work out this problem not by dropping certain problematic variables arbitrarily but by using a statistical method. Considering all, Elastic Net is the most suitable statistical method for selecting the variables. The equation for estimating the regression coefficients of Elastic Net is as follows:

In the above equation, β ̂ indicates the vector of shrunk coefficients of j predictors. The second term on the right side is a penalty function, which is a combination of the L 1 norm and the L 2 norm. Elastic Net has two tuning parameters, α and λ. First, λ is a regularization (or penalty) parameter, which controls the extent of regularization as in LASSO. The larger λ means that the coefficient shrinks closer to zero and the smaller λ means the coefficient is closer to the least square estimation ( Yoo, 2018 ). Next, α is a tuning parameter that connects Ridge and LASSO. If the value of α approaches 1, it approximates to LASSO, and if it approaches 0, it approximates to the Ridge. In general, it is considered relatively more important to determine the degree of regulation between the two tuning parameters, so it is not necessary to justify both tuning parameters ( Yoo, 2021 ). Therefore, it is common to select the value of α at the researcher’s discretion (T. Hastie, personal communication, February 9, 2017, as cited in Yoo, 2021 ).

RQ1: Variable selection results

We used Elastic Net to select the most important variables to explain the overall PISA 2012 problem-solving ability of those students with the correct answer. For the analysis, we divided all the data randomly into a test set and a training set, by dividing the data by 7 to 3. Then, after fitting the model with 10-fold cross-validation on the training set, we obtained a prediction error with the test set. This selection process was repeated 100 times, and we used the variables which are selected 100 times out of a total of 100 times as criteria for clustering.

We set the dependent variable as the overall problem-solving ability in PISA 2012, which is calculated as the ratio of the student’s total score (the sum of credits they received) and the possible maximum score that the student could receive in the corresponding booklet. This is because, since PISA 2012 depends on matrix sampling, the booklet presented to each country or student is different, and the number of items, their difficulty, and the maximum score of each booklet are also different. Thus, it is inappropriate to use the ratio of the simply added-up score for the dependent variable. As an alternative, we used the PISA scale score as reported in the PISA 2012 results ( OECD, 2014b ), which was scaled with a mean of 500 and a standard deviation of 100, considering the characteristics of each item. For all CPS items in the booklet, the threshold of the PISA scale score was assigned according to the actual credits (0, 1, and 2) that each student received from each item. By using the ratio of the PISA scale score as the dependent variable, the possible differences in scores between students who received different booklets were compensated for and students’ overall problem-solving ability in PISA 2012 could be accurately reflected simultaneously.

In Elastic Net, as suggested by Hastie and Qian (2016) , we set α to 0.5 to take advantage of both Ridge and LASSO equally. Another tuning parameter λ was taken through the 10-fold cross-validation and Figure 4 illustrates the result of the 10-fold cross-validation with the Mean-Squared Error (MSE). The vertical dotted lines in Figure 4 are the upper and lower bounds of the one-standard-error rule. In the plot, the number of non-zero coefficients with the upper bound corresponds to 5. With the results of variable selection counts ( Table 3 ), we selected a total of five variables for the most parsimonious model.

Figure 4 . 10-fold cross-validation result with mean-squared error for students with the correct answer.

Table 3 . Variables selection counts out of 100-times repeats for students with the correct answer.

As shown in Table 3 , “time_b1,” “time_b3,” “time_start,” “time_irrelevant,” and “time_avg_btw_events” were selected through Elastic Net. The test set has a good RMSE of 0.18.

RQ2: Problem-solving patterns by clusters

Students were clustered using the k -medoids method using the five previously selected variables. The k -medoids method uses minimal dissimilarity to all objects in a cluster as the determinant that is opposite to the distance in the k -means method. Before clustering, we determined the optimal number of clusters using the elbow method. The result is shown in Figure 5 . In the graph, the elbow point is k  = 4, and we clustered the students into four groups.

Figure 5 . Result of elbow method for determining optimal k for students with the correct answer.

Table 4 ; Figure 6 show the results of the descriptive statistics and process map of each cluster, respectively. Specifically, the process map is filtered by 0.8 based on the students’ trace frequency. In the process map, the number written on the edge shows the average time that students took to explore the path. The thickness of the edge indicates the percentage of the frequency of passing the path. In addition, each step to buying a ticket in the item is expressed as a node, and the number in the node is the percentage of students who clicked the node.

Table 4 . Descriptive statistics (mean (standard deviation)) of variables by cluster.

Figure 6 . Process maps for four clusters for students with the correct answer.

Cluster 1 (curious) had the smallest number of students (823) among the four clusters. This cluster characterized the long “time_irrelevant,” which means that sequences of the actions other than the correct answer-related ones were explored in various ways. It can also be seen from the process map that Cluster 1 is close to a spaghetti shape, that is, it is an entangled and complex problem-solving process. In the process map, the percentage of clicking buttons that do not correspond to the correct answer conditions (e.g., “COUNTRY,” “FULL FARE,” “trip_n”) was higher than in other clusters, which is related to the longest value of “time_irrelevant.” In addition, “length,” “time_explore_total,” and “event_num” were also long. Based on all information, Cluster 1 explored many other sequences as well as those sequences related to the correct answer. Since students in this cluster are learning through trial and error, it is necessary to know that exploring only the correct-answer-related things is not always the optimal way. Also, a teacher needs to respect their various interests and present abundant learning materials which may help draw their attention.

Cluster 2 (speedy) had the largest number of students (2,412) among the four clusters. In this cluster, the values of all variables used for clustering were relatively small compared to other clusters. “time_start,” especially, had the smallest value across the clusters, and “time_b3” and “time_irrelevant” also had small values. With a process map of Cluster 2, the map shows that students mainly explored those sequences related to the correct answer. Thus, the students did everything quickly, including understanding the given conditions, setting up a problem-solving strategy, and exploring the strategy. Also, the short “time_avg_btw_events” implies that the decisions were made over a short time when deciding which button to click in each step. When exploring the tickets, even though some students made some mistakes, such as clicking “COUNTRY,” which is not related to the correct answer, they quickly realized them and tried to make things right by clicking “CANCEL” without going any further. For the students in this cluster, it could be helpful to let them know that they made mistakes quite frequently during the problem-solving process and they did not need to hurry.

Cluster 3 (repetitive) is characterized by exploring the correct answer repetitively compared to other clusters. This is because “time_a,” “time_b2,” and “time_b4” are short while “length” is long. Based on this, it can be inferred that “time_b3” has a large value not because the sequence of “CITY-CONCESSION-INDIVIDUAL-trip_4-CANCEL” was explored for a long time, but because the sequence had been explored repeatedly several times. Furthermore, in the process map, a relatively large percentage of students clicked buttons that did not meet the conditions in the item such as “FULL FARE” or “COUNTRY.” Also, they did not click “CANCEL” immediately when they clicked the wrong button. Instead, they tended to keep exploring the sequence such as “CONCESSION” after “COUNTRY.” In summary, Cluster 3 can be interpreted as that after exploring various sequences, the sequences corresponding to the correct answer were checked repeatedly to ensure correct decision-making. Given that the students did not correct the answer immediately, they need to practice thinking reflectively to make fewer mistakes, particularly on the test with time limits.

Cluster 4 (strategic) is characterized by fewer clicks and mistakes, but longer resolution times. As the process map shows, Cluster 4 spent a great deal of time addressing the problem and devising a strategy to solve it and spent a relatively long time clicking the button (“time_avg_btw_events”), carefully addressing the problem. In addition, this cluster did not explore some actions unnecessarily, such as “COUNTRY” and “trip_n.” Also, “time_b3” and “time_irrelevant” are short, so we can infer that most of the students solved this problem with the shortest correct-answer-related sequences, which are “a-b1” and “b3-a-b1.” The evidence can also be found in the process map that the edges from node “CONCESSION” are divided into two that have a similar thickness. In brief, they are students who solve the problem with one careful search based on the optimal strategy, rather than repeatedly searching the sequences to check whether their answer is correct. Considering that they are the students who think more and act less, so even though they do not react immediately, we need to acknowledge their style and wait for them.

RQ3: Differences between problem-solving patterns of students with the correct answer and students with incorrect answers

In order to verify the internal validity of this study, we also analyzed students with incorrect answers through the process given above. Through the Elastic Net, seven variables were selected (see Figure 7 ; Table 5 ). As shown in Table 5 , “time_b1,” “time_b4,” “time_start,” “time_irrelevant,” “time_avg_btw_events,” “length,” and “events_num” were selected and the test set has a good RMSE of 0.21.

Figure 7 . 10-fold cross-validation result with mean-squared error for students with incorrect answers.

Table 5 . Variables selection counts out of 100-times repeats for students with incorrect answers.

Using these variables, the students were clustered into eight categories (see Figure 8 ), and the process map of each cluster is illustrated in Figure 9 . Based on the four problem-solving patterns of students with the correct answer in RQ2, we were able to identify that these eight clusters can be classified similarly. In particular, Clusters A and B were similar to Custer 1 (curious); Cluster C was similar to Cluster 2 (speedy); and Clusters D–H were similar to Cluster 4 (strategic).

Figure 8 . Result of elbow method for determining optimal k for students with incorrect answers.

Figure 9 . Process maps for eight clusters for students with incorrect answers.

Unlike other clusters, Cluster A ( n  = 2,194) and Cluster B ( n  = 2,616) were the only clusters that clicked “CANCEL,” which means the students explored more than one path. That is the reason why these clusters showed more complex problem-solving processes than other clusters. Cluster A is more similar to Cluster 1 than Cluster B, in that students in this cluster explored irrelevant sequences, such as “COUNTRY” and “FULL FARE.”

Cluster C ( n  = 4,661) was similar to Cluster 2 in that all decisions were made over a relatively short period concerning other clusters. One of the idiosyncrasies is that the cluster includes students with the sequence of “START_ITEM →END_ITEM.” According to Zoanetti (2010) , these students can be considered as they were not able to understand what to do.

Cluster D ( n  = 3,779), E ( n  = 3,345), F ( n  = 3,392), G ( n  = 1,918), and H ( n  = 1,344) can be interpreted as we interpreted Cluster 4 in that they had their problem-solving strategy, even though they partially understood the question of the item. They did not recognize that comparing two possible types of tickets was the intention of the item. Most of the students in Clusters E–H only explored sequence a*(city→concession→daily→buy), whereas Cluster D only explored b*(city→concession→individual→trip_n/trip_4 → buy). Nevertheless, in terms of the allocated time between the clusters, there were some differences. For example, Cluster E had relatively shorter “time_start” and longer “time_avg_btw_events,” whereas Cluster F showed the opposite patterns. In addition, Cluster G showed the longest “time_avg_btw_events,” and Cluster H showed the longest “time_start.”

Summary and discussion

This study identified various problem-solving patterns of students found with the correct answer and compared them with the problem-solving patterns of students with incorrect answers. We captured students’ problem-solving characteristics based on their response time by directly accessing their process data of the PISA 2012 Ticket item (CP038q1) instead of using traditional methods such as self-reporting questionnaires. Specifically, we defined the crucial and necessary steps to address this item and generated variables with the time spent on each step and the number of actions in the problem-solving process. Then, we selected important variables for addressing the students’ overall problem-solving ability. Since generated time-related variables are derived from the total time and consist of the time spent on each step, they inevitably introduced multicollinearity problems. Elastic Net is a combination of LASSO (which can select variables) and Ridge (which can mitigate collinearity problems), so we adopted Elastic Net as the variable selection method.

Next, we categorized the students into four clusters using the k -medoids method with the selected variables. The results of analyzing and comparing characteristics between clusters based on descriptive statistics and process maps are as follows. First, Cluster 1 (curious) explored sequences unrelated to the correct answer in the most diverse way compared to other clusters. Second, Cluster 2 (speedy) spent the shortest time exploring the sequences and choosing the final answer. They did everything very quickly. Third, Cluster 3 (repetitive) chose the final answer after checking their answer repeatedly, though they made some mistakes exploring the correct-answer-related sequences. Lastly, Cluster 4 (strategic) spent a relatively long time reading the question phrasing of the item and devising an optimal solution strategy. Rather than checking the answer repeatedly, students in this cluster chose the answer after one careful search. As demonstrated, the four clusters were distinct. Given each cluster’s characteristics, feedback based on their problem-solving style is needed to improve students’ performance.

Finally, this study confirmed the internal validity of this approach by identifying the problem-solving patterns of students with incorrect answers. They were grouped into eight clusters, but considering their essential differences, they can be roughly classified into four clusters, making a pattern with a closer resemblance to the problem-solving patterns of students with the correct answer. Admittedly, given that they were students with incorrect answers, their problem-solving processes were more divergent.

Indeed, it cannot be said that one pattern is better than others. Each pattern has its way of approaching the item, and students can have the best strategy for their style as well as the characteristics of given items ( Naumann, 2019 ). The fact that the problem-solving patterns were related to the item characteristics is also supported by the results of this study that the problem-solving patterns of students with incorrect answers can be also roughly classified similarly to the patterns of the students with the correct answer. In the same context, the values for each time-related variable should also be interpreted concerning individual abilities and item characteristics.

The study’s findings have important implications for researchers and educators. First, we accessed students’ actual problem-solving behavior directly through the process data, which were generated from the interactions between computers and humans. Unlike the self-reporting questionnaire, which could reflect examinees’ subjectivity, process data only include honest information about their problem-solving behaviors. Therefore, our results using process data revealed students’ problem-solving patterns objectively. Furthermore, we adopted the process map technique and visualized students’ cognitive processes that occurred during problem-solving. The process map is useful because it can not only show students’ problem-solving patterns ( Figure 6 ) but also diagnose a student’s problem-solving behavior once their data are mapped. While previous studies were focused on short sequence units (e.g., Han et al., 2019 ; Ren et al., 2019 ), this study addressed problem-solving patterns using allotted time at the sequence-level while also aggregating them into process-level using a process map.

Second, we suggest that it is necessary to study the perspectives and positions of students who answered correctly, which have been difficult to investigate so far because they provide a single, identical correct answer. Indeed, this study reveals that the students show distinctly different characteristics when addressing a given problem. It is important to comprehend their diverse problem-solving patterns to devise learner-centered instructional designs and fulfill adaptive learning—such as providing appropriate feedback—for their further performance.

However, in terms of generalizability, this study has some limitations. Since this study is to showcase the potential of analyzing log data to identify problem-solving patterns of students, we analyzed one item as an example. Thus, to have external validity, the method that we proposed needs to be scrutinized using other items.

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found at: https://www.oecd.org/pisa/pisaproducts/database-cbapisa2012.htm .

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author contributions

H-JP, DL, and HP contributed to the study’s conception and design. H-JP was in charge of the paper administration, supervision, and validation of the paper. DL and HP wrote the first draft, data curation, and formal analysis. All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. ^ According to the PISA 2012 result ( OECD, 2014a ), students with partial credit correspond to Level 2, and they can test a simple hypothesis that is given to them and can solve a problem that has a single, specific constraint. While students who received full credit correspond to Level 5 on the problem-solving scale, they can systematically explore a complex problem scenario to gain an understanding of how relevant information is structured.

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Keywords: process data, response time analysis, process map, learning process, problem-solving patterns, PISA 2012

Citation: Park H-J, Lee D and Park H (2023) Understanding students’ problem-solving patterns: Evidence from an allotted response time in a PISA 2012 item. Front. Psychol . 13:1050435. doi: 10.3389/fpsyg.2022.1050435

Received: 21 September 2022; Accepted: 25 November 2022; Published: 04 January 2023.

Reviewed by:

Copyright © 2023 Park, Lee and Park. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Hyemin Park, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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18.12: Chapter 14- Problem Solving, Categories and Concepts

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This page is a draft and under active development. Please forward any questions, comments, and/or feedback to the ASCCC OERI ( [email protected] ).

• Kenneth A. Koenigshofer
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Learning Objectives

• Define problem types
• Describe problem solving strategies
• Define algorithm and heuristic
• Describe the role of insight in problem solving
• Explain some common roadblocks to effective problem solving
• What is meant by a search problem
• Describe means-ends analysis
• How do analogies and restructuring contribute to problem solving
• Explain how experts solve problems and what gives them an advantage over non-experts
• Describe the brain mechanisms in problem solving

In this section we examine problem-solving strategies. People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy, usually a set of steps, for solving the problem.

Defining Problems

We begin this module on Problem Solving by giving a short description of what psychologists regard as a problem. Afterwards we are going to present different approaches towards problem solving, starting with gestalt psychologists and ending with modern search strategies connected to artificial intelligence. In addition we will also consider how experts do solve problems and finally we will have a closer look at two topics: The neurophysiological background on the one hand and the question what kind of role can be assigned to evolution regarding problem solving on the other.

The most basic definition is “A problem is any given situation that differs from a desired goal”. This definition is very useful for discussing problem solving in terms of evolutionary adaptation, as it allows to understand every aspect of (human or animal) life as a problem. This includes issues like finding food in harsh winters, remembering where you left your provisions, making decisions about which way to go, repeating and varying all kinds of complex movements by learning, and so on. Though all these problems were of crucial importance during the evolutionary process that created us the way we are, they are by no means solved exclusively by humans. We find a most amazing variety of different solutions for these problems of adaptation in animals as well (just consider, e.g., by which means a bat hunts its prey, compared to a spider ).

However, for this module, we will mainly focus on abstract problems that humans may encounter (e.g. playing chess or doing an assignment in college). Furthermore, we will not consider those situations as abstract problems that have an obvious solution: Imagine a college student, let's call him Knut. Knut decides to take a sip of coffee from the mug next to his right hand. He does not even have to think about how to do this. This is not because the situation itself is trivial (a robot capable of recognizing the mug, deciding whether it is full, then grabbing it and moving it to Knut’s mouth would be a highly complex machine) but because in the context of all possible situations it is so trivial that it no longer is a problem our consciousness needs to be bothered with. The problems we will discuss in the following all need some conscious effort, though some seem to be solved without us being able to say how exactly we got to the solution. Still we will find that often the strategies we use to solve these problems are applicable to more basic problems, as well as the more abstract ones such as completing a reading or writing assignment for a college class.

Non-trivial, abstract problems can be divided into two groups:

Well-defined Problems

For many abstract problems it is possible to find an algorithmic solution. We call all those problems well-defined that can be properly formalized, which comes along with the following properties:

• The problem has a clearly defined given state. This might be the line-up of a chess game, a given formula you have to solve, or the set-up of the towers of Hanoi game (which we will discuss later ).
• There is a finite set of operators, that is, of rules you may apply to the given state. For the chess game, e.g., these would be the rules that tell you which piece you may move to which position.
• Finally, the problem has a clear goal state: The equations is resolved to x, all discs are moved to the right stack, or the other player is in checkmate.

Not surprisingly, a problem that fulfills these requirements can be implemented algorithmically (also see convergent thinking ). Therefore many well-defined problems can be very effectively solved by computers, like playing chess.

Ill-defined Problems

Though many problems can be properly formalized (sometimes only if we accept an enormous complexity) there are still others where this is not the case. Good examples for this are all kinds of tasks that involve creativity , and, generally speaking, all problems for which it is not possible to clearly define a given state and a goal state: Formalizing a problem of the kind “Please paint a beautiful picture” may be impossible. Still this is a problem most people would be able to access in one way or the other, even if the result may be totally different from person to person. And while Knut might judge that picture X is gorgeous, you might completely disagree.

Nevertheless ill-defined problems often involve sub-problems that can be totally well-defined. On the other hand, many every-day problems that seem to be completely well-defined involve a great deal of creativity and many ambiguities. For example, suppose Knut has to read some technical material and then write an essay about it.

If we think of Knut's fairly ill-defined task of writing an essay, he will not be able to complete this task without first understanding the text he has to write about. This step is the first sub-goal Knut has to solve. Interestingly, ill-defined problems often involve subproblems that are well-defined.

Knut’s situation could be explained as a classical example of problem solving: He needs to get from his present state – an unfinished assignment – to a goal state - a completed assignment - and has certain operators to achieve that goal. Both Knut’s short and long term memory are active. He needs his short term memory to integrate what he is reading with the information from earlier passages of the paper. His long term memory helps him remember what he learned in the lectures he took and what he read in other books. And of course Knut’s ability to comprehend language enables him to make sense of the letters printed on the paper and to relate the sentences in a proper way.

Same place, different day. Knut is sitting at his desk again, staring at a blank paper in front of him, while nervously playing with a pen in his right hand. Just a few hours left to hand in his essay and he has not written a word. All of a sudden he smashes his fist on the table and cries out: "I need a plan!

How is a problem represented in the mind?

Generally speaking, problem representations are models of the situation as experienced by the agent. Representing a problem means to analyze it and split it into separate components:

• objects, predicates
• state space
• selection criteria

Therefore the efficiency of Problem Solving depends on the underlying representations in a person’s mind. Analyzing the problem domain according to different dimensions, i.e., changing from one representation to another, results in arriving at a new understanding of a problem. This is basically what is described as restructuring.

There are two very different ways of approaching a goal-oriented situation . In one case an organism readily reproduces the response to the given problem from past experience. This is called reproductive thinking .

The second way requires something new and different to achieve the goal, prior learning is of little help here. Such productive thinking is (sometimes) argued to involve insight . Gestalt psychologists even state that insight problems are a separate category of problems in their own right.

Tasks that might involve insight usually have certain features – they require something new and non-obvious to be done and in most cases they are difficult enough to predict that the initial solution attempt will be unsuccessful. When you solve a problem of this kind you often have a so called "AHA-experience" – the solution pops up all of a sudden. At one time you do not have any ideas of the answer to the problem, you do not even feel to make any progress trying out different ideas, but in the next second the problem is solved.

Sometimes, previous experience or familiarity can even make problem solving more difficult. This is the case whenever habitual directions get in the way of finding new directions – an effect called fixation .

Functional fixedness

Functional fixedness concerns the solution of object-use problems . The basic idea is that when the usual way of using an object is emphasised, it will be far more difficult for a person to use that object in a novel manner.

An example is the two-string problem : Knut is left in a room with a chair and a pair of pliers given the task to bind two strings together that are hanging from the ceiling. The problem he faces is that he can never reach both strings at a time because they are just too far away from each other. What can Knut do?

Figure \(\PageIndex{1}\): Put the two strings together by tying the pliers to one of the strings and then swing it toward the other one.

Mental fixedness

Functional fixedness as involved in the examples above illustrates a mental set – a person’s tendency to respond to a given task in a manner based on past experience. Because Knut maps an object to a particular function he has difficulties to vary the way of use (pliers as pendulum's weight).

Problem-Solving Strategies

When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution. Regardless of strategy, you will likely be guided, consciously or unconsciously, by your knowledge of cause-effect relations among the elements of the problem and the similarity of the problem to previous problems you have solved before. As discussed in earlier sections of this chapter, innate dispositions of the brain to look for and represent causal and similarity relations are key components of general intelligence (Koenigshofer, 2017).

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them. For example, a well-known strategy is trial and error. The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Problem Solving as a Search Problem

The idea of regarding problem solving as a search problem originated from Alan Newell and Herbert Simon while trying to design computer programs which could solve certain problems. This led them to develop a program called General Problem Solver which was able to solve any well-defined problem by creating heuristics on the basis of the user's input. This input consisted of objects and operations that could be done on them.

As we already know, every problem is composed of an initial state, intermediate states and a goal state (also: desired or final state), while the initial and goal states characterise the situations before and after solving the problem. The intermediate states describe any possible situation between initial and goal state. The set of operators builds up the transitions between the states. A solution is defined as the sequence of operators which leads from the initial state across intermediate states to the goal state.

The simplest method to solve a problem, defined in these terms, is to search for a solution by just trying one possibility after another (also called trial and error ).

As already mentioned above, an organised search, following a specific strategy, might not be helpful for finding a solution to some ill-defined problem, since it is impossible to formalise such problems in a way that a search algorithm can find a solution.

As an example we could just take Knut and his essay: he has to find out about his own opinion and formulate it and he has to make sure he understands the sources texts. But there are no predefined operators he can use, there is no panacea how to get to an opinion and even not how to write it down.

Means-End Analysis

In Means-End Analysis you try to reduce the difference between initial state and goal state by creating sub-goals until a sub-goal can be reached directly (in computer science, what is called recursion works on this basis).

An example of a problem that can be solved by Means-End Analysis is the " Towers of Hanoi "

Figure \(\PageIndex{2}\): Towers of Hanoi with 8 discs – A well defined problem (image from Wikimedia Commons; https://commons.wikimedia.org/wiki/F..._of_Hanoi.jpeg , by User:Evanherk .licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license).

The initial state of this problem is described by the different sized discs being stacked in order of size on the first of three pegs (the “start-peg“). The goal state is described by these discs being stacked on the third pegs (the “end-peg“) in exactly the same order.

Figure \(\PageIndex{3}\): This animation shows the solution of the game "Tower of Hanoi" with four discs. (image from Wikimedia Commons; https://commons.wikimedia.org/wiki/F...of_Hanoi_4.gif ; by André Karwath aka Aka ; licensed under the Creative Commons Attribution-Share Alike 2.5 Generic license).

There are three operators:

• You are allowed to move one single disc from one peg to another one
• You are only able to move a disc if it is on top of one stack
• A disc cannot be put onto a smaller one.

In order to use Means-End Analysis we have to create sub-goals. One possible way of doing this is described in the picture:

1. Moving the discs lying on the biggest one onto the second peg.

2. Shifting the biggest disc to the third peg.

3. Moving the other ones onto the third peg, too

You can apply this strategy again and again in order to reduce the problem to the case where you only have to move a single disc – which is then something you are allowed to do.

Strategies of this kind can easily be formulated for a computer; the respective algorithm for the Towers of Hanoi would look like this:

1. move n-1 discs from A to B

2. move disc #n from A to C

3. move n-1 discs from B to C

where n is the total number of discs, A is the first peg, B the second, C the third one. Now the problem is reduced by one with each recursive loop.

Means-end analysis is important to solve everyday-problems – like getting the right train connection: You have to figure out where you catch the first train and where you want to arrive, first of all. Then you have to look for possible changes just in case you do not get a direct connection. Third, you have to figure out what are the best times of departure and arrival, on which platforms you leave and arrive and make it all fit together.

Analogies describe similar structures and interconnect them to clarify and explain certain relations. In a recent study, for example, a song that got stuck in your head is compared to an itching of the brain that can only be scratched by repeating the song over and over again. Useful analogies appears to be based on a psychological mapping of relations between two very disparate types of problems that have abstract relations in common. Applied to STEM problems, Gray and Holyoak (2021) state: "Analogy is a powerful tool for fostering conceptual understanding and transfer in STEM and other fields. Well-constructed analogical comparisons focus attention on the causal-relational structure of STEM concepts, and provide a powerful capability to draw inferences based on a well-understood source domain that can be applied to a novel target domain." Note that similarity between problems of different types in their abstract relations, such as causation, is a key feature of reasoning, problem-solving and inference when forming and using analogies. Recall the discussion of general intelligence in module 14.2. There, similarity relations, causal relations, and predictive relations between events were identified as key components of general intelligence, along with ability to visualize in imagination possible future actions and their probable outcomes prior to commiting to actual behavior in the physical world (Koenigshofer, 2017).

Restructuring by Using Analogies

One special kind of restructuring, the way already mentioned during the discussion of the Gestalt approach, is analogical problem solving. Here, to find a solution to one problem – the so called target problem, an analogous solution to another problem – the source problem, is presented.

An example for this kind of strategy is the radiation problem posed by K. Duncker in 1945:

As a doctor you have to treat a patient with a malignant, inoperable tumour, buried deep inside the body. There exists a special kind of ray, which is perfectly harmless at a low intensity, but at the sufficient high intensity is able to destroy the tumour – as well as the healthy tissue on his way to it. What can be done to avoid the latter?

When this question was asked to participants in an experiment, most of them couldn't come up with the appropriate answer to the problem. Then they were told a story that went something like this:

A General wanted to capture his enemy's fortress. He gathered a large army to launch a full-scale direct attack, but then learned, that all the roads leading directly towards the fortress were blocked by mines. These roadblocks were designed in such a way, that it was possible for small groups of the fortress-owner's men to pass them safely, but every large group of men would initially set them off. Now the General figured out the following plan: He divided his troops into several smaller groups and made each of them march down a different road, timed in such a way, that the entire army would reunite exactly when reaching the fortress and could hit with full strength.

Here, the story about the General is the source problem, and the radiation problem is the target problem. The fortress is analogous to the tumour and the big army corresponds to the highly intensive ray. Consequently a small group of soldiers represents a ray at low intensity. The solution to the problem is to split the ray up, as the general did with his army, and send the now harmless rays towards the tumour from different angles in such a way that they all meet when reaching it. No healthy tissue is damaged but the tumour itself gets destroyed by the ray at its full intensity.

M. Gick and K. Holyoak presented Duncker's radiation problem to a group of participants in 1980 and 1983. Only 10 percent of them were able to solve the problem right away, 30 percent could solve it when they read the story of the general before. After given an additional hint – to use the story as help – 75 percent of them solved the problem.

With this results, Gick and Holyoak concluded, that analogical problem solving depends on three steps:

1. Noticing that an analogical connection exists between the source and the target problem. 2. Mapping corresponding parts of the two problems onto each other (fortress → tumour, army → ray, etc.) 3. Applying the mapping to generate a parallel solution to the target problem (using little groups of soldiers approaching from different directions → sending several weaker rays from different directions)

The concept that links the target problem with the analogy (the “source problem“) is called problem schema. Gick and Holyoak obtained the activation of a schema on their participants by giving them two stories and asking them to compare and summarize them. This activation of problem schemata is called “schema induction“.

The two presented texts were picked out of six stories which describe analogical problems and their solution. One of these stories was "The General."

After solving the task the participants were asked to solve the radiation problem. The experiment showed that in order to solve the target problem reading of two stories with analogical problems is more helpful than reading only one story: After reading two stories 52% of the participants were able to solve the radiation problem (only 30% were able to solve it after reading only one story, namely: “The General“).

The process of using a schema or analogy, i.e. applying it to a novel situation, is called transduction . One can use a common strategy to solve problems of a new kind.

To create a good schema and finally get to a solution using the schema is a problem-solving skill that requires practice and some background knowledge.

How Do Experts Solve Problems?

With the term expert we describe someone who devotes large amounts of his or her time and energy to one specific field of interest in which he, subsequently, reaches a certain level of mastery. It should not be of surprise that experts tend to be better in solving problems in their field than novices (people who are beginners or not as well trained in a field as experts) are. They are faster in coming up with solutions and have a higher success rate of right solutions. But what is the difference between the way experts and non-experts solve problems? Research on the nature of expertise has come up with the following conclusions:

When it comes to problems that are situated outside the experts' field, their performance often does not differ from that of novices.

Knowledge: An experiment by Chase and Simon (1973a, b) dealt with the question how well experts and novices are able to reproduce positions of chess pieces on chessboards when these are presented to them only briefly. The results showed that experts were far better in reproducing actual game positions, but that their performance was comparable with that of novices when the chess pieces were arranged randomly on the board. Chase and Simon concluded that the superior performance on actual game positions was due to the ability to recognize familiar patterns: A chess expert has up to 50,000 patterns stored in his memory. In comparison, a good player might know about 1,000 patterns by heart and a novice only few to none at all. This very detailed knowledge is of crucial help when an expert is confronted with a new problem in his field. Still, it is not pure size of knowledge that makes an expert more successful. Experts also organise their knowledge quite differently from novices.

Organization: In 1982 M. Chi and her co-workers took a set of 24 physics problems and presented them to a group of physics professors as well as to a group of students with only one semester of physics. The task was to group the problems based on their similarities. As it turned out the students tended to group the problems based on their surface structure (similarities of objects used in the problem, e.g. on sketches illustrating the problem), whereas the professors used their deep structure (the general physical principles that underlay the problems) as criteria. By recognizing the actual structure of a problem experts are able to connect the given task to the relevant knowledge they already have (e.g. another problem they solved earlier which required the same strategy).

Analysis: Experts often spend more time analyzing a problem before actually trying to solve it. This way of approaching a problem may often result in what appears to be a slow start, but in the long run this strategy is much more effective. A novice, on the other hand, might start working on the problem right away, but often has to realise that he reaches dead ends as he chose a wrong path in the very beginning.

Creative Cognition

Divergent thinking.

The term divergent thinking describes a way of thinking that does not lead to one goal, but is open-ended. Problems that are solved this way can have a large number of potential 'solutions' of which none is exactly 'right' or 'wrong', though some might be more suitable than others.

Solving a problem like this involves indirect and productive thinking and is mostly very helpful when somebody faces an ill-defined problem , i.e. when either initial state or goal state cannot be stated clearly and operators are either insufficient or not given at all.

The process of divergent thinking is often associated with creativity, and it undoubtedly leads to many creative ideas. Nevertheless, researches have shown that there is only modest correlation between performance on divergent thinking tasks and other measures of creativity. Additionally it was found that in processes resulting in original and practical inventions things like searching for solutions, being aware of structures and looking for analogies are heavily involved, too.

Figure \(\PageIndex{4}\): functional MRI images of the brains of musicians playing improvised jazz revealed that a large brain region involved in monitoring one's performance shuts down during creative improvisation, while a small region involved in organizing self-initiated thoughts and behaviors is highly activated (Image and modified caption from Wikimedia Commons. File:Creative Improvisation (24130148711).jpg; https://commons.wikimedia.org/wiki/F...130148711).jpg ; by NIH Image Gallery ; As a work of the U.S. federal government , the image is in the public domain .

Convergent Thinking

Convergent thinking patterns are problem solving techniques that unite different ideas or fields to find a solution. The focus of this mindset is speed, logic and accuracy, also identification of facts, reapplying existing techniques, gathering information. The most important factor of this mindset is: there is only one correct answer. You only think of two answers, namely right or wrong. This type of thinking is associated with certain science or standard procedures. People with this type of thinking have logical thinking, are able to memorize patterns, solve problems and work on scientific tests. Most school subjects sharpen this type of thinking ability.

Research shows that the creative process involves both types of thought processes.

Brain Mechanisms in Problem Solving

Presenting Neurophysiology in its entirety would be enough to fill several books. Instead, let's focus only on the aspects that are especially relevant to problem solving. Still, this topic is quite complex and problem solving cannot be attributed to one single brain area. Rather there are systems or networks of several brain areas working together to perform a specific problem solving task. This is best shown by an example, playing chess:

Table 2: Brain areas involved in a complex cognitive task.

One of the key tasks, namely planning and executing strategies , is performed by the prefrontal cortex (PFC) , which also plays an important role for several other tasks correlated with problem solving. This can be made clear from the effects of damage to the PFC on ability to solve problems.

Patients with a lesion in this brain area have difficulty switching from one behavioral pattern to another. A well known example is the wisconsin card-sorting task . A patient with a PFC lesion who is told to separate all blue cards from a deck, would continue sorting out the blue ones, even if the experimenter next told him to sort out all brown cards. Transferred to a more complex problem, this person would most likely fail, because he is not flexible enough to change his strategy after running into a dead end or when the problem changes.

Another example comes from a young homemaker, who had a tumour in the frontal lobe. Even though she was able to cook individual dishes, preparing a whole family meal was an impossible task for her.

Mushiake et al. (2009) note that to achieve a goal in a complex environment, such as problem‐solving situations like those above, we must plan multiple steps of action. When planning a series of actions, we have to anticipate future outcomes that will occur as a result of each action, and, in addition, we must mentally organize the temporal sequence of events in order to achieve the goal. These researchers investigated the role of lateral prefrontal cortex (PFC) in problem solving in monkeys. They found that "PFC neurons reflected final goals and immediate goals during the preparatory period. [They] also found some PFC neurons reflected each of all the forthcoming steps of actions during the preparatory period and they increased their [neural] activity step by step during the execution period. [Furthermore, they] found that the transient increase in synchronous activity of PFC neurons was involved in goal subgoal transformations. [They concluded] that the PFC is involved primarily in the dynamic representation of multiple future events that occur as a consequence of behavioral actions in problem‐solving situations" (Mushiake et al., 2009, p. 1). In other words, the prefrontal cortex represents in our imagination the sequence of events following each step that we take in solving a particular problem, guiding us step by step to the solution.

As the examples above illustrate, the structure of our brain is of great importance regarding problem solving, i.e. cognitive life. But how was our cognitive apparatus designed? How did perception-action integration as a central species-specific property of humans come about? The answer, as argued extensively in earlier sections of this book, is, of course, natural selection and other forces of genetic evolution. Clearly, animals and humans with genes facilitating brain organization that led to good problem solving skills would be favored by natural selection over genes responsible for brain organization less adept at solving problems. We became equipped with brains organized for effective problem solving because flexible abilities to solve a wide range of problems presented by the environment enhanced ability to survive, to compete for resources, to escape predators, and to reproduce (see chapter on Evolution and Genetics in this text).

In short, good problem solving mechanisms in brains designed for the real world gave a competitive advantage and increased biological fitness. Consequently, humans (and many other animals to a lesser degree) have "innate ability to problem-solve in the real world. Solving real world problems in real time given constraints posed by one's environment is crucial for survival . . . Real world problem solving (RWPS) is different from those that occur in a classroom or in a laboratory during an experiment. They are often dynamic and discontinuous, accompanied by many starts and stops . . . Real world problems are typically ill-defined, and even when they are well-defined, often have open-ended solutions . . . RWPS is quite messy and involves a tight interplay between problem solving, creativity, and insight . . . In psychology and neuroscience, problem-solving broadly refers to the inferential steps taken by an agent [human, animal, or computer] that leads from a given state of affairs to a desired goal state" (Sarathy, 2018, p. 261-2). According to Sarathy (2018), the initial stage of RWPS requires defining the problem and generating a representation of it in working memory. This stage involves activation of parts of the " prefrontal cortex (PFC) , default mode network (DMN) , and the dorsal anterior cingulate cortex (dACC) ." The DMN includes the medial prefrontal cortex , posterior cingulate cortex , and the inferior parietal lobule . Other structures sometimes considered part of the network are the lateral temporal cortex , hippocampal formation , and the precuneus . This network of structures is called "default mode" because these structures show increased activity when one is not engaged in focused, attentive, goal-directed actions, but rather a "resting state" (a baseline default state) and show decreased neural activity when one is focused and attentive to a particular goal-directed behavior (Raichle, et al., 2001).

Moral Reasoning

Jeurissen, et al., (2014) examined a special type of reasoning, moral reasoning, using TMS (Transcranial Magnetic Stimulation). The dorsolateral prefrontal cortex (DLPFC) and temporal-parietal junction (TPJ) have both been shown to be involved in moral judgments, but this study by Jeurissen, et al., (2014) uses TMS to tease out the different roles these brain areas play in different scenarios involving moral dilemmas.

Moral dilemmas have been categorized by researchers as moral-impersonal (e.g., trolley or switch dilemma-- save the lives of five workmen at the expense of the life of one by switching train to another track) and moral-personal dilemmas (e.g., footbridge dilemma-- push a strange r in front of a train to save the lives of five others). In the first scenario, the person just pulls a switch resulting in death of one person to save five, but in the second, the person pushes the victim to their death to save five others.

Dual-process theory proposes that moral decision-making involves two components: an automatic emotional response and a voluntary application of a utilitarian decision-rule (in this case, one death to save five people is worth it). The thought of being responsible for the death of another person elicits an aversive emotional response, but at the same time, cognitive reasoning favors the utilitarian option. Decision making and social cognition are often associated with the DLPFC. Neurons in the prefrontal cortex have been found to be involved in cost-benefit analysis and categorize stimuli based on the predicted consequences.

Theory-of-mind (TOM) is a cognitive mechanism which is used when one tries to understand and explain the knowledge, beliefs, and intention of others. TOM and empathy are often associated with TPJ functioning .

In the article by Jeurissen, et al., (2014), brain activity is measured by BOLD. BOLD refers to Blood-oxygen-level-dependent imaging , or BOLD-contrast imaging, which is a way to measure neural activity in different brain areas in MRI images .

Greene et al., 2001 (Links to an external site.) , 2004 (Links to an external site.) reported that activity in the prefrontal cortex is thought to be important for the cognitive reasoning process , which can counteract the automatic emotional response that occurs in moral dilemmas like the one in Jeurissen, et al., (2014). Greene et al. (2001) (Links to an external site.) found that the medial portions of the medial frontal gyrus, the posterior cingulate gyrus, and the bilateral angular gyrus showed a higher BOLD response in the moral-personal condition than the moral-impersonal condition. The right middle frontal gyrus and the bilateral parietal lobes showed a lower BOLD response in the moral-personal condition than in the moral impersonal. Furthermore, Greene et al. (2004) (Links to an external site.) showed an increased BOLD response for the bilateral amygdale in personal compared to the impersonal dilemmas.

Given the role of the prefrontal cortex in moral decision-making, Jeurissen, et al., (2014) hypothesized that when magnetically stimulating prefrontal cortex , they will selectively influence the decision process of the moral personal dilemmas because the cognitive reasoning for which the DLPFC is important is disrupted , thereby releasing the emotional component making it more influential in the resolution of the dilemma. Because the activity in the TPJ is related to emotional processing and theory of mind ( Saxe and Kanwisher, 2003 (Links to an external site.) ; Young et al., 2010 (Links to an external site.) ), Jeurissen, et al., (2014) hypothesized that when magnetically stimulating this area, the TPJ, during a moral decision, this will selectively influence the decision process of moral-impersonal dilemmas.

Results of this study by Jeurissen, et al., (2014) showed an important role of the TPJ in moral judgment . Experiments using fMRI ( Greene et al., 2004 (Links to an external site.) ), have found the cingulate cortex to be involved in moral judgment . In earlier studies, the cingulate cortex was found to be involved in the emotional response. Since the moral-personal dilemmas are more emotional ly salient, the higher activity observed for TPJ in the moral-personal condition (more emotional) is consistent with this view. Another area that is hypothesized to be associated with the emotional response is the temporal cortex . In this study by Jeurissen, et al., (2014) , magnetic stimulation of the right DLPFC and right TPJ shows roles for right DLPFC (reasoning and utilitarian) and right TPJ (emotion) in moral impersonal and moral personal dilemmas respectively. TMS over the right DLPFC (disrupting neural activity here) leads to behavior changes consistent with less cognitive control over emotion . After right DLPFC stimulation, participants show less feelings of regret than after magnetic stimulation of the right TPJ. This last finding indicates that the right DLPFC is involved in evaluating the outcome of the decision process. In summary, this experiment by Jeurissen, et al., (2014) adds to evidence of a critical role of right DLPFC and right TPJ in moral decision-making and supports that hypothesis that the former is involved in judgments based on cognitive reasoning and anticipation of outcomes, whereas the latter is involved in emotional processing related to moral dilemmas.

Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. The brain mechanisms involved in problem solving vary to some degree depending upon the sensory modalities involved in the problem and its solution, however, the prefrontal cortex is one brain region that appears to be centrally involved in all problem-solving. The prefrontal cortex is required for flexible shifts in attention, for representing the problem in working memory, and for holding steps in problem solving in working memory along with representations of future consequences of those actions permitting planning and execution of plans. Also implicated is the Default Mode Network (DMN) including medial prefrontal cortex, posterior cingulate cortex, and the inferior parietal module, and sometimes the lateral temporal cortex, hippocampus, and the precuneus. Moral reasoning involves a different set of brain areas, primarily the dorsolateral prefrontal cortex (DLPFC) and temporal-parietal junction (TPJ).

Review Questions

• an algorithm
• a heuristic
• a mental set
• trial and error

Gray, M. E., & Holyoak, K. J. (2021). Teaching by analogy: From theory to practice. Mind, Brain, and Education , 15 (3), 250-263.

Hunt, L. T., Behrens, T. E., Hosokawa, T., Wallis, J. D., & Kennerley, S. W. (2015). Capturing the temporal evolution of choice across prefrontal cortex. Elife , 4 , e11945.

Mushiake, H., Sakamoto, K., Saito, N., Inui, T., Aihara, K., & Tanji, J. (2009). Involvement of the prefrontal cortex in problem solving. International review of neurobiology , 85 , 1-11.

Jeurissen, D., Sack, A. T., Roebroeck, A., Russ, B. E., & Pascual-Leone, A. (2014). TMS affects moral judgment, showing the role of DLPFC and TPJ in cognitive and emotional processing. Frontiers in neuroscience , 8 , 18.

Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus, and Giroux.

Koenigshofer, K. A. (2017). General Intelligence: Adaptation to Evolutionarily Familiar Abstract Relational Invariants, Not to Environmental or Evolutionary Novelty. The Journal of Mind and Behavior , 119-153.

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Attributions

"Overview," "Problem Solving Strategies," adapted from Problem Solving by OpenStax Colleg licensed CC BY-NC 4.0 via OER Commons

"Defining Problems," "Problem Solving as a Search Problem," "Creative Cognition," "Brain Mechanisms in Problem-Solving" adapted by Kenneth A. Koenigshofer, Ph.D., from 2.1, 2.2, 2.3, 2.4, 2.5, 2.6 in Cognitive Psychology and Cognitive Neuroscience (Wikibooks) https://en.wikibooks.org/wiki/Cognit...e_Neuroscience ; unless otherwise noted, LibreTexts content is licensed by CC BY-NC-SA 3.0 . Legal ; the LibreTexts libraries are Powered by MindTouch

Moral Reasoning was written by Kenneth A. Koenigshofer, Ph.D, Chaffey College.

Categories and Concepts

People form mental concepts of categories of objects, which permit them to respond appropriately to new objects they encounter. Most concepts cannot be strictly defined but are organized around the “best” examples or prototypes, which have the properties most common in the category. Objects fall into many different categories, but there is usually a most salient one, called the basic-level category, which is at an intermediate level of specificity (e.g., chairs, rather than furniture or desk chairs). Concepts are closely related to our knowledge of the world, and people can more easily learn concepts that are consistent with their knowledge. Theories of concepts argue either that people learn a summary description of a whole category or else that they learn exemplars of the category. Recent research suggests that there are different ways to learn and represent concepts and that they are accomplished by different neural systems.

• Understand the problems with attempting to define categories.
• Understand typicality and fuzzy category boundaries.
• Learn about theories of the mental representation of concepts.
• Learn how knowledge may influence concept learning.

Introduction

Consider the following set of objects: some dust, papers, a computer monitor, two pens, a cup, and an orange. What do these things have in common? Only that they all happen to be on my desk as I write this. This set of things can be considered a category , a set of objects that can be treated as equivalent in some way. But, most of our categories seem much more informative—they share many properties. For example, consider the following categories: trucks, wireless devices, weddings, psychopaths, and trout. Although the objects in a given category are different from one another, they have many commonalities. When you know something is a truck, you know quite a bit about it. The psychology of categories concerns how people learn, remember, and use informative categories such as trucks or psychopaths.

The mental representations we form of categories are called concepts . There is a category of trucks in the actual physical world, and I also have a concept of trucks in my head. We assume that people’s concepts correspond more or less closely to the actual category, but it can be useful to distinguish the two, as when someone’s concept is not really correct.

Concepts are at the core of intelligent behavior . We expect people to be able to know what to do in new situations and when confronting new objects. If you go into a new classroom and see chairs, a blackboard, a projector, and a screen, you know what these things are and how they will be used. You’ll sit on one of the chairs and expect the instructor to write on the blackboard or project something onto the screen. You do this even if you have never seen any of these particular objects before , because you have concepts of classrooms, chairs, projectors, and so forth, that tell you what they are and what you’re supposed to do with them. Furthermore, if someone tells you a new fact about the projector—for example, that it has a halogen bulb—you are likely to extend this fact to other projectors you encounter. In short, concepts allow you to extend what you have learned about a limited number of objects to a potentially infinite set of entities (i.e. generalization ). Notice how categories and concepts arise from similarity, one of the abstract features of the world that has been genetically internalized into the brain during evolution , creating an innate disposition of brains to search for and to represent groupings of similar things, forming one component of general intelligence. One property of the human brain that distinguishes us from other animals is the high degrees of abstraction in similarity relations that the human brain is capable of encoding compared to the brains of non-human animals (Koenigshofer, 2017).

Simpler organisms, such as animals and human infants, also have concepts ( Mareschal, Quinn, & Lea, 2010 ). Squirrels may have a concept of predators, for example, that is specific to their own lives and experiences. However, animals likely have many fewer concepts and cannot understand complex concepts such as mortgages or musical instruments.

You know thousands of categories, most of which you have learned without careful study or instruction. Although this accomplishment may seem simple, we know that it isn’t, because it is difficult to program computers to solve such intellectual tasks. If you teach a learning program that a robin, a swallow, and a duck are all birds, it may not recognize a cardinal or peacock as a bird. However, this shortcoming in computers may be at least partially overcome when the type of processing used is parallel distributed processing as employed in artificial neural networks (Koenigshofer, 2017), discussed in this chapter. As we’ll shortly see, the problem for computers is that objects in categories are often surprisingly diverse.

Nature of Categories

Traditionally, it has been assumed that categories are well-defined . This means that you can give a definition that specifies what is in and out of the category. Such a definition has two parts. First, it provides the necessary features for category membership: What must objects have in order to be in it? Second, those features must be jointly sufficient for membership: If an object has those features, then it is in the category. For example, if I defined a dog as a four-legged animal that barks, this would mean that every dog is four-legged, an animal, and barks, and also that anything that has all those properties is a dog.

Unfortunately, it has not been possible to find definitions for many familiar categories. Definitions are neat and clear-cut; the world is messy and often unclear. For example, consider our definition of dogs. In reality, not all dogs have four legs; not all dogs bark. I knew a dog that lost her bark with age (this was an improvement); no one doubted that she was still a dog. It is often possible to find some necessary features (e.g., all dogs have blood and breathe), but these features are generally not sufficient to determine category membership (you also have blood and breathe but are not a dog).

Even in domains where one might expect to find clear-cut definitions, such as science and law, there are often problems. For example, many people were upset when Pluto was downgraded from its status as a planet to a dwarf planet in 2006. Upset turned to outrage when they discovered that there was no hard-and-fast definition of planethood: “Aren’t these astronomers scientists? Can’t they make a simple definition?” In fact, they couldn’t. After an astronomical organization tried to make a definition for planets, a number of astronomers complained that it might not include accepted planets such as Neptune and refused to use it. If everything looked like our Earth, our moon, and our sun, it would be easy to give definitions of planets, moons, and stars, but the universe has not conformed to this ideal.

Fuzzy Categories

Borderline items.

Experiments also showed that the psychological assumptions of well-defined categories were not correct. Hampton ( 1979 ) asked subjects to judge whether a number of items were in different categories. He did not find that items were either clear members or clear nonmembers. Instead, he found many items that were just barely considered category members and others that were just barely not members, with much disagreement among subjects. Sinks were barely considered as members of the kitchen utensil category, and sponges were barely excluded. People just included seaweed as a vegetable and just barely excluded tomatoes and gourds. Hampton found that members and nonmembers formed a continuum, with no obvious break in people’s membership judgments. If categories were well defined, such examples should be very rare. Many studies since then have found such borderline members that are not clearly in or clearly out of the category.

McCloskey and Glucksberg ( 1978 ) found further evidence for borderline membership by asking people to judge category membership twice, separated by two weeks. They found that when people made repeated category judgments such as “Is an olive a fruit?” or “Is a sponge a kitchen utensil?” they changed their minds about borderline items—up to 22 percent of the time. So, not only do people disagree with one another about borderline items, they disagree with themselves! As a result, researchers often say that categories are fuzzy , that is, they have unclear boundaries that can shift over time.

A related finding that turns out to be most important is that even among items that clearly are in a category, some seem to be “better” members than others ( Rosch, 1973 ). Among birds, for example, robins and sparrows are very typical . In contrast, ostriches and penguins are very atypical (meaning not typical). If someone says, “There’s a bird in my yard,” the image you have will be of a smallish passerine bird such as a robin, not an eagle or hummingbird or turkey.

You can find out which category members are typical merely by asking people. Table 1 shows a list of category members in order of their rated typicality. Typicality is perhaps the most important variable in predicting how people interact with categories. The following text box is a partial list of what typicality influences.

We can understand the two phenomena of borderline members and typicality as two sides of the same coin. Think of the most typical category member: This is often called the category prototype . Items that are less and less similar to the prototype become less and less typical. At some point, these less typical items become so atypical that you start to doubt whether they are in the category at all. Is a rug really an example of furniture? It’s in the home like chairs and tables, but it’s also different from most furniture in its structure and use. From day to day, you might change your mind as to whether this atypical example is in or out of the category. So, changes in typicality ultimately lead to borderline members.

Source of Typicality

Intuitively, it is not surprising that robins are better examples of birds than penguins are, or that a table is a more typical kind of furniture than is a rug. But given that robins and penguins are known to be birds, why should one be more typical than the other? One possible answer is the frequency with which we encounter the object: We see a lot more robins than penguins, so they must be more typical. Frequency does have some effect, but it is actually not the most important variable ( Rosch, Simpson, & Miller, 1976 ). For example, I see both rugs and tables every single day, but one of them is much more typical as furniture than the other.

The best account of what makes something typical comes from Rosch and Mervis’s ( 1975 ) family resemblance theory . They proposed that items are likely to be typical if they (a) have the features that are frequent in the category and (b) do not have features frequent in other categories. Let’s compare two extremes, robins and penguins. Robins are small flying birds that sing, live in nests in trees, migrate in winter, hop around on your lawn, and so on. Most of these properties are found in many other birds. In contrast, penguins do not fly, do not sing, do not live in nests or in trees, do not hop around on your lawn. Furthermore, they have properties that are common in other categories, such as swimming expertly and having wings that look and act like fins. These properties are more often found in fish than in birds.

According to Rosch and Mervis, then, it is not because a robin is a very common bird that makes it typical. Rather, it is because the robin has the shape, size, body parts, and behaviors that are very common (i.e. most similar) among birds—and not common among fish, mammals, bugs, and so forth.

In a classic experiment, Rosch and Mervis ( 1975 ) made up two new categories, with arbitrary features. Subjects viewed example after example and had to learn which example was in which category. Rosch and Mervis constructed some items that had features that were common in the category and other items that had features less common in the category. The subjects learned the first type of item before they learned the second type. Furthermore, they then rated the items with common features as more typical. In another experiment, Rosch and Mervis constructed items that differed in how many features were shared with a different category. The more features were shared, the longer it took subjects to learn which category the item was in. These experiments, and many later studies, support both parts of the family resemblance theory.

Category Hierarchies

Many important categories fall into hierarchies , in which more concrete categories are nested inside larger, abstract categories. For example, consider the categories: brown bear, bear, mammal, vertebrate, animal, entity. Clearly, all brown bears are bears; all bears are mammals; all mammals are vertebrates; and so on. Any given object typically does not fall into just one category—it could be in a dozen different categories, some of which are structured in this hierarchical manner. Examples of biological categories come to mind most easily, but within the realm of human artifacts, hierarchical structures can readily be found: desk chair, chair, furniture, artifact, object.

Brown ( 1958 ), a child language researcher, was perhaps the first to note that there seems to be a preference for which category we use to label things. If your office desk chair is in the way, you’ll probably say, “Move that chair,” rather than “Move that desk chair” or “piece of furniture.” Brown thought that the use of a single, consistent name probably helped children to learn the name for things. And, indeed, children’s first labels for categories tend to be exactly those names that adults prefer to use ( Anglin, 1977 ).

This preference is referred to as a preference for the basic level of categorization , and it was first studied in detail by Eleanor Rosch and her students ( Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976 ). The basic level represents a kind of Goldilocks effect, in which the category used for something is not too small (northern brown bear) and not too big (animal), but is just right (bear). The simplest way to identify an object’s basic-level category is to discover how it would be labeled in a neutral situation. Rosch et al. ( 1976 ) showed subjects pictures and asked them to provide the first name that came to mind. They found that 1,595 names were at the basic level, with 14 more specific names ( subordinates ) used. Only once did anyone use a more general name ( superordinate ). Furthermore, in printed text, basic-level labels are much more frequent than most subordinate or superordinate labels (e.g., Wisniewski & Murphy, 1989 ).

The preference for the basic level is not merely a matter of labeling. Basic-level categories are usually easier to learn. As Brown noted, children use these categories first in language learning, and superordinates are especially difficult for children to fully acquire. [1] People are faster at identifying objects as members of basic-level categories ( Rosch et al., 1976 ).

Rosch et al. ( 1976 ) initially proposed that basic-level categories cut the world at its joints, that is, merely reflect the big differences between categories like chairs and tables or between cats and mice that exist in the world. However, it turns out that which level is basic is not universal. North Americans are likely to use names like tree, fish , and bird to label natural objects. But people in less industrialized societies seldom use these labels and instead use more specific words, equivalent to elm, trout, and finch ( Berlin, 1992 ). Because Americans and many other people living in industrialized societies know so much less than our ancestors did about the natural world, our basic level has “moved up” to what would have been the superordinate level a century ago. Furthermore, experts in a domain often have a preferred level that is more specific than that of non-experts. Birdwatchers see sparrows rather than just birds, and carpenters see roofing hammers rather than just hammers ( Tanaka & Taylor, 1991 ). This all suggests that the preferred level is not (only) based on how different categories are in the world, but that people’s knowledge and interest in the categories has an important effect.

One explanation of the basic-level preference is that basic-level categories are more differentiated: The category members are similar to one another, but they are different from members of other categories ( Murphy & Brownell, 1985 ; Rosch et al., 1976 ). (The alert reader will note a similarity to the explanation of typicality I gave above. However, here we’re talking about the entire category and not individual members.) Chairs are pretty similar to one another, sharing a lot of features (legs, a seat, a back, similar size and shape); they also don’t share that many features with other furniture. Superordinate categories are not as useful because their members are not very similar to one another. What features are common to most furniture? There are very few. Subordinate categories are not as useful, because they’re very similar to other categories: Desk chairs are quite similar to dining room chairs and easy chairs. As a result, it can be difficult to decide which subordinate category an object is in ( Murphy & Brownell, 1985 ). Experts can differ from novices in which categories are the most differentiated, because they know different things about the categories, therefore changing how similar the categories are.

[1] This is a controversial claim, as some say that infants learn superordinates before anything else (Mandler, 2004). However, if true, then it is very puzzling that older children have great difficulty learning the correct meaning of words for superordinates, as well as in learning artificial superordinate categories (Horton & Markman, 1980; Mervis, 1987). However, it seems fair to say that the answer to this question is not yet fully known.

Theories of Concept Representation

Now that we know these facts about the psychology of concepts, the question arises of how concepts are mentally represented. There have been two main answers. The first, somewhat confusingly called the prototype theory suggests that people have a summary representation of the category, a mental description that is meant to apply to the category as a whole. (The significance of summary will become apparent when the next theory is described.) This description can be represented as a set of weighted features ( Smith & Medin, 1981 ). The features are weighted by their frequency in the category. For the category of birds, having wings and feathers would have a very high weight; eating worms would have a lower weight; living in Antarctica would have a lower weight still, but not zero, as some birds do live there.

The idea behind prototype theory is that when you learn a category, you learn a general description that applies to the category as a whole: Birds have wings and usually fly; some eat worms; some swim underwater to catch fish. People can state these generalizations, and sometimes we learn about categories by reading or hearing such statements (“The kimodo dragon can grow to be 10 feet long”).

When you try to classify an item, you see how well it matches that weighted list of features. For example, if you saw something with wings and feathers fly onto your front lawn and eat a worm, you could (unconsciously) consult your concepts and see which ones contained the features you observed. This example possesses many of the highly weighted bird features, and so it should be easy to identify as a bird.

This theory readily explains the phenomena we discussed earlier. Typical category members have more, higher-weighted features. Therefore, it is easier to match them to your conceptual representation. Less typical items have fewer or lower-weighted features (and they may have features of other concepts). Therefore, they don’t match your representation as well (less similarity). This makes people less certain in classifying such items. Borderline items may have features in common with multiple categories or not be very close to any of them. For example, edible seaweed does not have many of the common features of vegetables but also is not close to any other food concept (meat, fish, fruit, etc.), making it hard to know what kind of food it is.

A very different account of concept representation is the exemplar theory ( exemplar being a fancy name for an example; Medin & Schaffer, 1978 ). This theory denies that there is a summary representation. Instead, the theory claims that your concept of vegetables is remembered examples of vegetables you have seen. This could of course be hundreds or thousands of exemplars over the course of your life, though we don’t know for sure how many exemplars you actually remember.

How does this theory explain classification? When you see an object, you (unconsciously) compare it to the exemplars in your memory, and you judge how similar it is to exemplars in different categories. For example, if you see some object on your plate and want to identify it, it will probably activate memories of vegetables, meats, fruit, and so on. In order to categorize this object, you calculate how similar it is to each exemplar in your memory. These similarity scores are added up for each category. Perhaps the object is very similar to a large number of vegetable exemplars, moderately similar to a few fruit, and only minimally similar to some exemplars of meat you remember. These similarity scores are compared, and the category with the highest score is chosen . [2]

Why would someone propose such a theory of concepts? One answer is that in many experiments studying concepts, people learn concepts by seeing exemplars over and over again until they learn to classify them correctly. Under such conditions, it seems likely that people eventually memorize the exemplars ( Smith & Minda, 1998 ). There is also evidence that close similarity to well-remembered objects has a large effect on classification . Allen and Brooks ( 1991 ) taught people to classify items by following a rule. However, they also had their subjects study the items, which were richly detailed. In a later test, the experimenters gave people new items that were very similar to one of the old items but were in a different category. That is, they changed one property so that the item no longer followed the rule. They discovered that people were often fooled by such items. Rather than following the category rule they had been taught, they seemed to recognize the new item as being very similar to an old one and so put it, incorrectly, into the same category.

Many experiments have been done to compare the prototype and exemplar theories. Overall, the exemplar theory seems to have won most of these comparisons . However, the experiments are somewhat limited in that they usually involve a small number of exemplars that people view over and over again. It is not so clear that exemplar theory can explain real-world classification in which people do not spend much time learning individual items (how much time do you spend studying squirrels? or chairs?). Also, given that some part of our knowledge of categories is learned through general statements we read or hear, it seems that there must be room for a summary description separate from exemplar memory.

Many researchers would now acknowledge that concepts are represented through multiple cognitive systems. For example, your knowledge of dogs may be in part through general descriptions such as “dogs have four legs.” But you probably also have strong memories of some exemplars (your family dog, Lassie) that influence your categorization. Furthermore, some categories also involve rules (e.g., a strike in baseball). How these systems work together is the subject of current study.

[2] Actually, the decision of which category is chosen is more complex than this, but the details are beyond this discussion.

The final topic has to do with how concepts fit with our broader knowledge of the world. We have been talking very generally about people learning the features of concepts. For example, they see a number of birds and then learn that birds generally have wings, or perhaps they remember bird exemplars. From this perspective, it makes no difference what those exemplars or features are—people just learn them. But consider two possible concepts of buildings and their features in Table 2.

Imagine you had to learn these two concepts by seeing exemplars of them, each exemplar having some of the features listed for the concept (as well as some idiosyncratic features). Learning the donker concept would be pretty easy. It seems to be a kind of underwater building, perhaps for deep-sea explorers. Its features seem to go together. In contrast, the blegdav doesn’t really make sense. If it’s in the desert, how can you get there by submarine, and why do they have polar bears as pets? Why would farmers live in the desert or use submarines? What good would steel windows do in such a building? This concept seems peculiar. In fact, if people are asked to learn new concepts that make sense, such as donkers, they learn them quite a bit faster than concepts such as blegdavs that don’t make sense ( Murphy & Allopenna, 1994 ). Furthermore, the features that seem connected to one another (such as being underwater and getting there by submarine) are learned better than features that don’t seem related to the others (such as being red).

Such effects demonstrate that when we learn new concepts, we try to connect them to the knowledge we already have about the world. If you were to learn about a new animal that doesn’t seem to eat or reproduce, you would be very puzzled and think that you must have gotten something wrong. By themselves, the prototype and exemplar theories don’t predict this. They simply say that you learn descriptions or exemplars, and they don’t put any constraints on what those descriptions or exemplars are. However, the knowledge approach to concepts emphasizes that concepts are meant to tell us about real things in the world, and so our knowledge of the world is used in learning and thinking about concepts.

We can see this effect of knowledge when we learn about new pieces of technology. For example, most people could easily learn about tablet computers (such as iPads) when they were first introduced by drawing on their knowledge of laptops, cell phones, and related technology. Of course, this reliance on past knowledge can also lead to errors, as when people don’t learn about features of their new tablet that weren’t present in their cell phone or expect the tablet to be able to do something it can’t.

One important aspect of people’s knowledge about categories is called psychological essentialism ( Gelman, 2003 ; Medin & Ortony, 1989 ). People tend to believe that some categories—most notably natural kinds such as animals, plants, or minerals—have an underlying property that is found only in that category and that causes its other features. Most categories don’t actually have essences, but this is sometimes a firmly held belief. For example, many people will state that there is something about dogs, perhaps some specific gene or set of genes, that all dogs have and that makes them bark, have fur, and look the way they do. Therefore, decisions about whether something is a dog do not depend only on features that you can easily see but also on the assumed presence of this cause.

Belief in an essence can be revealed through experiments describing fictional objects. Keil ( 1989 ) described to adults and children a fiendish operation in which someone took a raccoon, dyed its hair black with a white stripe down the middle, and implanted a “sac of super-smelly yucky stuff” under its tail. The subjects were shown a picture of a skunk and told that this is now what the animal looks like. What is it? Adults and children over the age of 4 all agreed that the animal is still a raccoon. It may look and even act like a skunk, but a raccoon cannot change its stripes (or whatever!)—it will always be a raccoon.

Importantly, the same effect was not found when Keil described a coffeepot that was operated on to look like and function as a bird feeder. Subjects agreed that it was now a bird feeder. Artifacts don’t have an essence.

Signs of essentialism include (a) objects are believed to be either in or out of the category, with no in-between; (b) resistance to change of category membership or of properties connected to the essence; and (c) for living things, the essence is passed on to progeny.

Essentialism is probably helpful in dealing with much of the natural world, but it may be less helpful when it is applied to humans. Considerable evidence suggests that people think of gender, racial, and ethnic groups as having essences, which serves to emphasize the difference between groups and even justify discrimination ( Hirschfeld, 1996 ). Historically, group differences were described by inheriting the blood of one’s family or group. “Bad blood” was not just an expression but a belief that negative properties were inherited and could not be changed. After all, if it is in the nature of “those people” to be dishonest (or clannish or athletic ...), then that could hardly be changed, any more than a raccoon can change into a skunk.

Research on categories of people is an exciting ongoing enterprise, and we still do not know as much as we would like to about how concepts of different kinds of people are learned in childhood and how they may (or may not) change in adulthood. Essentialism doesn’t apply only to person categories, but it is one important factor in how we think of groups.

Concepts are central to our everyday thought. When we are planning for the future or thinking about our past, we think about specific events and objects in terms of their categories. If you’re visiting a friend with a new baby, you have some expectations about what the baby will do, what gifts would be appropriate, how you should behave toward it, and so on. Knowing about the category of babies helps you to effectively plan and behave when you encounter this child you’ve never seen before. Such inferences from knowledge about a category are highly adaptive and an important component of thinking and intelligence.

Learning about those categories is a complex process that involves seeing exemplars (babies), hearing or reading general descriptions (“Babies like black-and-white pictures”), general knowledge (babies have kidneys), and learning the occasional rule (all babies have a rooting reflex). Current research is focusing on how these different processes take place in the brain. It seems likely that these different aspects of concepts are accomplished by different neural structures ( Maddox & Ashby, 2004 ). However, it is clear that the brain is genetically predisposed to seek out similarities in the environment and to represent groupings of things forming categories that can be used to make inferences about new instances of the category which have never been encountered before. In this way knowledge is organized and expectations from this knowledge allow improved adaptation to newly encountered environmental objects and situations by virtue of their similarity to a known category previously formed (Koenigshofer, 2017).

Another interesting topic is how concepts differ across cultures. As different cultures have different interests and different kinds of interactions with the world, it seems clear that their concepts will somehow reflect those differences. On the other hand, the structure of the physical world also imposes a strong constraint on what kinds of categories are actually useful. The interplay of culture, the environment, and basic cognitive processes in establishing concepts has yet to be fully investigated.

Discussion Questions

• Pick a couple of familiar categories and try to come up with definitions for them. When you evaluate each proposal (a) is it in fact accurate as a definition, and (b) is it a definition that people might actually use in identifying category members?
• For the same categories, can you identify members that seem to be “better” and “worse” members? What about these items makes them typical and atypical?
• Going around the room, point to some common objects (including things people are wearing or brought with them) and identify what the basic-level category is for that item. What are superordinate and subordinate categories for the same items?
• List some features of a common category such as tables. The knowledge view suggests that you know reasons for why these particular features occur together. Can you articulate some of those reasons? Do the same thing for an animal category.
• Choose three common categories: a natural kind, a human artifact, and a social event. Discuss with class members from other countries or cultures whether the corresponding categories in their cultures differ. Can you make a hypothesis about when such categories are likely to differ and when they are not?
• Allen, S. W., & Brooks, L. R. (1991). Specializing the operation of an explicit rule. Journal of Experimental Psychology: General, 120 , 3–19.
• Anglin, J. M. (1977). Word, object, and conceptual developmen t. New York, NY: W. W. Norton.
• Berlin, B. (1992). Ethnobiological classification: Principles of categorization of plants and animals in traditional societies . Princeton, NJ: Princeton University Press.
• Brown, R. (1958). How shall a thing be called? Psychological Review, 65 , 14–21.
• Gelman, S. A. (2003). The essential child: Origins of essentialism in everyday thought . Oxford, UK: Oxford University Press.
• Hampton, J. A. (1979). Polymorphous concepts in semantic memory. Journal of Verbal Learning and Verbal Behavior, 18 , 441–461.
• Hirschfeld, L. A. (1996). Race in the making: Cognition, culture, and the child's construction of human kinds . Cambridge, MA: MIT Press.
• Horton, M. S., & Markman, E. M. (1980). Developmental differences in the acquisition of basic and superordinate categories. Child Development , 51, 708–719.
• Keil, F. C. (1989). Concepts, kinds, and cognitive development . Cambridge, MA: MIT Press.
• Koenigshofer, K. A. (2017). General Intelligence: Adaptation to Evolutionarily Familiar Abstract Relational Invariants, Not to Environmental or Evolutionary Novelty. The Journal of Mind and Behavior , 38(2):119-153.
• Maddox, W. T., & Ashby, F. G. (2004). Dissociating explicit and procedural-based systems of perceptual category learning. Behavioural Processes, 66 , 309–332.
• Mandler, J. M. (2004). The foundations of mind: Origins of conceptual thought . Oxford, UK: Oxford University Press.
• Mareschal, D., Quinn, P. C., & Lea, S. E. G. (Eds.) (2010). The making of human concepts . Oxford, UK: Oxford University Press.
• McCloskey, M. E., & Glucksberg, S. (1978). Natural categories: Well defined or fuzzy sets? Memory & Cognition, 6 , 462–472.
• Medin, D. L., & Ortony, A. (1989). Psychological essentialism. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 179–195). Cambridge, UK: Cambridge University Press.
• Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review , 85, 207–238.
• Mervis, C. B. (1987). Child-basic object categories and early lexical development. In U. Neisser (Ed.), Concepts and conceptual development: Ecological and intellectual factors in categorization (pp. 201–233). Cambridge, UK: Cambridge University Press.
• Murphy, G. L., & Allopenna, P. D. (1994). The locus of knowledge effects in concept learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20 , 904–919.
• Murphy, G. L., & Brownell, H. H. (1985). Category differentiation in object recognition: Typicality constraints on the basic category advantage. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11 , 70–84.
• Norenzayan, A., Smith, E. E., Kim, B. J., & Nisbett, R. E. (2002). Cultural preferences for formal versus intuitive reasoning. Cognitive Science, 26 , 653–684.
• Rosch, E., & Mervis, C. B. (1975). Family resemblance: Studies in the internal structure of categories. Cognitive Psychology , 7, 573–605.
• Rosch, E., Mervis, C. B., Gray, W., Johnson, D., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8 , 382–439.
• Rosch, E., Simpson, C., & Miller, R. S. (1976). Structural bases of typicality effects. Journal of Experimental Psychology: Human Perception and Performance, 2 , 491–502.
• Rosch, E. H. (1973). On the internal structure of perceptual and semantic categories. In T. E. Moore (Ed.), Cognitive development and the acquisition of language (pp. 111–144). New York, NY: Academic Press.
• Smith, E. E., & Medin, D. L. (1981). Categories and concepts . Cambridge, MA: Harvard University Press.
• Smith, J. D., & Minda, J. P. (1998). Prototypes in the mist: The early epochs of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24 , 1411–1436.
• Tanaka, J. W., & Taylor, M. E. (1991). Object categories and expertise: Is the basic level in the eye of the beholder? Cognitive Psychology, 15 , 121–149.
• Wisniewski, E. J., & Murphy, G. L. (1989). Superordinate and basic category names in discourse: A textual analysis. Discourse Processes, 12 , 245–261.

• Gregory Murphy is Professor of Psychology at New York University. He previously taught at the University of Illinois and Brown University. His research focuses on concepts and reasoning, and he is the author of The Big Book of Concepts (MIT Press, 2002).

Creative Commons License

Adapted by Kenneth Koenigshofer, PhD, from Categories and Concepts by Gregory Murphy , licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License . Permissions beyond the scope of this license may be available in our Licensing Agreement .

How to cite this noba module using apa style.

Murphy, G. (2021). Categories and concepts. In R. Biswas-Diener & E. Diener (Eds), Noba textbook series: Psychology. Champaign, IL: DEF publishers. Retrieved from http://noba.to/6vu4cpkt

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A Better Framework for Solving Tough Problems

Start with trust and end with speed.

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When it comes to solving complicated problems, the default for many organizational leaders is to take their time to work through the issues at hand. Unfortunately, that often leads to patchwork solutions or problems not truly getting resolved.

But Anne Morriss offers a different framework. In this episode, she outlines a five-step process for solving any problem and explains why starting with trust and ending with speed is so important for effective change leadership. As she says, “Let’s get into dialogue with the people who are also impacted by the problem before we start running down the path of solving it.”

Morriss is an entrepreneur and leadership coach. She’s also the coauthor of the book, Move Fast and Fix Things: The Trusted Leader’s Guide to Solving Hard Problems .

Key episode topics include: strategy, decision making and problem solving, strategy execution, managing people, collaboration and teams, trustworthiness, organizational culture, change leadership, problem solving, leadership.

HBR On Strategy curates the best case studies and conversations with the world’s top business and management experts, to help you unlock new ways of doing business. New episodes every week.

• Listen to the full HBR IdeaCast episode: How to Solve Tough Problems Better and Faster (2023)
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HANNAH BATES: Welcome to HBR On Strategy , case studies and conversations with the world’s top business and management experts, hand-selected to help you unlock new ways of doing business.

When it comes to solving complicated problems, many leaders only focus on the most apparent issues. Unfortunately that often leads to patchwork or partial solutions. But Anne Morriss offers a different framework that aims to truly tackle big problems by first leaning into trust and then focusing on speed.

Morriss is an entrepreneur and leadership coach. She’s also the co-author of the book, Move Fast and Fix Things: The Trusted Leader’s Guide to Solving Hard Problems . In this episode, she outlines a five-step process for solving any problem. Some, she says, can be solved in a week, while others take much longer. She also explains why starting with trust and ending with speed is so important for effective change leadership.

This episode originally aired on HBR IdeaCast in October 2023. Here it is.

CURT NICKISCH: Welcome to the HBR IdeaCast from Harvard Business Review. I’m Curt Nickisch.

Problems can be intimidating. Sure, some problems are fun to dig into. You roll up your sleeves, you just take care of them; but others, well, they’re complicated. Sometimes it’s hard to wrap your brain around a problem, much less fix it.

And that’s especially true for leaders in organizations where problems are often layered and complex. They sometimes demand technical, financial, or interpersonal knowledge to fix. And whether it’s avoidance on the leaders’ part or just the perception that a problem is systemic or even intractable, problems find a way to endure, to keep going, to keep being a problem that everyone tries to work around or just puts up with.

But today’s guest says that just compounds it and makes the problem harder to fix. Instead, she says, speed and momentum are key to overcoming a problem.

Anne Morriss is an entrepreneur, leadership coach and founder of the Leadership Consortium and with Harvard Business School Professor Francis Frei, she wrote the new book, Move Fast and Fix Things: The Trusted Leaders Guide to Solving Hard Problems . Anne, welcome back to the show.

ANNE MORRISS: Curt, thank you so much for having me.

CURT NICKISCH: So, to generate momentum at an organization, you say that you really need speed and trust. We’ll get into those essential ingredients some more, but why are those two essential?

ANNE MORRISS: Yeah. Well, the essential pattern that we observed was that the most effective change leaders out there were building trust and speed, and it didn’t seem to be a well-known observation. We all know the phrase, “Move fast and break things,” but the people who were really getting it right were moving fast and fixing things, and that was really our jumping off point. So when we dug into the pattern, what we observed was they were building trust first and then speed. This foundation of trust was what allowed them to fix more things and break fewer.

CURT NICKISCH: Trust sounds like a slow thing, right? If you talk about building trust, that is something that takes interactions, it takes communication, it takes experiences. Does that run counter to the speed idea?

ANNE MORRISS: Yeah. Well, this issue of trust is something we’ve been looking at for over a decade. One of the headlines in our research is it’s actually something we’re building and rebuilding and breaking all the time. And so instead of being this precious, almost farbege egg, it’s this thing that is constantly in motion and this thing that we can really impact when we’re deliberate about our choices and have some self-awareness around where it’s breaking down and how it’s breaking down.

CURT NICKISCH: You said break trust in there, which is intriguing, right? That you may have to break trust to build trust. Can you explain that a little?

ANNE MORRISS:  Yeah, well, I’ll clarify. It’s not that you have to break it in order to build it. It’s just that we all do it some of the time. Most of us are trusted most of the time. Most of your listeners I imagine are trusted most of the time, but all of us have a pattern where we break trust or where we don’t build as much as could be possible.

CURT NICKISCH: I want to talk about speed, this other essential ingredient that’s so intriguing, right? Because you think about solving hard problems as something that just takes a lot of time and thinking and coordination and planning and designing. Explain what you mean by it? And also, just  how we maybe approach problems wrong by taking them on too slowly?

ANNE MORRISS: Well, Curt, no one has ever said to us, “I wish I had taken longer and done less.” We hear the opposite all the time, by the way. So what we really set out to do was to create a playbook that anyone can use to take less time to do more of the things that are going to make your teams and organizations stronger.

And the way we set up the book is okay, it’s really a five step process. Speed is the last step. It’s the payoff for the hard work you’re going to do to figure out your problem, build or rebuild trust, expand the team in thoughtful and strategic ways, and then tell a real and compelling story about the change you’re leading.

Only then do you get to go fast, but that’s an essential part of the process, and we find that either people under emphasize it or speed has gotten a bad name in this world of moving fast and breaking things. And part of our mission for sure was to rehabilitate speed’s reputation because it is an essential part of the change leader’s equation. It can be the difference between good intentions and getting anything done at all.

CURT NICKISCH: You know, the fact that nobody ever tells you, “I wish we had done less and taken more time.” I think we all feel that, right? Sometimes we do something and then realize, “Oh, that wasn’t that hard and why did it take me so long to do it? And I wish I’d done this a long time ago.” Is it ever possible to solve a problem too quickly?

ANNE MORRISS: Absolutely. And we see that all the time too. What we push people to do in those scenarios is really take a look at the underlying issue because in most cases, the solution is not to take your foot off the accelerator per se and slow down. The solution is to get into the underlying problem. So if it’s burnout or a strategic disconnect between what you’re building and the marketplace you’re serving, what we find is the anxiety that people attach to speed or the frustration people attach to speed is often misplaced.

CURT NICKISCH: What is a good timeline to think about solving a problem then? Because if we by default take too long or else jump ahead and we don’t fix it right, what’s a good target time to have in your mind for how long solving a problem should take?

ANNE MORRISS: Yeah. Well, we’re playful in the book and talking about the idea that many problems can be solved in a week. We set the book up five chapters. They’re titled Monday, Tuesday, Wednesday, Thursday, Friday, and we’re definitely having fun with that. And yet, if you count the hours in a week, there are a lot of them. Many of our problems, if you were to spend a focused 40 hours of effort on a problem, you’re going to get pretty far.

But our main message is, listen, of course it’s going to depend on the nature of the problem, and you’re going to take weeks and maybe even some cases months to get to the other side. What we don’t want you to do is take years, which tends to be our default timeline for solving hard problems.

CURT NICKISCH: So you say to start with identifying the problem that’s holding you back, seems kind of obvious. But where do companies go right and wrong with this first step of just identifying the problem that’s holding you back?

ANNE MORRISS: And our goal is that all of these are going to feel obvious in retrospect. The problem is we skip over a lot of these steps and this is why we wanted to underline them. So this one is really rooted in our observation and I think the pattern of our species that we tend to be overconfident in the quality of our thoughts, particularly when it comes to diagnosing problems.

And so we want to invite you to start in a very humble and curious place, which tends not to be our default mode when we’re showing up for work. We convince ourselves that we’re being paid for our judgment. That’s exactly what gets reinforced everywhere. And so we tend to counterintuitively, given what we just talked about, we tend to move too quickly through the diagnostic phase.

CURT NICKISCH: “I know what to do, that’s why you hired me.”

ANNE MORRISS: Exactly. “I know what to do. That’s why you hired me. I’ve seen this before. I have a plan. Follow me.” We get rewarded for the expression of confidence and clarity. And so what we’re inviting people to do here is actually pause and really lean into what are the root causes of the problem you’re seeing? What are some alternative explanations? Let’s get into dialogue with the people who are also impacted by the problem before we start running down the path of solving it.

CURT NICKISCH: So what do you recommend for this step, for getting to the root of the problem? What are questions you should ask? What’s the right thought process? What do you do on Monday of the week?

ANNE MORRISS: In our experience of doing this work, people tend to undervalue the power of conversation, particularly with other people in the organization. So we will often advocate putting together a team of problem solvers, make it a temporary team, really pull in people who have a particular perspective on the problem and create the space, make it as psychologically safe as you can for people to really, as Chris Argyris so beautifully articulated, discuss the undiscussable.

And so the conditions for that are going to look different in every organization depending on the problem, but if you can get a space where smart people who have direct experience of a problem are in a room and talking honestly with each other, you can make an extraordinary amount of progress, certainly in a day.

CURT NICKISCH: Yeah, that gets back to the trust piece.

ANNE MORRISS: Definitely.

CURT NICKISCH: How do you like to start that meeting, or how do you like to talk about it? I’m just curious what somebody on that team might hear in that meeting, just to get the sense that it’s psychologically safe, you can discuss the undiscussable and you’re also focusing on the identification part. What’s key to communicate there?

ANNE MORRISS: Yeah. Well, we sometimes encourage people to do a little bit of data gathering before those conversations. So the power of a quick anonymous survey around whatever problem you’re solving, but also be really thoughtful about the questions you’re going to ask in the moment. So a little bit of preparation can go a long way and a little bit of thoughtfulness about the power dynamic. So who’s going to walk in there with license to speak and who’s going to hold back? So being thoughtful about the agenda, about the questions you’re asking about the room, about the facilitation, and then courage is a very infectious emotion.

So if you can early on create the conditions for people to show up bravely in that conversation, then the chance that you’re going to get good information and that you’re going to walk out of that room with new insight in the problem that you didn’t have when you walked in is extraordinarily high.

CURT NICKISCH: Now, in those discussions, you may have people who have different perspectives on what the problem really is. They also bear different costs of addressing the problem or solving it. You talked about the power dynamic, but there’s also an unfairness dynamic of who’s going to actually have to do the work to take care of it, and I wonder how you create a culture in that meeting where it’s the most productive?

ANNE MORRISS: For sure, the burden of work is not going to be equitably distributed around the room. But I would say, Curt, the dynamic that we see most often is that people are deeply relieved that hard problems are being addressed. So it really can create, and more often than not in our experience, it does create this beautiful flywheel of action, creativity, optimism. Often when problems haven’t been addressed, there is a fair amount of anxiety in the organization, frustration, stagnation. And so credible movement towards action and progress is often the best antidote. So even if the plan isn’t super clear yet, if it’s credible, given who’s in the room and their decision rights and mandate, if there’s real momentum coming out of that to make progress, then that tends to be deeply energizing to people.

CURT NICKISCH: I wonder if there’s an organization that you’ve worked with that you could talk about how this rolled out and how this took shape?

ANNE MORRISS: When we started working with Uber, that was wrestling with some very public issues of culture and trust with a range of stakeholders internally, the organization, also external, that work really started with a campaign of listening and really trying to understand where trust was breaking down from the perspective of these stakeholders?

So whether it was female employees or regulators or riders who had safety concerns getting into the car with a stranger. This work, it starts with an honest internal dialogue, but often the problem has threads that go external. And so bringing that same commitment to curiosity and humility and dialogue to anyone who’s impacted by the problem is the fastest way to surface what’s really going on.

CURT NICKISCH: There’s a step in this process that you lay out and that’s communicating powerfully as a leader. So we’ve heard about listening and trust building, but now you’re talking about powerful communication. How do you do this and why is it maybe this step in the process rather than the first thing you do or the last thing you do?

ANNE MORRISS: So in our process, again, it’s the days of the week. On Monday you figured out the problem. Tuesday you really got into the sandbox in figuring out what a good enough plan is for building trust. Wednesday, step three, you made it better. You created an even better plan, bringing in new perspectives. Thursday, this fourth step is the day we’re saying you got to go get buy-in. You got to bring other people along. And again, this is a step where we see people often underinvest in the power and payoff of really executing it well.

CURT NICKISCH: How does that go wrong?

ANNE MORRISS: Yeah, people don’t know the why. Human behavior and the change in human behavior really depends on a strong why. It’s not just a selfish, “What’s in it for me?” Although that’s helpful, but where are we going? I may be invested in a status quo and I need to understand, okay, if you’re going to ask me to change, if you’re going to invite me into this uncomfortable place of doing things differently, why am I here? Help me understand it and articulate the way forward and language that not only I can understand, but also that’s going to be motivating to me.

CURT NICKISCH: And who on my team was part of this process and all that kind of stuff?

ANNE MORRISS: Oh, yeah. I may have some really important questions that may be in the way of my buy-in and commitment to this plan. So certainly creating a space where those questions can be addressed is essential. But what we found is that there is an architecture of a great change story, and it starts with honoring the past, honoring the starting place. Sometimes we’re so excited about the change and animated about the change that what has happened before or what is even happening in the present tense is low on our list of priorities.

Or we want to label it bad, because that’s the way we’ve thought about the change, but really pausing and honoring what came before you and all the reasonable decisions that led up to it, I think can be really helpful to getting people emotionally where you want them to be willing to be guided by you. Going back to Uber, when Dara Khosrowshahi came in.

CURT NICKISCH: This is the new CEO.

ANNE MORRISS: The new CEO.

CURT NICKISCH: Replaced Travis Kalanick, the founder and first CEO, yeah.

ANNE MORRISS: Yeah, and had his first all-hands meeting. One of his key messages, and this is a quote, was that he was going to retain the edge that had made Uber, “A force of nature.” And in that meeting, the crowd went wild because this is also a company that had been beaten up publicly for months and months and months, and it was a really powerful choice. And his predecessor, Travis was in the room, and he also honored Travis’ incredible work and investment in bringing the company to the place where it was.

And I would use words like grace to also describe those choices, but there’s also an incredible strategic value to naming the starting place for everybody in the room because in most cases, most people in that room played a role in getting to that starting place, and you’re acknowledging that.

CURT NICKISCH: You can call it grace. Somebody else might call it diplomatic or strategic. But yeah, I guess like it or not, it’s helpful to call out and honor the complexity of the way things have been done and also the change that’s happening.

ANNE MORRISS: Yeah, and the value. Sometimes honoring the past is also owning what didn’t work or what wasn’t working for stakeholders or segments of the employee team, and we see that around culture change. Sometimes you’ve got to acknowledge that it was not an equitable environment, but whatever the worker, everyone in that room is bringing that pass with them. So again, making it discussable and using it as the jumping off place is where we advise people to start.

Then you’ve earned the right to talk about the change mandate, which we suggest using clear and compelling language about the why. “This is what happened, this is where we are, this is the good and the bad of it, and here’s the case for change.”

And then the last part, which is to describe a rigorous and optimistic way forward. It’s a simple past, present, future arc, which will be familiar to human beings. We love stories as human beings. It’s among the most powerful currency we have to make sense of the world.

CURT NICKISCH: Yeah. Chronological is a pretty powerful order.

ANNE MORRISS: Right. But again, the change leaders we see really get it right, are investing an incredible amount of time into the storytelling part of their job. Ursula Burns, the Head of Xerox is famous for the months and years she spent on the road just telling the story of Xerox’s change, its pivot into services to everyone who would listen, and that was a huge part of her success.

CURT NICKISCH: So Friday or your fifth step, you end with empowering teams and removing roadblocks. That seems obvious, but it’s critical. Can you dig into that a little bit?

ANNE MORRISS: Yeah. Friday is the fun day. Friday’s the release of energy into the system. Again, you’ve now earned the right to go fast. You have a plan, you’re pretty confident it’s going to work. You’ve told the story of change the organization, and now you get to sprint. So this is about really executing with urgency, and it’s about a lot of the tactics of speed is where we focus in the book. So the tactics of empowerment, making tough strategic trade-offs so that your priorities are clear and clearly communicated, creating mechanisms to fast-track progress. At Etsy, CEO Josh Silverman, he labeled these projects ambulances. It’s an unfortunate metaphor, but it’s super memorable. These are the products that get to speed out in front of the other ones because the stakes are high and the clock is sticking.

CURT NICKISCH: You pull over and let it go by.

ANNE MORRISS: Yeah, exactly. And so we have to agree as an organization on how to do something like that. And so we see lots of great examples both in young organizations and big complex biotech companies with lots of regulatory guardrails have still found ways to do this gracefully.

And I think we end with this idea of conflict debt, which is a term we really love. Leanne Davey, who’s a team scholar and researcher, and anyone in a tech company will recognize the idea of tech debt, which is this weight the organization drags around until they resolve it. Conflict debt is a beautiful metaphor because it is this weight that we drag around and slows us down until we decide to clean it up and fix it. The organizations that are really getting speed right have figured out either formally or informally, how to create an environment where conflict and disagreements can be gracefully resolved.

CURT NICKISCH: Well, let’s talk about this speed more, right? Because I think this is one of those places that maybe people go wrong or take too long, and then you lose the awareness of the problem, you lose that urgency. And then that also just makes it less effective, right? It’s not just about getting the problem solved as quickly as possible. It’s also just speed in some ways helps solve the problem.

ANNE MORRISS: Oh, yeah. It really is the difference between imagining the change you want to lead and really being able to bring it to life. Speed is the thing that unlocks your ability to lead change. It needs a foundation, and that’s what Monday through Thursday is all about, steps one through four, but the finish line is executing with urgency, and it’s that urgency that releases the system’s energy, that communicates your priorities, that creates the conditions for your team to make progress.

CURT NICKISCH: Moving fast is something that entrepreneurs and tech companies certainly understand, but there’s also this awareness that with big companies, the bigger the organization, the harder it is to turn the aircraft carrier around, right? Is speed relative when you get at those levels, or do you think this is something that any company should be able to apply equally?

ANNE MORRISS: We think this applies to any company. The culture really lives at the level of team. So we believe you can make a tremendous amount of progress even within your circle of control as a team leader. I want to bring some humility to this and careful of words like universal, but we do think there’s some universal truths here around the value of speed, and then some of the byproducts like keeping fantastic people. Your best people want to solve problems, they want to execute, they want to make progress and speed, and the ability to do that is going to be a variable in their own equation of whether they stay or they go somewhere else where they can have an impact.

CURT NICKISCH: Right. They want to accomplish something before they go or before they retire or finish something out. And if you’re able to just bring more things on the horizon and have it not feel like it’s going to be another two years to do something meaningful.

ANNE MORRISS: People – I mean, they want to make stuff happen and they want to be around the energy and the vitality of making things happen, which again, is also a super infectious phenomenon. One of the most important jobs of a leader, we believe, is to set the metabolic pace of their teams and organizations. And so what we really dig into on Friday is, well, what does that look like to speed something up? What are the tactics of that?

CURT NICKISCH: I wonder if that universal truth, that a body in motion stays in motion applies to organizations, right? If an organization in motion stays in motion, there is something to that.

ANNE MORRISS: Absolutely.

CURT NICKISCH: Do you have a favorite client story to share, just where you saw speed just become a bit of a flywheel or just a positive reinforcement loop for more positive change at the organization?

ANNE MORRISS: Yeah. We work with a fair number of organizations that are on fire. We do a fair amount of firefighting, but we also less dramatically do a lot of fire prevention. So we’re brought into organizations that are working well and want to get better, looking out on the horizon. That work is super gratifying, and there is always a component of, well, how do we speed this up?

What I love about that work is there’s often already a high foundation of trust, and so it’s, well, how do we maintain that foundation but move this flywheel, as you said, even faster? And it’s really energizing because often there’s a lot of pent-up energy that… There’s a lot of loyalty to the organization, but often it’s also frustration and pent-up energy. And so when that gets released, when good people get the opportunity to sprint for the first time in a little while, it’s incredibly energizing, not just for us, but for the whole organization.

CURT NICKISCH: Anne, this is great. I think finding a way to solve problems better but also faster is going to be really helpful. So thanks for coming on the show to talk about it.

ANNE MORRISS:  Oh, Curt, it was such a pleasure. This is my favorite conversation. I’m delighted to have it anytime.

HANNAH BATES: That was entrepreneur, leadership coach, and author Anne Morriss – in conversation with Curt Nickisch on HBR IdeaCast.

We’ll be back next Wednesday with another hand-picked conversation about business strategy from Harvard Business Review. If you found this episode helpful, share it with your friends and colleagues, and follow our show on Apple Podcasts, Spotify, or wherever you get your podcasts. While you’re there, be sure to leave us a review.

When you’re ready for more podcasts, articles, case studies, books, and videos with the world’s top business and management experts, you’ll find it all at HBR.org.

This episode was produced by Mary Dooe, Anne Saini, and me, Hannah Bates. Ian Fox is our editor. Special thanks to Rob Eckhardt, Maureen Hoch, Erica Truxler, Ramsey Khabbaz, Nicole Smith, Anne Bartholomew, and you – our listener. See you next week.

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This article is about strategy.

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Module 7: Thinking and Intelligence

Solving problems, learning objectives.

• Describe problem solving strategies, including algorithms and heuristics
• Explain some common roadblocks to effective problem solving

People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

Problem-Solving Strategies

When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them. For example, a well-known strategy is trial and error . The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Link to Learning

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Everyday Connections: Solving Puzzles

Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below (Figure 1) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

Figure 1. How long did it take you to solve this sudoku puzzle? (You can see the answer at the end of this section.)

Here is another popular type of puzzle that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Figure 2. Did you figure it out? (The answer is at the end of this section.) Once you understand how to crack this puzzle, you won’t forget.

Take a look at the “Puzzling Scales” logic puzzle below (Figure 3). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

Figure 3. The puzzle reads, “Since the scales now balance…and balance when arranged this way, then how many marbles will it require to balance with that top?

Were you able to determine how many marbles are needed to balance the scales in the Puzzling Scales? You need nine. Were you able to solve the other problems above? Here are the answers:

Pitfalls to Problem-Solving

Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.  Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

Check out this Apollo 13 scene where the group of NASA engineers are given the task of overcoming functional fixedness.

Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An anchoring bias occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. This bias proves that first impressions do matter and that we tend to look for information to confirm our initial judgments of others.

Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . To use a common example, would you guess there are more murders or more suicides in America each year? When asked, most people would guess there are more murders. In truth, there are twice as many suicides as there are murders each year. However, murders seem more common because we hear a lot more about murders on an average day. Unless someone we know or someone famous takes their own life, it does not make the news. Murders, on the other hand, we see in the news every day. This leads to the erroneous assumption that the easier it is to think of instances of something, the more often that thing occurs. Watch the following video for an example of the availability heuristic.

Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in Table 2 below.

Test your understanding of heuristics and common biases through this interactive website .

You can also visit this site to see a clever music video explaining these and other cognitive biases.

Think It Over

Which type of bias do you recognize in your own decision making processes? How has this bias affected how you’ve made decisions in the past and how can you use your awareness of it to improve your decisions making skills in the future?

• Modification and adaptation. Provided by : Lumen Learning. License : CC BY: Attribution
• Problem-Solving. Authored by : OpenStax College. Located at : http://cnx.org/contents/[email protected]:Lk3YnvuC@6/Problem-Solving . License : CC BY: Attribution . License Terms : Download for free at http://cnx.org/content/col11629/latest/.
• Can you solve Einsteinu2019s Riddle? . Authored by : Dan Van der Vieren. Provided by : Ted-Ed. Located at : https://www.youtube.com/watch?v=1rDVz_Fb6HQ&index=3&list=PLUmyCeox8XCwB8FrEfDQtQZmCc2qYMS5a . License : Other . License Terms : Standard YouTube License

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Constructive and Unconstructive Repetitive Thought

Edward r. watkins.

University of Exeter

The author reviews research showing that repetitive thought (RT) can have constructive or unconstructive consequences. The main unconstructive consequences of RT are (a) depression, (b) anxiety, and (c) difficulties in physical health. The main constructive consequences of RT are (a) recovery from upsetting and traumatic events, (b) adaptive preparation and anticipatory planning, (c) recovery from depression, and (d) uptake of health-promoting behaviors. Several potential principles accounting for these distinct consequences of RT are identified within this review: (a) the valence of thought content, (b) the intrapersonal and situational context in which RT occurs, and (c) the level of construal (abstract vs. concrete processing) adopted during RT. Of the existing models of RT, it is proposed that an elaborated version of the control theory account provides the best theoretical framework to account for its distinct consequences.

Repetitive, prolonged, and recurrent thought about one's self, one's concerns and one's experiences is a mental process commonly engaged in by all people ( Harvey, Watkins, Mansell, & Shafran, 2004 ). Such thinking bridges many topics within psychology: social cognition, emotion, motivation, self-regulation, goal attainment, stress, psychopathology, and mental health. Examples of such thinking include worry, rumination, perseverative cognition, emotional processing, cognitive processing, mental simulation, rehearsal, reflection, and problem solving (e.g., Martin & Tesser, 1996 ; Mor & Winquist, 2002 ; Papageorgiou & Wells, 2004 ; Wyer, 1996 ). Across these constructs, there is considerable similarity and overlap in theoretical conceptualizations and operational definitions. However, because these constructs have emerged in distinct research domains, they are usually not equated with one another and have rarely been considered together. Moreover, research has shown that these constructs have diverse outcomes, such that repetitive thought (RT) can have both unconstructive and constructive consequences. For example, on one hand, within the cognitive processing literature, RT about symptoms and upsetting events has been conceptualized as necessary for people to come to terms with traumatic and upsetting events ( Horowitz, 1985 ; Pennebaker, 1997 ; Rachman, 1980 ; Tedeschi & Calhoun, 2004 ). On the other hand, RT about symptoms and upsetting events has been found to predict future depression ( Ingram, 1990 ; Nolen-Hoeksema, 1991 , 2000 ; Pyszczynski & Greenberg, 1987 ) and poor recovery from traumatic and upsetting events.

Accounting for the discrepant consequences of RT is critical in understanding the underlying mechanisms of RT and is of obvious applied and clinical value, in terms of improving recovery from traumatic events and reducing vulnerability to anxiety and depression. Nonetheless, there have been few systematized attempts to account for the distinct constructive and unconstructive outcomes of RT (for initial suggestions, see Harvey et al., 2004 ; Martin & Tesser, 1996 ; Nolen-Hoeksema, 2004b ; Segerstrom, Stanton, Alden, & Shortridge, 2003 ). Thus, the first aim of the current article is to address this omission by reviewing and organizing the extensive literature on the distinct consequences of RT in a coherent way. The second aim is to identify principles and/or mechanisms that could explain the distinct consequences of RT. The third aim is to discuss existing models of RT in the light of this review to determine which theory best accounts for the extant literature on RT. I first define the constructs used in this review, including the generic construct repetitive thought , as well as more specific examples and classes of RT considered in this article. I then evaluate the evidence relevant to making a distinction between constructive and unconstructive consequences of RT before summarizing and abstracting the key factors that emerge from this review to account for these distinct consequences of RT. Finally, I examine which of the existing models of RT best accounts for this data.

What Is Meant by RT?

This review focuses on a number of thought processes that that have been highlighted as important in the wider literature relevant to self-regulation, psychopathology, and mental and physical health. A property common to all of these constructs is the process conceptualized by Segerstrom et al. (2003 , p. 909) as “repetitive thought,”, defined as the “process of thinking attentively, repetitively or frequently about one's self and one's world,” which was proposed to form “the core of a number of different models of adjustment and maladjustment.” As the rest of this section makes clear, these different classes of RT encompass a wide range of conceptualizations, associated with both unconstructive and constructive consequences.

Depressive Rumination ( Nolen-Hoeksema, 1991 )

Nolen-Hoeksema defined depressive rumination as “behaviors and thoughts that focus one's attention on one's depressive symptoms and on the implications of these symptoms” ( Nolen-Hoeksema, 1991 , p. 569) and as “passively and repetitively focusing on one's symptoms of distress and the circumstances surrounding these symptoms” ( Nolen-Hoeksema, McBride, & Larson, 1997 ). Nolen-Hoeksema's Response Styles Theory (RST; 1991 , 2000 , 2004a , 2004b ) hypothesized that depressive rumination is a particular response style to depressed mood, which is causally implicated in the onset and maintenance of depression. Depressive rumination is typically assessed on the Response Styles Questionnaire (RSQ; Nolen-Hoeksema & Morrow, 1991 ), which asks participants to endorse how much they ruminate in response to sad or depressed mood (e.g., “When you feel sad, down or depressed how often do you: Think `Why do I always react this way?'”). A related questionnaire is the Rumination on Sadness Scale (RSS; Conway, Csank, Holm, & Blake, 2000 ), which assesses tendency to engage in RT when feeling sad, down, or blue (e.g., “I repeatedly analyze and keep thinking about the reasons for my sadness”).

Rumination ( Martin & Tesser, 1996 )

Rumination was defined as “a class of conscious thoughts that revolve around a common instrumental theme and that recur in the absence of immediate environmental demands requiring the thoughts” ( Martin & Tesser, 1996 , p. 7). Within this conceptualization, rumination is RT on a theme related to personal goals and concerns, which can have either constructive or unconstructive consequences, depending on whether the RT helps or hinders the progress toward the unattained goal that triggered the rumination. It is assessed with the Global Rumination Scale, which measures the extent to which an individual dwells on problems and concerns ( W. D. McIntosh & Martin, 1992 ).

Worry has been defined as “a chain of thoughts and images, negatively affect-laden and relatively uncontrollable” and as “an attempt to engage in mental problem-solving on an issue whose outcome is uncertain but contains the possibility of one or more negative outcomes” ( Borkovec, Robinson, Pruzinsky, & Depree, 1983 , p. 9). Worry typically involves RT about future potential threat, imagined catastrophes, uncertainties, and risks (e.g., “What if they have an accident?”). It is conceptualized as an attempt to avoid negative events, to prepare for the worst, and to problem solve, and it is linked to unconstructive outcomes including increased negative affect, interference with cognitive function, and disruptions to physiological processes ( Borkovec, Ray, & Stober, 1998 ). However, worry is also proposed to serve a number of constructive functions when it is objective, controllable, and brief ( Tallis & Eysenck, 1994 ): (a) an alarm function that interrupts ongoing behavior and directs attention to an issue demanding immediate priority; (b) a prompt function, keeping an individual aware of potential unresolved threats; and (c) a preparation function, motivating an individual to prepare for difficulties and to adopt adaptive behaviors that reduce potential threat. The Penn State Worry Questionnaire (PSWQ; see Davey, 1993 , for a discussion of this and other measures; Meyer, Miller, Metzger, & Borkovec, 1990 ) assesses predisposition to worry (e.g., “I am always worrying about something”).

Perseverative Cognition

Perseverative cognition has been defined as “the repeated or chronic activation of the cognitive representation of one or more psychological stressors” and is hypothesized to be a core feature of worry, rumination, and other forms of RT ( Brosschot, Gerin, & Thayer, 2006 ; Brosschot, Pieper, & Thayer, 2005 ; Pieper & Brosschot, 2005 ). Perseverative cognition is hypothesized to involve repeated cognitive representations of a psychological problem or crisis, which acts to prolong the immediate psychological and physiological responses to such life events and daily stressors such that the body's systems associated with stress (e.g., cardiovascular, hypothalamic–pituitary–adrenal, and immune systems) become chronically activated, leading to the development of disease ( Brosschot et al., 2006 ; A. R. Schwartz et al., 2003 ).

Cognitive and Emotional Processing

Cognitive processing has been defined as the process of actively thinking about a stressor, the thoughts and feelings it evokes, and its implications for one's life and future ( J. E. Bower, Kemeny, Taylor, & Fahey, 1998 ; Greenberg, 1995 ), thus falling within the definition of RT ( Silver, Boone, & Stone, 1983 ). Cognitive processing accounts propose that RT about upsetting events, for example in the form of persistent intrusions about the event, is part of the process of attempting to resolve the discrepancy between stressful events and core beliefs and assumptions ( Greenberg, 1995 ; Horowitz, 1985 ; McCann, Sakheim, & Abrahamson, 1988 ; D. N. Mcintosh, Silver, & Wortman, 1993 ). Such accounts hypothesize that in response to a stressful experience, people think repetitively about their experience in order to work it through, make sense of it, and integrate it into their beliefs and assumptions about the world ( Harber & Pennebaker, 1992 ; Horowitz, 1986 ; Janoff-Bulman, 1992 ; Tait & Silver, 1989 ). Similarly, RT is hypothesized to be a central process in the development of posttraumatic growth, defined as “the experience of significant positive change arising from the struggle with a major life crisis” ( Calhoun, Cann, Tedeschi, & McMillan, 2000 , p. 521; see also Calhoun & Tedeschi, 1998 ; Tedeschi & Calhoun, 2004 ). Tedeschi and Calhoun (2004) proposed that major traumatic events challenge or destroy key aspects of individuals' beliefs and goals, producing emotional distress, which in turn produces RT in order to resolve the distress, leading to personal growth.

Emotional processing has been defined as volitional efforts to acknowledge and understand the significance of one's emotions and is operationalized as persistent focus and analysis of feelings (e.g., “I take time to figure out what I'm really feeling”; Stanton, Danoff-Burg, et al., 2000 ; Stanton, Kirk, Cameron, & Danoff-Burg, 2000 ). Emotional processing has been associated with both constructive outcomes, such as better adjustment, and unconstructive outcomes, such as increased distress.

Planning, Problem Solving, and Mental Simulation

RT can also take the form of cognitive coping strategies, such as anticipatory coping, planning, rehearsal, and problem solving. Problem solving has been conceptualized as involving several stages: definition or appraisal of the problem, generation of alternative solutions, selection of alternatives, implementing the chosen solution, and evaluating its effectiveness ( D'Zurilla & Goldfried, 1971 ), each of which could involve RT. Plan rehearsal involves envisioning the steps or strategies one could use to achieve a desired outcome and often involves repetitive mental rehearsing of future actions and situations. Similarly, mental simulation has been defined as the imaginative and imitative mental construction and representation of some event or series of events ( Taylor, Pham, Rivkin, & Armor, 1998 ; Taylor & Schneider, 1989 ). Repeated mental simulation can be an important process in planning, coping, and self-regulation, via rehearsal of likely future events or by replaying past events ( Pham & Taylor, 1999 ). Mental simulations can also take the form of “painful ruminations that plague many people suffering from depression or reacting to trauma” ( Taylor et al., 1998 , p. 431), for example, an individual repetitively replaying a memory of a car accident.

Counterfactual Thinking

Counterfactual thinking is the generation of imagined mental representations of alternative versions of the past ( Roese, 1997 ; upward if better than what actually happened, e.g., “If only I had studied more, I would have done better”; downward if worse than reality, e.g., “If I had turned left, I would have crashed”). Repeated counterfactual thinking is often prompted by negative affect and in response to difficult events ( Roese & Olson, 1993 ). Upward counterfactuals can have unconstructive consequences, such as exacerbating shame, guilt, anxiety, sadness, and regret ( Mandel, 2003 ; Markman, Gavanski, Sherman, & McMullen, 1993 ; Niedenthal, Tangney, & Gavanski, 1994 ; Sanna, 1997 ), and can have constructive consequences, such as generating inferences about the causes of previous difficulties, guiding effective preparative and preventive behavior ( Mandel & Lehman, 1996 ; Roese, 1997 ).

Defensive Pessimism

Defensive pessimism is characterized by (a) setting low expectations about future outcomes and (b) a “thinking through” process, called reflectivity/reflection, in which individuals extensively reflect on and rehearse possible “worst-case scenarios” of what could go wrong prior to an event and then imagine how these negative outcomes might be prevented ( Cantor & Norem, 1989 ; Norem & Cantor, 1986a , 1986b ; Norem & Chang, 2002 ; Norem & Illingworth, 1993 , 2004 ; Spencer & Norem, 1996 ). Defensive pessimism is conceptualized as strategically serving (a) a self-protective goal of preparing for possible failure and (b) a motivational goal of increasing effort to enhance the possibility of doing well ( Sanna, 1996 , 2000 ; Showers, 1992 ; Showers & Ruben, 1990 ).

Reflection has been defined as chronic self-consciousness that involves playful exploration of novel, unique, or alternative self-perceptions, motivated by curiosity and pleasurable, intrinsic interest in philosophical thinking ( Trapnell & Campbell, 1999 ). The construct of reflection developed as an attempt to explain the “self-absorption paradox,” which reflects the fact that private self-consciousness is positively associated with both increased self-knowledge, which is assumed to facilitate psychological adjustment, and increased psychological distress and psychopathology. Noting that private self-consciousness was correlated with both Neuroticism and Openness to Experience, Trapnell and Campbell (1999) hypothesized that the self-absorption paradox could be explained if there was a neurotically motivated, threat-avoidant form of chronic self-focus, labeled rumination , which contributes to psychopathology, as well as a contrasting form of chronic self-focus, motivated by epistemic curiosity, labeled reflection , which would be associated with increased self-knowledge. The Rumination–Reflection Questionnaire ( Trapnell & Campbell, 1999 ) distinguishes between reflection (e.g., “I love analyzing why I do things”) and rumination, defined as RT about the self prompted by threats, losses, or injustices to the self.

Mind Wandering

Mind wandering has been defined as “a shift of attention from a primary task toward internal information, such as memories” ( Smallwood & Schooler, 2006 , p. 946). Mind wandering can be persistent and repetitive, and as such fits within RT. Mind wandering has unconstructive consequences in terms of reduced attention to external task-related information and interfering with performance on tasks that require substantial controlled processing ( Smallwood, Davies, et al., 2004 ; Teasdale, Dritschel, et al., 1995 ). However, it is hypothesized to facilitate problem solving by repeated working over unresolved current concerns ( Smallwood & Schooler, 2006 ).

Post-Event Rumination

Post-event rumination (also called “post-event processing” and “post-mortem thinking”) has been defined as “repetitive thoughts about subjective experiences during a recent social interaction, including self-appraisals and external evaluations of partners and other details involving the event” ( Kashdan & Roberts, 2007 , p. 286). Post-event rumination is hypothesized to contribute to the development and maintenance of social anxiety ( Clark & Wells, 1995 ; Rapee & Heimberg, 1997 ).

Positive Rumination

Positive rumination has been defined as “the tendency to respond to positive affective states with thoughts about positive self-qualities, positive affective experience, and one's favorable life circumstances that might amplify the positive affect” ( S. L. Johnson, McKenzie, & McMurrich, in press ). Positive rumination is hypothesized to be a process that may contribute to the dysregulation of positive affect in individuals vulnerable to mania and hypomania. The Responses to Positive Affect Questionnaire ( Feldman, Joorman, & Johnson, in press ) assesses how much an individual ruminates in response to positive mood (e.g., “When you feel happy, excited, or enthused how often do you: `Think about how happy you feel'”).

Habitual Negative Self-Thinking

Habitual negative self-thinking is negative self-thinking that has become a mental habit, defined as having “a history of repetition, characterized by a lack of awareness and conscious intent, mentally efficient, and sometimes difficult to control” ( Verplanken, Friborg, Wang, Trafimow, & Woolf, 2007 , p. 526). The Habit Index of Negative Thinking ( Verplanken et al., 2007 ) assesses the self-reported experience of the frequency, awareness, automaticity, and control of negative thinking.

From this brief summary, it is clear that RT is a process common to a number of important constructs in the realms of psychopathology and self-regulation that has been hypothesized to have both constructive and unconstructive consequences. Throughout this article, I will use the construct RT as the generic label to represent the constructs reviewed above, in preference to other labels such as worry and rumination, because RT is (a) more inclusive than other conceptualizations, encompassing the full range of constructs reviewed above; (b) not wedded to a particular theoretical viewpoint, unlike, say, rumination, which is typically associated with RST; (c) less likely to cause confusion than other terms that already have multiple conceptualizations and meanings (e.g., rumination); (d) uncontaminated with prior assumptions as to whether it is constructive or unconstructive, unlike rumination, whose clinical usage typically reflects pathological processes; (e) highly correlated with measures of worry and rumination, which in turn are highly related to each other, suggesting the value of examining more generic conceptualizations of thought process ( Feldman & Hayes, 2005 ; Fresco, Frankel, Mennin, Turk, & Heimberg, 2002 ; Harrington & Blankenship, 2002 ; Hong, 2007 ; Muris, Roelofs, Rassin, Franken, & Mayer, 2005 ; Segerstrom, Tsao, Alden, & Craske, 2000 ; Verplanken et al., 2007 ; Watkins, 2004b ; Watkins, Moulds, & Mackintosh, 2005 ).

Studies Included in the Review

A computerized search using keyword terms was conducted to identify relevant publications for this review. The search, intended to search for studies investigating RT, included the following terms (using wild cards, such as ruminat* for ruminate, rumination, ruminator, ruminative ): repetitive thought, worry, rumination, perseverative cognition, mental simulation, cognitive processing, emotional processing, reflection, problem solving, defensive pessimism, mind wandering , and counterfactual entered into a number of academic databases (e.g., Web of Science—Science Citation Index Extended and Social Science Citation Index, PsycINFO, MEDLINE) from the beginning point of each database through the middle of 2007. The Social Science Citation Index was also searched for references citing seminal articles (e.g., Nolen-Hoeksema, 1991 , 2000 ). In addition, reference lists of the obtained articles as well as numerous review articles and chapters (e.g., Martin & Tesser, 1989 , 1996 ) were reviewed for relevant articles.

Studies were included in this review if they reported either constructive or unconstructive consequences associated with RT. Constructive consequences were defined in terms of beneficial and positive outcomes and products, including (but not limited to) reduced negative affect, increased positive affect, decreases in anxiety and depression, improved physical or mental health, improved performance (e.g., better academic grades and exam results), helpful cognitions and behaviors (e.g., generating plans, active behavioral problem solving, information seeking), and improved cognitive functioning (e.g., improved memory recall, better concentration), with unconstructive consequences defined in terms of the reverse, detrimental and negative outcomes.

Three principal types of studies were considered: (a) cross-sectional designs in which a measure of RT was found to be correlated with a measure of positive or negative outcome; (b) prospective longitudinal designs that assessed extent of RT at an initial assessment point (T1) and examined whether it predicted a dependent variable (e.g., depression) at a later date (T2), typically controlling for the dependent variable at T1; and (c) experimental designs that manipulated degree and/or nature of RT, and measured potential consequences, and, thus, could determine whether RT had a causal effect on the measured dependent variable. The latter two designs were given greater weight in the review because they demonstrate that the dependent variable is a consequence of RT, through indicating either a direct causal role of RT (experimental) or a predictive function for RT antecedent to the dependent variable (longitudinal). Throughout, the review will be organized by type of study, and, where appropriate, by whether the consequences are main effects of RT or are moderated by interactions with other factors. It is worth noting at the outset that the literature on the unconstructive consequences of RT has been better developed than the literature on the constructive consequences of RT.

RT With Unconstructive Consequences

The main findings that emerged from reviewing this literature are that RT is implicated in (a) vulnerability to depression, (b) vulnerability to anxiety, and (c) difficulties in physical health. Table 1 summarizes the relevant articles, reporting the design, sample, measures, and main findings. The section on RT and depression is the largest because of the extensive research on depressive rumination.

RT and Vulnerability to Depression

Cross-sectional studies.

In cross-sectional studies using the RSQ, depressive rumination is found to be (a) elevated in currently depressed patients, formerly depressed patients, and women relative to men ( Riso et al., 2003 ; Roberts, Gilboa, & Gotlib, 1998 ) and (b) associated with depressive symptoms in adults ( Eshun, 2000 ; Ito et al., 2003 ; Lam, Smith, Checkley, Rijsdijk, & Sham, 2003 ; Richmond, Spring, Sommerfeld, & McChargue, 2001 ; see the review by Thomsen, 2006 ), children ( Abela, Vanderbilt, & Rochon, 2004 ; Ziegert & Kistner, 2002 ), and adolescents ( Kuyken, Watkins, Holden, & Cook, 2006 ). Moreover, depressive rumination partially accounts for the 2:1 rates of depression in women relative to men: Once statistically adjusted for, there is no difference between men and women in rates of depression ( Butler & Nolen-Hoeksema, 1994 ; Grant et al., 2004 ; Nolen-Hoeksema, Larson, & Grayson, 1999 ).

Measures of forms of RT other than depressive rumination are also positively and significantly correlated with depression, including a general tendency toward RT (e.g., global rumination scale, Harrington & Blankenship, 2002 ; W. D. McIntosh & Martin, 1992 ; Segerstrom et al., 2000 , Study 1), worry (PSWQ, Meyer et al., 1990 ; Segerstrom et al., 2000 ; or self-rating, Borkovec et al., 1983 ), rumination on sadness ( Conway et al., 2000 ), rumination as operationalized by Trapnell and Campbell (1999) , content-independent perseverative thinking ( Ehring, 2007 ), or RT measured on the Measure of Mental Anticipatory Processes (MMAP; Feldman & Hayes, 2005 ). The MMAP assesses trait disposition to respond with various forms of RT when faced with an “important, difficult and stressful problem” (p. 492), including Stagnant Deliberation (e.g., “Whenever I think about the problem, I often wind up getting stuck”), Problem Analysis (e.g., “I think about why this problem is happening”), Plan Rehearsal (e.g., “I mentally visualize the steps involved in solving the problem”), and Outcome Fantasy (e.g., “I fantasize about it all just going away”) subscales. Both Stagnant Deliberation and Outcome Fantasy were positively correlated with worry (PSWQ), depressive rumination (RSQ), and depression symptoms. Likewise, mind wandering, as measured by thought sampling during a task, is consistently associated with self-reported dysphoria across a wide range of tasks, including word learning ( Smallwood et al., 2003 ; Smallwood, O'Connor, Sudberry, Haskell, & Ballantyne, 2004 ; Smallwood, O'Connor, Sudberry, & Obonsawin, 2007 ), sustained attention ( Smallwood, Davies, et al., 2004 ), and word fragment completion ( Smallwood, O'Connor, & Heim, 2005 ).

Prospective Longitudinal Studies

Main effect of rt.

Prospective longitudinal studies have found that the RSQ predicts (a) the future onset of a major depressive episode across a range of follow-up periods in initially ND individuals ( Just & Alloy, 1997 ; Nolen-Hoeksema, 2000 ; and Spasojevic & Alloy, 2001 , by using the same sample as Just & Alloy, 1997 , found that rumination mediated the effect of other risk factors on onset of depression); (b) depressive symptoms across a range of follow-up periods in initially ND individuals, after controlling for baseline symptoms ( Abela, Brozina, & Haigh, 2002 ; Butler & Nolen-Hoeksema, 1994 ; Hong, 2007 ; Nolen-Hoeksema, 2000 ; Nolen-Hoeksema & Morrow, 1991 ; Nolen-Hoeksema, Parker, & Larson, 1994 ; Nolen-Hoeksema, Stice, Wade, & Bohon, 2007 ; Sakamoto, Kambara, & Tanno, 2001 ; J. A. J. Schwartz & Koenig, 1996 ; J. M. Smith, Alloy, & Abramson, 2006 ); (c) depressive symptoms in patients with clinical depression, after controlling for baseline depression ( Kuehner & Weber, 1999 ; Nolen-Hoeksema, 2000 ; Rohan, Sigmon, & Dorhofer, 2003 ), although one non-replication should be noted (88 college students with recent onset major depressive episode, follow-up after 6 months; reported in both Kasch, Klein, & Lara, 2001 ; Lara, Klein, & Kasch, 2000 ).

It is worth noting one limitation of the RSQ: RSQ items are multidimensional, such that rumination assessed on the RSQ overlaps conceptually with a number of other constructs including depressive symptoms ( Roberts et al., 1998 ; Treynor, Gonzalez, & Nolen-Hoeksema, 2003 ), negative affectivity–neuroticism ( Kasch et al., 2001 ; Watson & Clark, 1984 ), and cognitive reactivity ( Scher, Ingram, & Segal, 2005 ; Segal, Gemar, & Williams, 1999 ; Segal et al., 2006 ; Van der Does, 2002 ), each of which could potentially account for the RSQ predicting prospective depression. However, this concern has been offset by convergent evidence that other measures of RT predict depression. First, other measures of depressive rumination predicted future depressive mood: (a) diary studies in which participants recorded their moods and responses to their moods every day for at least 2 weeks, for both undergraduates ( Nolen-Hoeksema, Morrow, & Fredrickson, 1993 ) and patients with seasonal affective disorder ( Young & Azam, 2003 ); (b) rumination ratings of interview transcripts about a gay male partner's recent death from AIDS ( Nolen-Hoeksema et al., 1997 ); and (c) experience sampling methodology in which momentary ruminative self-focus reported in response to randomly timed beeps on an electronic watch predicted negative affect at the subsequent recording point (on average 1.5 hr later), after controlling for T1 negative affect ( Moberly & Watkins, in press ).

Second, forms of RT other than depressive rumination predict future levels of depression in prospective longitudinal studies including (a) the Rumination to Sadness Scale in depressed patients with 7-month follow-up ( Raes et al., 2006 ); (b) the Emotion Control Questionnaire—Rehearsal subscale with 8-week follow-up ( Rector & Roger, 1996 ); (c) Stagnant Deliberation and Outcome Fantasy subscales on the MMAP predicted depression symptoms 13 weeks later in 1st year law students, after controlling for initial levels of depression ( Feldman & Hayes, 2005 ); (d) habitual negative self-thinking predicted depressive symptoms 9 months later, after controlling for baseline depression, negative life events, and dysfunctional attitudes in 1,102 Norwegian citizens ( Verplanken et al., 2007 ); and (e) with an 8-month follow-up, rumination about negative content predicted future depression and mediated the effects of depressive rumination in predicting depression ( Ito, Takenaka, & Agari, 2005 ; Ito, Takenaka, Tomita, & Agari, 2006 ).

Effect of RT moderated by context

Several studies reported moderating relationships between depressive rumination and intrapersonal variables in predicting future depression. First, within the Temple–Wisconsin Cognitive Vulnerability to Depression project, in which undergraduates selected for high and low risk on negative cognitive style were followed up for 2.5 years, an interaction of negative cognitive style and stress-reactive rumination significantly predicted the rate, number, and duration of major depressive episodes, even after controlling for level of depression at T1 ( Just & Alloy, 1997 ; Robinson & Alloy, 2003 ; for other Cognitive Vulnerability to Depression studies, see J. M. Smith et al., 2006 ; Spasojevic & Alloy, 2001 ). Stress-reactive rumination assessed the tendency to ruminate about negative inferences following stressful events by adapting the RSQ (e.g., “Think about how the stressful event was all your fault,” Robinson & Alloy, 2003 ). Negative cognitive style was assessed by the Dysfunctional Attitudes Scale ( Weissman & Beck, 1978 ), which indexes the endorsement of maladaptive, perfectionistic beliefs about the contingencies necessary to demonstrate self-worth (e.g., “If I do not do well all the time people will not respect me”) and by the Cognitive Style Questionnaire, which assesses attributions about the internality, stability, and globality of events and inferences about the consequences of events for self-worth. Stress-reactive rumination predicted future episodes of major depression in individuals with high levels of negative cognitive style, but not in individuals with low levels of negative cognitive style.

Second, trait depressive rumination, self-esteem, and stressful life events interacted in predicting maintenance of depression over a 6-week period in mildly depressed undergraduates ( Ciesla & Roberts, 2007 ). Depressive rumination predicted depression at follow-up only among participants with both low self-esteem and a high level of stressful life events. Third, depressive rumination interacted with baseline depression symptoms to predict future depression ( Nolan, Roberts, & Gotlib, 1998 ; Roelofs, Muris, Hulbers, Peeters, & Arntz, 2006 ). Moreover, one study found that depressive rumination interacted with stressful life events to predict future depression, indicating that situational context can moderate the effects of rumination ( Morrison & O'Connor, 2005 ). Thus, across these studies, the unconstructive consequences of depressive rumination occurred only in individuals with more negative self-beliefs, more pessimistic attributions, more depressed mood, or negative life events.

Effect of RT moderated by thought content

Factor analyses of the RSQ have identified distinct subtypes of depressive rumination: Brooding versus Reflective Pondering ( Treynor et al., 2003 ), Dwelling on the Negative versus Active Cognitive Appraisal ( Fresco et al., 2002 ), and Symptom-Focused Rumination versus Introspection versus Self-Blame ( Roberts et al., 1998 ). Across these distinctions, the subtypes linked to more unconstructive consequences (Brooding, Dwelling on the Negative, Self-Blame) all share a common theme as reflected in scale items, that is, negative, self-critical, evaluative (e.g., “Why can't I handle things better?”), judgmental, and comparative thinking about the self (e.g., “Why do I have problems other people don't have?”; Nolen-Hoeksema & Morrow, 1991 ). The evidence is strongest for the distinction between Brooding and Reflective Pondering, which was found when the RSQ was factor analyzed once the items referring to symptoms of depression were removed. Brooding is characterized by “moody pondering” ( Treynor et al., 2003 , p. 251), whereas Reflective Pondering is characterized by items such as “Analyze recent events to understand why you are depressed” and was interpreted “as a purposeful turning inward to engage in cognitive problem solving to alleviate one's depressive symptoms” ( Treynor et al., 2003 , p. 256). Brooding measured at T1 predicted both increased concurrent depression and increased future depression assessed 1 year later, even after controlling for depression levels at T1, whereas Reflective Pondering measured at T1 predicted increased concurrent depression but reduced future depression assessed 1 year later ( Treynor et al., 2003 ). In adolescents, Brooding but not Reflective Pondering predicted the development of depressive symptoms over time ( Burwell & Shirk, 2007 ). Furthermore, in patients with major depression, Brooding but not Reflective Pondering was significantly correlated with an attentional bias toward sad facial expressions relative to neutral facial expressions, as assessed on a facial dot-probe task, after controlling for level of depressive symptoms ( Joormann, Dkane, & Gotlib, 2006 ). These results suggest that thought valence and content during RT may moderate its consequences, with the negative, self-critical thinking typical of brooding being more maladaptive.

Limitations

A general limitation of these longitudinal prospective studies is that many studies have not factored prior episodes of the relevant disorder (e.g., prior major depression as opposed to depressive symptoms) into the analyses. As such, the possibility that past major depressive episodes is a common factor linking RT and prospective depression cannot be ruled out. For example, if RT is the result of “scarring” from a previous episode, then this relationship could explain why RT is associated with increased risk for future depression.

Experimental Studies

Studies that experimentally manipulated RT in the form of worry, by asking participants to briefly worry about a self-chosen concern, found that worry increases depressed mood in normal participants ( Andrews & Borkovec, 1988 ; Behar, Zuellig, & Borkovec, 2005 ; Borkovec et al., 1983 ; McLaughlin, Borkovec, & Sibrava, 2007 ; see the review in Borkovec et al., 1998 ) and produces a short-term increase in negative intrusive thoughts, relative to relaxation or visual imagery or no instruction conditions ( Borkovec et al., 1983 ; Wells & Papageorgiou, 1995 ; York, Borkovec, Vasey, & Stern, 1987 ). Experimental studies have also demonstrated that trait predisposition toward RT increases emotional reactivity to negative mood inductions and mood challenges, particularly when participants are provided with a delay period that allows the opportunity to ruminate ( Conway et al., 2000 ; Thomsen, Jorgensen, Mehlsen, & Zachariae, 2004 ).

Effect of RT moderated by intrapersonal context

Moreover, a series of studies provided convergent evidence that RT in the form of depressive rumination plays a causal role in a range of unconstructive outcomes associated with depression, including exacerbating negative affect and increasing negative cognition (for further details, see Table 1 ). These studies used a standardized rumination induction, in which participants are instructed to spend 8 minutes concentrating on a series of sentences that involve rumination about themselves, their current feelings and physical state, and the causes and consequences of their feelings (e.g., “Think about the way you feel inside”; Lyubomirsky & Nolen-Hoeksema, 1995 ; Nolen-Hoeksema & Morrow, 1993 ). As a control condition, a distraction induction is typically used in which participants are instructed to spend 8 minutes concentrating on a series of sentences that involve imagining visual scenes that are unrelated to the self or to current feelings (e.g., “Think about a fire darting round a log in a fire place”).

Compared with the distraction induction, the rumination induction is reliably found to have negative consequences on mood and cognition. Critically, the differential effects of these manipulations are found only when participants are already in a dysphoric mood before the manipulations, indicating a moderating role for intrapersonal context. Under these conditions, compared with distraction, rumination exacerbates negative mood ( Lavender & Watkins, 2004 ; Lyubomirsky & Nolen-Hoeksema, 1995 ; Morrow & Nolen-Hoeksema, 1990 ; Nolen-Hoeksema & Morrow, 1993 ; Watkins & Teasdale, 2001 ), increases negative thinking ( Lyubomirsky & Nolen-Hoeksema, 1995 ), increases negative autobiographical memory recall ( Lyubomirsky, Caldwell, & Nolen-Hoeksema, 1998 ), reduces the specificity of autobiographical memory retrieval ( Kao, Dritschel, & Astell, 2006 ; Park, Goodyer, & Teasdale, 2004 ; Watkins & Teasdale, 2001 ; Watkins, Teasdale, & Williams, 2000 ; see Williams et al., 2007 , for a discussion), increases negative thinking about the future ( Lavender & Watkins, 2004 ), impairs concentration and central executive functioning ( Lyubomirsky, Kasri, & Zehm, 2003 ; Watkins & Brown, 2002 ), impairs controlled memory retrieval ( Hertel, 1998 ), and impairs social problem solving ( Donaldson & Lam, 2004 ; Lyubomirsky & Nolen-Hoeksema, 1995 ; Lyubomirsky, Tucker, Caldwell, & Berg, 1999 ). Likewise, when they ruminated after a negative mood induction, dysphoric individuals recalled more negative memories, whereas non-dysphoric individuals recalled more positive memories ( Joormann & Siemer, 2004 ). This pattern of results has been found for both dysphoric, non-clinical participants and for depressed patients (e.g., Donaldson & Lam, 2004 ; Lavender & Watkins, 2004 ; Park et al., 2004 ; Rimes & Watkins, 2005 ; Watkins & Brown, 2002 ; Watkins & Teasdale, 2001 ), suggesting that the effects generalize to clinical samples.

Extending the role of intrapersonal context, Ciesla and Roberts (2007) found that the effect of trait predisposition toward depressive rumination (RSQ) on subsequent emotional response was moderated by dysfunctional attitudes and self-esteem, such that following a negative mood induction, higher levels of trait rumination were associated with higher levels of dysphoric affect after an 8-minute no-task delay period in participants with low self-esteem or high dysfunctional attitudes but not in participants with high self-esteem or low dysfunctional attitudes. Moreover, self-esteem and dysfunctional attitudes interacted with the rumination versus distraction manipulations after a sad mood induction to predict later levels of dysphoria, such that individuals with lower self-esteem and more dysfunctional attitudes had elevated dysphoric mood, with this effect stronger in the rumination condition than in the distraction condition ( Ciesla & Roberts, 2007 ).

Markman and Miller (2006) further extended the moderating effect of level of depression on the consequences of RT to forms of RT other than depressive rumination. A sample of students with a range of depressive symptoms (non-depressed, ND; mild-to-moderately depressed, MD; severely depressed, SD) generated upward counterfactuals about a recent negative academic outcome ( Markman & Miller, 2006 ). There was a greater reduction in negative evaluation of the event following RT for the ND and MD participants than for the SD participants. Further, MD participants generated a greater proportion of counterfactuals focusing on specific controllable behaviors relative to uncontrollable, enduring qualities of the self than did the ND and SD participants. In turn, the SD participants generated more counterfactuals involving characterological self-blame than did the ND and MD participants. Thus, RT was unconstructive in the SD group but constructive in the MD depressed group.

Effect of RT moderated by concrete versus abstract processing during RT

The effect of trait predisposition toward RT on emotional reactivity is moderated by the thinking style adopted by participants. Increasing trait predisposition toward RT (as assessed on the Action Control Scale—Preoccupation; Kuhl, 1994 ; sample item “When I am in a competition and have lost every time, the thought that I lost keeps running through my mind”) was correlated with slower emotional recovery following a prior failure experience ( Watkins, 2004a ) and greater emotional reactivity to a subsequent failure experience ( Moberly & Watkins, 2006 ), but only in participants manipulated into adopting an abstract, evaluative mindset focused on the causes, meanings, and implications of events. Watkins (2004a) randomly allocated participants to expressive writing about a previously induced failure in either an abstract, evaluative way (e.g., “Why did you feel this way?”) or a concrete, experiential way (e.g., “How did you feel moment-by-moment?”). At higher levels of preoccupation, levels of negative mood 12 hours after the failure were greater, but only in individuals who wrote in the abstract, evaluative way and not in individuals who wrote in the more concrete, experiential way. Moberly and Watkins (2006) trained participants to repetitively think about emotional scenarios, either imagining the concrete details of what is happening in each scenario or evaluating the causes, meanings, and implications of each scenario, prior to an unanticipated failure experience. After the failure experience, higher levels of trait preoccupation were significantly correlated with lower levels of positive affect, but only for participants in the evaluative condition and not for participants in the concrete condition.

A limitation of many experimental studies comparing rumination versus distraction is the lack of a no-intervention control making it impossible to determine whether the distinct consequences are due to active negative effects of rumination and/or active positive effects of distraction. However, selecting an appropriate control condition is difficult in dysphoric participants: A passive control condition that involves “doing nothing” may simply allow naturally occurring rumination to continue (e.g., Hertel, 1998 ), whereas any active control condition may act as a distraction. Nonetheless, a number of other experimental manipulations of RT, for example, of worry, also included a no-intervention control and replicated the finding that RT increased depression, consistent with RT having an active detrimental effect.

Summary of RT and Vulnerability to Depression

This review reveals that there is an extensive body of findings suggesting that RT is involved in the onset and maintenance of depression, with both depressive rumination and a range of other types of RT predicting future depression in longitudinal prospective studies as well as increasing negative affect when experimentally induced. Thus, there is convergent evidence across numerous studies utilizing different populations, different measures (RSQ, interview, self-report), different study designs, and different forms of RT, all of which are consistent with the hypothesis that RT is a process underpinning the onset and development of depression.

RT and Vulnerability to Anxiety

In non-clinical samples, RT is significantly and positively correlated with increased levels of concurrent trait and state anxiety, whether assessed as worry (e.g., Davey, Hampton, Farrell, & Davidson, 1992 ; Meyer et al., 1990 ; Siddique, LaSalle-Ricci, Glass, Arnkoff, & Diaz, 2006 ), Stagnant Deliberation, Outcome Fantasy, Problem Analysis ( Feldman & Hayes, 2005 ), global rumination ( Harrington & Blakenship, 2002 ), rumination about a traumatic event ( Steil & Ehlers, 2000 ), or emotional processing ( Stanton, Danoff-Burg, et al., 2000 ).

Moreover, RT is a key element of a number of anxiety disorders ( Chelminski & Zimmerman, 2003 ; Harvey et al., 2004 ): generalized anxiety disorder, social anxiety, and posttraumatic stress disorder (PTSD). Chronic worry is a central and defining characteristic of generalized anxiety disorder ( American Psychiatric Association, 1994 ; Hoyer, Becker, & Margraf, 2002 ). Within social anxiety, post-event rumination has been identified as an important process: Compared with low-anxious control participants, individuals with high social anxiety and patients with a diagnosis of social anxiety demonstrate significantly more post-event RT following social interactions, performing mental “post-mortems” on how the interaction went and how they performed ( Abbott & Rapee, 2004 ; Edwards, Rapee, & Franklin, 2003 ; Kocovski, Endler, Rector, & Flett, 2005 ; Mellings & Alden, 2000 ; Perini, Abbott, & Rapee, 2006 ; Rachman, Gruter-Andrew, & Shafran, 2000 ; Rapee & Heimberg, 1997 ).

RT has also been implicated as an important process in the development of PTSD. Ehlers and colleagues have conceptualized RT about a traumatic event as a causal mechanism in the development of PTSD. By using brief self-report measures of RT about an identified traumatic event (e.g., “Do you go over and over what happened again and again?”), they have found RT to be elevated in patients with PTSD compared with RT in non-clinical control participants (e.g., Ehlers, Mayou & Bryant, 1998 ). Likewise, in survivors of physical assault, the frequency of counterfactual thoughts was positively correlated with PTSD symptoms such as intrusions about the negative event ( El Leithy, Brown, & Robbins, 2006 ), and for women who had experienced recurrent miscarriage, upward counterfactual thinking was positively correlated with anxiety ( Callander & Brown, 2007 ). Similarly, counterfactual thinking following uncontrollable and traumatic events, such as sudden infant death, is associated with a greater level of distress ( C. G. Davis, Lehman, Wortman, Silver, & Thompson, 1995 ).

In non-clinical samples, RT has been found to predict (a) elevated levels of self-reported anxiety in undergraduates following their midterm exams, after controlling for baseline anxiety ( Sarin, Abela, & Auerbach, 2005 ; Segerstrom et al., 2000 ); (b) prospective increases in anxiety for law students before and after their first semester final exams ( Siddique et al., 2006 ); (c) prospective increases in anxiety over 1 month ( Hong, 2007 ), over 6–8 weeks ( Calmes & Roberts, 2007 ), and over 9 months ( Verplanken et al., 2007 ); and (d) the onset and severity of posttraumatic stress symptoms following traumatic events such as the Lomo Prieta earthquake of 1989 ( Nolen-Hoeksema & Morrow, 1991 ). Furthermore, following traumatic events, RT about the trauma predicts the persistence of PTSD in prospective longitudinal studies from 6 months to 3 years later, for road accidents ( Ehlers, Mayou, & Bryant, 1998 , 2003 ; Holeva, Tarrier, & Wells, 2001 ; Mayou, Bryant, & Ehlers, 2001 ; Mayou, Ehlers, & Bryant, 2002 ; Murray, Ehlers, & Mayou, 2002 ), assaults ( Halligan, Michael, Clark, & Ehlers, 2003 ; Michael, Ehlers, Halligan, & Clark, 2005 ), and in ambulance workers ( Clohessy & Ehlers, 1999 ).

Main effects of RT

In experimental studies, RT has been found to increase anxiety, whether the RT consists of brief periods of worry about self-chosen concerns ( Andrews & Borkovec, 1988 ; Behar et al., 2005 ; Borkovec et al., 1983 ; McLaughlin et al., 2007 ) or a rumination manipulation that exacerbates pre-existing anxious mood ( Blagden & Craske, 1996 ). When university students were asked to describe a distressing event that occurred in the last 2 years and then randomly allocated to rumination (prompts like “Why has this event happened to me?”) or distraction (a word generation task), rumination resulted in a greater increase in negative affect and higher levels of intrusive memories than did distraction ( Ehring, Szeimies, & Schaffrick, 2007 ), suggesting a potential causal role for rumination in the development of posttraumatic symptoms.

Effect of RT moderated by interpersonal and situational context

Kashdan and Roberts (2007) found that there was an interactive effect of intrapersonal and situational context on the consequences of post-event rumination for next-day negative affect following a social situation. Unacquainted undergraduates engaged in 45-minute interactions with randomly paired opposite-sex partners, working through questions structured to induce either personal self-disclosure (e.g., “What is your most treasured memory?”) or to mimic small talk (“What is the best TV show you've seen?”). For individuals with higher levels of trait social anxiety, post-event rumination for the 24 hours post-event was associated with increases in negative affect following personal disclosure but associated with decreases in negative affect following small talk ( Kashdan & Roberts, 2007 ). There was no interaction between rumination and situation in predicting negative affect for individuals with lower levels of social anxiety. Thus, in a situational context that was more personally revealing and, presumably, more meaningful and threatening for individuals high in social anxiety, post-event rumination had more negative consequences.

In an analogue study of posttraumatic stress symptoms, undergraduates watched a distressing film showing the aftermath of motor vehicle accidents, known to induce negative affect and intrusions, and were then randomly allocated to abstract rumination, concrete rumination, or distraction ( Ehring et al., 2007 ). Across time, abstract rumination resulted in slower recovery from negative affect than did concrete rumination or distraction. Moreover, concrete rumination resulted in fewer negative intrusions than did abstract rumination and distraction, which did not differ from each other. Thus, these results suggest that abstract rumination may be particularly unconstructive following exposure to a distressing event.

RT and Impaired Physical Health

Consistent with the perseverative cognition hypothesis ( Brosschot et al., 2006 ), RT correlates with indices of poor physical health and prospectively predicts health-related outcomes.

First, RT is associated with increases in cortisol secretion, which is an index of activation of the hypothalamic–pituitary–adrenal axis, whether assessed as worry ( Schlotz, Hellhammer, Schulz, & Stone, 2004 ) or Rehearsal ( Roger & Najarian, 1998 ). Second, high-trait worry is associated with suppression of the expected increase in natural killer immune cells when experimentally exposed to a fearful situation ( Segerstrom, Glover, Craske, & Fahey, 1999 ) and with reduced natural killer immune cells in response to a naturally occurring trauma ( Segerstrom, Solomon, Kemeny, & Fahey, 1998 ). Third, RT is associated with dysregulated cardiovascular function: Worry is associated with reduced heart rate variability and increased heart rate ( Borkovec & Hu, 1990 ; Borkovec, Lyonfields, Wiser, & Deihl, 1993 ; Brosschot & Thayer, 2003 ; Lyonfields, Borkovec, & Thayer, 1995 ); RT (Rehearsal) is associated with delayed heart rate recovery following a challenging task ( Roger & Jamieson, 1988 ; Roger & Najarian, 1989 ). Reduced heart rate variability is an index of parasympathetic activity and a risk factor for increased mortality, specifically associated with hypertension and cardiovascular disorders ( P. K. Stein & Kleiger, 1999 ). Fourth, high levels of depressive rumination are associated with delay in presenting the symptoms of breast cancer to a healthcare professional ( Lyubomirsky, Kasri, Chang, & Chung, 2006 ), and RT is associated with more physical symptoms in women undergoing a breast cancer prevention trial ( Segerstrom et al., 2003 ). Fifth, RT has also been implicated in the development of insomnia ( Gross & Borkovec, 1982 ; Harvey, 2000 ; Nelson & Harvey, 2002 ). Insomnia is associated with increased pre-sleep worry ( Harvey, 2000 ), and RT is associated with poorer sleep quality and longer time to fall asleep ( Thomsen, Mehlsen, Christensen, & Zachariae, 2003 ).

Increased RT prospectively predicts (a) increased heart disease over a 20-year follow-up doubling the risk for high worriers compared with low worriers ( Kubzansky et al., 1997 ); (b) increased somatic health complaints in high school students, with the use of a controlled worry period reducing subsequent somatic complaints ( Brosschot & van der Doef, 2006 ); (c) higher levels of fatigue over a 10-month follow-up ( Andrea et al., 2004 ); (d) slower recovery and impaired wound healing following surgery for hernias ( E. Broadbent, Petrie, Alley, & Booth, 2003 ); (e) fewer natural killer cells in the months after the Northridge earthquake ( Segerstrom et al., 1998 ); (f) slower clearing of psoriasis in response to psoralen-UV-A photochemotherapy ( Fortune et al., 2003 ); (g) reduced functional status and reduced grip strength 1 year after the diagnosis of rheumatoid arthritis ( Evers, Kraaimaat, Geenen, & Bijlsma, 1998 ); and (h) self-reported physical health problems 1 year later in 20–35-year-olds and increased health care utilization over the subsequent year in 70–85-year-olds ( Thomsen, Mehlsen, Hokland, et al., 2004 , Thomsen, Mehlsen, Olesen, et al., 2004 ).

Consistent with the hypothesis that RT plays a causal role in poor physical health, experimental manipulations of RT have been shown to influence health-related indices. First, experimental induction of rumination about a previous emotionally stressful task results in increased blood pressure (BP) and delayed recovery of BP, whereas distraction facilitates BP recovery ( Glynn, Christenfeld, & Gerin, 2002 ). Second, trait anger rumination predicts prolonged elevated BP after recalling an angry event ( A. R. Schwartz et al., 2000 ) or after an anger provocation ( Suchday, Carter, Ewart, Larkin, & Desiderato, 2004 ). High sustained BP is a risk factor for many diseases including cardiovascular disease and diabetes. Third, compared with distraction, rumination about a mid-session exam resulted in more pre-sleep intrusive thoughts and poorer ratings of sleep quality for high-trait ruminators but not for low-trait ruminators ( Guastella & Moulds, 2007 ). Fourth, Nelson and Harvey (2002) gave patients with insomnia a speech threat just prior to bedtime. Thinking about giving the speech in images produced more initial distress and self-reported arousal but shorter sleep onset latency than did worrying about the speech verbally.

RT With Constructive Consequences

There is also a growing literature indicating how RT can be adaptive, functional, and beneficial, although, as noted earlier, the constructive consequences of RT have been less investigated than the unconstructive consequences of RT. The relevant studies are summarized in Table 2 . The main emergent findings are that RT is implicated in (a) successful cognitive processing and recovery from upsetting and traumatic events, (b) adaptive preparation and planning for the future, (c) recovery from depression, and (d) uptake of health-promoting behaviors.

RT and Successful Cognitive Processing of Stress, Loss, and Trauma

A number of studies have found that, following stressful or traumatic events, RT in the form of cognitive processing is associated with acceptance and recovery. People who actively think about the trauma and its implications are more likely to find meaning or to experience growth than people who do not dwell on the trauma ( J. E. Bower et al., 1998 ; Calhoun et al., 2000 ; Tedeschi & Calhoun, 2004 ; Ullrich & Lutgendorf, 2002 ). Extent of RT after a traumatic or stressful event was positively associated with more posttraumatic growth, as indexed by self-reported increases in relating to others, discovering new possibilities, discovering personal strength, and increased appreciation of life ( Calhoun et al., 2000 ). For example, RT immediately after a child's death was associated with posttraumatic growth in bereaved parents, whereas more recent RT was not, and, in older adults, growth attributed to the struggle with their most stressful events was associated with frequency of rumination across all traumatic events (Calhoun, Tedeschi, Fulmer, & Harlan, 2000; and Tedeschi, Calhoun, & Cooper, 2000; both cited in Tedeschi et al., 2004 ). Similarly, RT, which was defined as recurrent event-related thoughts that help one understand, resolve, and make sense of trauma-related events, was correlated with competency beliefs about ability to handle problems arising from the trauma in children evacuated because of Hurricane Floyd ( Cryder, Kilmer, Tedeschi, & Calhoun, 2006 ).

Effects of RT moderated by thought content

Segerstrom and colleagues (2003) examined the nature of RT and its role in adjustment in women who were exposed to a stressful situation through being identified at high risk for breast cancer. In previous undergraduate studies, ( Segerstrom et al., 2003 , Studies 1 and 2), multidimensional scaling across large samples of structured measures of ruminative thinking and sampled thoughts concerning rumination had revealed that RT could be described on two independent structural dimensions: valence of content (negative vs. positive) and purpose. As thought content became more negative, affect was rated as more negative. The purpose dimension reflected the goals motivating rumination, with two extremes of purpose: searching for new ideas and experiences versus solving problems and improving certainty and predictability. Solving was defined as “trying to narrow down, to make sure, to make plans or to declare knowledge” ( Segerstrom et al., 2003 , p. 916). Examples included causal statements, summary statements, statements of definite consequences, and planning. Searching was defined as “exploring, considering possibilities, or expressing confusion” ( Segerstrom et al., 2003 , p. 916). Examples included expressions of uncertainty, generating options, indecision or confusion, listing multiple possibilities, and learning new perspectives or ways. In the breast cancer study, the valence of thought content during RT predicted concurrent affect and well-being: Less negative content during RT was associated with less negative affect, more positive affect, better overall mental health, less anxiety, and fewer physical symptoms ( Segerstrom et al., 2003 ). Furthermore, there were also interactions between valence and purpose on affect and ratings of physical health: When the valence of RT content was positive, a searching purpose was associated with decreased positive affect and decreased ratings of physical health, but when the valence of thought content was negative, a searching purpose was associated with increased positive affect and increased ratings of physical health. This pattern of results suggests that during RT about negative content, RT with a searching, exploring purpose is associated with more constructive outcomes than is RT with a solving, making-sure purpose.

In a prospective study examining outcomes for HIV-seropositive men who had experienced an AIDS-related bereavement, RT about the bereavement was associated with finding more meaning in the loss over the next 2–3 years, which in turn was associated with better immune responses and reduced AIDs-related mortality over a 7-year follow-up ( J. E. Bower et al., 1998 ). Finding meaning was operationalized as a major shift in values, priorities, or perspectives in response to the loss. RT about bereavement was a necessary although not a sufficient condition for discovery of meaning and improved physical health. Discovery of meaning included the development of new personal growth goals, an enhanced sense of living in the present, and the development of new perspectives, such as “life is precious,” which are consistent with the concept of finding benefit. Finding benefit is defined as considering positive meanings of the traumatic event and positive benefits or value learnt as a result of the event, and it is increasingly hypothesized to be an important contributor to successful cognitive processing of upsetting events ( Affleck & Tennen, 1996 ; King & Miner, 2000 ; Moskowitz, Folkman, Collette, & Vittinghoff, 1996 ). There is growing evidence from prospective longitudinal studies that finding benefit predicts better future psychological adjustment and more adaptive responses to negative life events than does simply trying to understand and make sense of the event ( C. G. Davis, Nolen-Hoeksema, & Larson, 1998 ; N. Stein, Folkman, Trabasso, & Richards, 1997 ; Taylor, Wood, & Lichtman, 1983 ; Tugade & Fredrickson, 2004 ; Tugade, Fredrickson, & Barrett, 2004 ).

Experimental studies of expressive writing, in which repeated writing about distressing events was found to have more beneficial consequences for psychological and physical health than those of repeated writing about a neutral event, have provided broad evidence consistent with a constructive effect for (at least a constrained form of) RT following distress ( Foa, Molnar, & Cashman, 1995 ; Klein & Boals, 2001 ; Pennebaker, 1997 ; Pennebaker, Mayne, & Francis, 1997 ; Pennebaker & Seagal, 1999 ; Sloan & Marx, 2004 ; Smyth, True, & Souto, 2001 ). For example, when undergraduates completed journals for 1 month, those who wrote about cognitions and emotions related to a stressful event had a greater increase in self-reported posttraumatic growth than did those who wrote only about emotions related to a stressful event or who wrote factually about media events ( Ullrich & Lutgendorf, 2002 ). As described earlier, Ehring et al. (2007) found experimental evidence that concrete RT about a distressing film resulted in fewer intrusions about the film compared with abstract RT or distraction.

It is important to acknowledge that this cognitive processing and posttraumatic growth literature has two major limitations: (a) The majority of studies are only cross-sectional, and (b) the principal outcome measures are self-report, leading to questions as to whether reported benefits can be taken at face value or reflect inaccurate, biased, or defensive perceptions ( Nolen-Hoeksema & Davis, 2004 ; Zoellner & Maercker, 2006 ).

RT Contributes to Adaptive Preparation and Anticipatory Planning

There is convergent evidence that RT contributes to anticipatory planning and adaptive self-regulation, consistent with the hypothesis that RT can facilitate preparatory and adaptive behaviors designed to reduce potential threats ( Tallis & Eysenck, 1994 ).

RT is associated with better academic and workplace performance and is correlated with constructive problem solving and creativity. First, worry is associated with better workplace performance but only for more able individuals ( Perkins & Corr, 2005 ). Second, after controlling for trait anxiety, worry is correlated with increased report of active behavioral problem solving and seeking more information in response to a recent stressful event ( Davey et al., 1992 ). Third, diary measures indicate that a large proportion of worry reflects problem-solving attempts, which are often successful ( Szabo & Lovibond, 2004 , 2006 ). Fourth, for survivors of physical assault, upward counterfactual fluency, assessed in terms of the number of different upward counterfactual thoughts generated about the trauma, was correlated with the generation of behavioral plans ( El Leithy et al., 2006 ). Fifth, reflectivity—operationalized as the number of themes and ideas produced when generating actions, outcomes, and consequences for coping plans to hypothetical but common problem situations—is positively correlated with better subsequent academic performance for individuals who preferentially use the defensive pessimism strategy but negatively correlated with academic performance for individuals who preferentially use an optimistic strategy, characterized by high expectations and little reflection prior to a task ( Cantor, Norem, Niedenthal, Langston, & Brower, 1987 ). Sixth, the Reflective Pondering subscale from the RSQ is significantly positively correlated with self-rated creative interests and objectively measured creative fluency, originality, and elaboration ( Verhaeghen, Joormann, & Khan, 2005 ). Unfortunately, Brooding was not assessed, so it is not known whether the relationship between RT and creativity is unique to Reflective Pondering or not.

After controlling for trait anxiety, worry prospectively predicts better academic performance during the 1st year of law school ( Siddique et al., 2006 ). Upward counterfactuals have also been found to produce useful intentions for future behavior and to predict better subsequent performance on anagram tasks and academic courses ( Nasco & Marsh, 1999 ; Roese, 1994 ; Spellman & Mandel, 1999 ).

Effect of RT moderated by thought content and intrapersonal context

On a laboratory arithmetic task, during a lab-based social interaction, or when pursuing their personal goals during an experience sampling methodology study, defensive pessimists performed better (e.g., more arithmetic solutions, talking for longer, more positive ratings by other participant in conversation) and experienced less negative affect and more positive self-relevant thoughts when manipulated to repetitively focus on possible negative outcomes compared with when manipulated to use no reflection or to focus on positive outcomes ( Norem & Illingworth, 1993 ; Showers, 1992 ). In contrast, there was little effect on performance of manipulating reflection in optimists. Similarly, defensive pessimists performed best on a dart-throwing task when they imagined what could go wrong as well as ways to correct these problems and performed significantly worse when they engaged in relaxation imagery or imagined a flawless performance ( Spencer & Norem, 1996 ). Thus RT on negative outcomes was constructive for defensive pessimists but not for optimists.

There is evidence that the focus of attention during repetitive mental simulations influences the effectiveness of planning and self-regulation ( Taylor et al., 1998 ; Taylor & Schneider, 1989 ). For example, students who repeatedly imagined the process of how to take steps toward obtaining a high exam grade studied more and obtained better grades than did students who repeatedly imagined the outcome of obtaining a high grade or students who simply monitored their studying with no mental simulation ( Pham & Taylor, 1999 ; Taylor et al., 1998 ). This effect of process simulation versus outcome simulation on exam performance was mediated by a reduction in anxiety and by increases in planning. Similarly, repeated imagining of an ongoing stressful event, how it happened, and its associated emotions produced more positive affect and greater report of active coping after 1 week than did imagining having resolved the situation or not imagining the event at all ( Rivkin & Taylor, 1999 ). Likewise, process simulations help to reduce the planning fallacy, in which participants tend to underestimate the time taken to complete tasks ( Taylor et al., 1998 ). Similarly, prompting RT focused on causal attributions and abstract evaluations (using a set of questions such as “Why did this problem happen?”) impaired social problem solving in a recovered depressed group, who performed as well as never-depressed participants in a no-prompt control condition, whereas prompting RT focused on the concrete process of how to proceed (using a set of questions such as “How are you deciding what to do next?”) ameliorated the problem-solving deficit normally found in a group of currently depressed patients ( Watkins & Baracaia, 2002 ). Again, RT focused on planning, induced by working through a list of the concrete (who, what) steps necessary to plan a charity fundraiser, resulted in less dysphoric mood, better concentration, and more efficient performance on a subsequent reading task than did the standard rumination manipulation in dysphoric participants ( Lyubomirsky et al., 2003 , Study 1).

RT Predicts Recovery From Depression

RT prospectively predicts reduced levels of depression, whether in (a) currently depressed patients receiving pharmacotherapy ( Yamada, Nagayama, Tsutiyama, Kitamura, & Furukawa, 2003 , RT = rating of extent “absorbed in thought about the dysphoric mood itself, its cause, and possible results when feeling down or depressed”), (b) a community sample ( Treynor et al., 2003 , Reflective Pondering on RSQ), and (c) 1st year law students ( Feldman & Hayes, 2005 ; Plan Rehearsal).

Depressive rumination interacted with self-esteem and dysfunctional attitudes in predicting response to a group psychoeducational treatment for patients with major or minor depression ( Ciesla & Roberts, 2002 ). In participants with low self-esteem or high dysfunctional attitudes, increased trait rumination was associated with worse treatment outcomes, whereas for participants with moderate levels of self-esteem or low levels of dysfunctional attitudes, increased trait rumination predicted lower levels of depression symptoms post-treatment, even when controlling for symptoms pre-treatment.

There is evidence from experimental studies suggesting that RT can have constructive consequences on aspects of cognition implicated in the onset and maintenance of depression. A series of studies have adapted the standardized rumination induction ( Nolen-Hoeksema & Morrow, 1993 ). Importantly, all variants retain the key elements of the original rumination manipulation, namely, repetitive focus on self, symptoms, and mood, but with instructions to adopt different styles of processing when focusing on the self. Thus, in depressed patients, a rumination induction encouraging more concrete, experiential processing, in which participants were instructed to “focus attention on the experience of” feelings, mood, and symptoms, was compared with a rumination induction encouraging more abstract and evaluative processing, in which participants were instructed to “think about the causes, meanings, and consequences” of feelings, mood, and symptoms ( Watkins & Teasdale, 2004 , p. 3; Watkins & Teasdale, 2001 ). Compared with abstract, evaluative rumination, experiential rumination reduced negative global self-judgments such as “I am worthless” ( Rimes & Watkins, 2005 ), improved social problem solving ( Watkins & Moulds, 2005a ), and increased specificity of autobiographical memory recall ( Watkins & Teasdale, 2001 , 2004 ). These cognitive processes are implicated in the onset and maintenance of depression ( Williams et al., 2007 ). These findings suggest that RT focused on the direct experience of moods and feelings reduces patterns of cognitive processing implicated in increased vulnerability for depression relative to RT focused on the causes, meanings, and consequences of moods and feelings. It is important to note that both variants of rumination involved focus on negative content: Both repetitively focused attention on the feelings and symptoms of patients with current depression. 1

RT Contributes to the Uptake of Health-Promoting Behaviors

There is some preliminary evidence that RT is implicated in health-promoting behaviors. First, increased worry about physical health predicted prospective attempts to quit smoking in smokers over the following 8 months ( Dijkstra & Brosschot, 2003 ). High worry was especially associated with a quit attempt in smokers with both high self-efficacy and beliefs that denied or rationalized away the risks associated with smoking. However, in ex-smokers with low self-efficacy and high denial beliefs, worry predicted a relapse back into smoking. Second, in a meta-analysis of 12 prospective studies that measured worry about breast cancer at baseline and subsequent breast self-examination or utilization of mammography, a small but reliable positive association was found between worry about breast cancer and screening behavior, with increased worry associated with greater probability of undertaking screening ( Hay, McCaul, & Magnan, 2006 ).

Properties of Constructive and Unconstructive RT

Reviewing the extant literature, it therefore appears that RT can be both helpful and unhelpful. It is important to acknowledge that sometimes RT has predominantly either constructive or unconstructive outcomes but that at other times RT may simultaneously have both constructive and unconstructive outcomes; for example, posttraumatic growth can occur alongside increased distress ( Tedeschi & Calhoun, 2004 ). What then determines whether RT has constructive consequences and/or unconstructive consequences? Examining the literature reviewed, a number of properties emerge that potentially account for the distinct consequences of RT. These properties reflect both structural aspects of RT, such as the valence of thought content during RT, and process aspects, such as the level of construal (concrete vs. abstract processing) adopted during RT. 2

Unsurprisingly, valence is important in determining the consequences of RT, both in terms of thought content (positive vs. negative) and the cognitive–affective systems of the individual engaged in RT (e.g., positive vs. negative mood; optimism vs. pessimism). For example, RT about the acceptance of an article that has had much work invested in it will have a very different and more positive affective quality than RT about the same article if it was rejected.

There is considerable evidence that the valence of thought content is a major factor in determining whether RT is helpful or unhelpful. First, Segerstrom et al.'s (2003) structural analysis of RT identified the valence of thought content as an important dimension within RT, with more negative content associated with worse overall mental health, more anxiety, and more physical symptoms. Second, Martin and Tesser (1996) identified that rumination contains several subclasses or modes, including RT about positive content or about negative content. Third, in a large meta-analysis of the self-focus literature, attention to negative aspects of the self was strongly related to increased levels of negative affect, whereas attention to positive aspects of the self was related to lower levels of negative affect ( Mor & Winquist, 2002 ). Moreover, depressive rumination was more strongly related to negative affect than was nonruminative self-focus. Thus, RT focused on negative aspects of the self would have more negative consequences than RT focused on positive aspects of the self. Fourth, depressive rumination, the form of RT most convincingly implicated in causing unconstructive consequences, is conceptualized in terms of response to negative mood, and indexed by a measure (RSQ) that explicitly focuses on negative content, with items characterized by thinking about feelings and symptoms when feeling sad, down, and depressed.

Fifth, the result that “finding benefit” during RT has more constructive consequences (e.g., J. E. Bower et al., 1998 ) is consistent with the valence of thought content influencing outcomes: Finding benefit involves a focus on positive content when repetitively thinking about the difficult or traumatic event. Consistent with this, the measure of RT used in the posttraumatic growth literature includes items that focus on positive gains (e.g., “I try to think of some good things that happened to me after the flooding”; Calhoun et al., 2000 ; Cryder et al., 2006 ). Sixth, the more pathological consequences found for Brooding could be a result of its particularly negative thought content, focused on self-evaluative analysis and self-critical judgment ( Treynor et al., 2003 ). A number of commentators have suggested that brooding is characterized by self-evaluative, self-critical, and self-judgmental analysis, consistent with more negative valence ( Gortner, Rude, & Pennebaker, 2006 ; Joormann et al., 2006 ; Mathews, 2006 ; Treynor et al., 2003 ; Watkins & Moulds, 2005a ; Watkins & Teasdale, 2004 ). Seventh, when the items of the RSQ were altered to de-emphasize evaluative, self-critical judgments, this Non-Judging Reflection scale was uncorrelated with depression symptoms, unlike the standard Reflection scale which was significantly correlated with depression. Thus, changing the negative judgmental quality of these items reduced their relationship to depression ( Rude, Maestas, & Neff, 2007 ). Eighth, while rumination about negative content predicted depression in an 8-month longitudinal study, rumination about depression was no longer a significant predictor of depression after controlling for negative rumination ( Ito et al., 2006 ). Thus, the effects of rumination appear to depend on whether it is focused on negative or non-negative content.

Ninth, the consequences of problem solving are known to depend on the valence of the problem orientation adopted. A positive orientation encompassing confidence in one's ability to solve the problem is associated with better outcomes than is a negative orientation characterized by reduced self-confidence, reduced optimism, and more extreme views of the severity and intractability of the problem ( Belzer, D'Zurilla, & Maydeu-Olivares, 2002 ; D'Zurilla, Chang, Nottingham, & Faccini, 1998 ; D'Zurilla & Nezu, 1990 , 1999 ; Elliott, Sherwin, Harkins, & Marmarosh, 1995 ; Maydeu-Olivares & D'Zurilla, 1996 ; Shewchuk, Johnson, & Elliott, 2000 ). Thus, the valence of thought content during RT appears to be a key determinant of whether RT has constructive or unconstructive consequences.

One mechanism by which valence may moderate the consequences of RT is by determining the direction of action for the magnifying effects of RT on mood and cognition. It has been hypothesized that RT exacerbates the pre-existing mood state and amplifies the reciprocal relationships between existing cognition and mood ( Ciesla & Roberts, 2007 ; Nolen-Hoeksema, 1991 ). It is argued that repetitive focus on affect and cognition serves to make them more salient and, to further elaborate, to consolidate and strengthen them. Consistent with this RT amplification hypothesis, (a) a considerable body of research has indicated that self-focus amplifies the effect of negative mood on thinking ( Ingram, 1990 ; Ingram & Smith, 1984 ; Pyszczynski & Greenberg, 1987 ) and of negative thoughts on mood ( Mor & Winquist, 2002 ); (b) depressive rumination is more strongly related to negative affect than is nonruminative self-focus, indicating additional effects of RT ( Mor & Winquist, 2002 ); (c) compared with distraction, rumination exacerbates pre-existing anxious mood ( Blagden & Craske, 1996 ), pre-existing anger ( Rusting & Nolen-Hoeksema, 1998 ), and increases anger in response to a provocation ( Bushman, 2002 ; Bushman, Bonacci, Pedersen, Vasquez, & Miller, 2005 ). Thus, for negatively valenced cognitions, RT would amplify the negative consequences of these negative cognitions and exacerbate existing negative mood, resulting in more unconstructive outcomes.

With this amplification hypothesis in mind, it is worth noting that, while in the majority of cases more negative valence during RT will be associated with more unconstructive consequences, positive valence during RT could possibly lead to unconstructive consequences in individuals vulnerable to hypomania and mania. Recent theories of bipolar disorder have hypothesized that repeated dwelling on positive affect could amplify positive mood and associated behavioral activation, fuelling the spiral of mood and cognition up into hypomania ( S. L. Johnson et al., in press ). Consistent with this hypothesis, compared with control participants with no history of mood disorders and individuals with major depression, individuals diagnosed with bipolar disorder endorsed elevated emotion-focused rumination in response to positive affect. Moreover, positive rumination was associated with hypomanic symptoms ( S. L. Johnson et al., in press ). Although preliminary, these findings suggest a link between excessive positive rumination and bipolar disorder: Future research will need to examine its causal relationship with mania symptoms.

Intrapersonal and Situational Context in Which RT Occurs: Valence and Ability

The context in which repetitive thinking occurs is also an important determinant of the consequences of RT. Key elements of context are (a) the prevailing valence of the cognitive–affective system of the individual engaged in RT, in terms of mood state, self-beliefs, and dispositional traits; and (b) the situation and environment in which RT occurs. Both contexts can range from negatively valenced (e.g., intrapersonal: dysphoric mood, negative expectations, low self-esteem; situational: stressful, traumatic events) to positively valenced (intrapersonal: positive mood, positive expectations, high self-esteem; situational: successful, rewarding events) and both will often determine the valence of thought content during RT. For example, when an individual has low self-esteem or is in a dysphoric mood, negative thoughts, memories and expectations become more easily accessible and available, as illustrated by the phenomenon of mood-congruent memory ( G. H. Bower, 1981 ; Teasdale, 1983 , 1988 ). Similarly, a negative, stressful environment will activate negative thoughts and increase the likelihood of negative mood. Thus, by extension, in the context of a negative valenced intrapersonal or situational context, RT about this negative context (which is itself negatively valenced) would further amplify the effect of that context on mood and cognition.

There is good evidence that the prevailing valence of an individuals' cognitive–affective system determines whether RT is helpful or unhelpful. First, there is extensive evidence that dysphoric mood and/or depressed symptoms is a setting condition for depressive rumination to produce unconstructive consequences: (a) The experimental literature repeatedly has found that there is no maladaptive effect on mood and cognition of manipulating rumination compared with distraction in individuals who are not already in a dysphoric mood (e.g., see the review by Nolen-Hoeksema, 2004b ); (b) the effects of ruminative style on delay in presenting symptoms of breast cancer to a healthcare professional was moderated in part by the experience of positive mood at the time of symptom discovery ( Lyubomirsky et al., 2006 ); (c) rumination exacerbated the predictive effects of baseline depression on depression 6 months later but did not predict prospective depression in the absence of depression ( Roelofs et al., 2006 ). Second, in a similar way, there is evidence that the consequences of worry are moderated by the levels of trait anxiety: Worry is associated with more active coping and greater information seeking ( Davey et al., 1992 ) and predicts better prospective performance ( Siddique et al., 2006 ) once levels of associated trait anxiety are held constant, suggesting that worry may be more constructive when levels of anxiety are low but becomes more problematic as trait anxiety increases. Trait anxiety is associated with poor problem-solving confidence ( Davey et al., 1992 ), which in turn is implicated in the content of worrying becoming more negative and more catastrophic, resulting in less constructive consequences ( Davey, Jubb, & Cameron, 1996 ). Davey et al. (1992 , p. 145) hypothesized that “pathological worrying is generated by a problem-focused cognitive style being thwarted by a lack of confidence in the solutions being generated.” Thus, an intrapersonal context characterized by ongoing negative affect, whether depressed mood or trait anxiety, will lead to more negative content during RT, and, thereby, more unconstructive consequences.

Third, a number of studies find that the ability of RT to predict depression is moderated by the degree of negative self-related beliefs, with dysfunctional attitudes and self-esteem moderating the extent to which rumination prospectively predicts (a) the onset of depressive episodes ( Robinson & Alloy, 2003 ) and (b) worse treatment outcome ( Ciesla & Roberts, 2002 ). Likewise, the effects of experimentally manipulating rumination were moderated by the negative self-related beliefs held by individuals ( Ciesla & Roberts, 2007 ). Similarly, the effects of worry on smoking behavior are moderated by levels of self-efficacy ( Dijkstra & Brosschot, 2003 ). Thus, there is good evidence to suggest that negative representations of the self and maladaptive beliefs about what is required to be a worthwhile person moderate whether RT is constructive or unconstructive. In the absence of dysphoric mood or negative self-beliefs, RT focused on the self need not be negative; however, in the presence of negative mood or negative self-beliefs, RT focused on the self is likely to involve negative content. As suggested by Ciesla and Roberts (2002 , p. 447) “the process of turning's one attention inward may be particularly caustic if one's thoughts are dominated by self-deprecating and perfectionistic cognitions.”

Similarly, there is good evidence that situational context can influence the effects of RT. First, Morrison and O'Connor (2005) found that depressive rumination interacted with reported stress to predict social dysfunction 6 months later. Second, trait rumination was predictive of depression at 6-week follow-up only among initially mildly depressed undergraduates who had both low self-esteem and a high level of stressful life events ( Ciesla & Roberts, 2007 ). Third, for individuals with higher levels of social anxiety, but not for individuals with low levels of social anxiety, post-event rumination was associated with increases in negative affect following personal disclosure but was associated with decreases in negative affect following small talk ( Kashdan & Roberts, 2007 ).

Another aspect of context that influences the consequences of RT is an individual's ability and expertise. Greater competence, ability, practice, and expertise in the domain of concern are hypothesized to produce more constructive outcomes during RT. First, the defensive pessimism literature has found that RT is associated with constructive outcomes when RT is congruent with an individuals' preferred strategy, such that defensive pessimists find RT focused on negative outcomes an adaptive strategy but optimists do not. Moreover, studies of defensive pessimism have explicitly selected participants on the basis of a history of success in the studied domain, whether academia or social interactions (e.g., grade point averages > 3.0 and reporting generally performing well in the past, Norem & Cantor, 1986a , 1986b ), such that, by definition, all defensive pessimists have been successful in the domain under study. Thus, the benefit of RT for defensive pessimists occurs within the context of a reasonably high level of experience and ability. Second, in a sample of financial sector managers, worry is correlated with better workplace performance for more able individuals, but worry is correlated with worse workplace performance for less able individuals, indicating the value of ability in moderating the role of RT ( Perkins & Corr, 2005 ). Third, the more constructive consequences of RT for individuals with high self-esteem and high self-efficacy, may, in part, reflect greater objective ability as well as more positive subjective perceptions of the self. Fourth, RT about the traits necessary to be a good tennis player was negatively correlated with the quality of play in inexperienced players but not in experienced players, suggesting that RT has less unconstructive consequences for those with more expertise in the relevant domain ( Wicklund & Braun, 1987 ). Thus, there is some evidence that personal ability and expertise may influence the consequences of RT.

Level of Construal Adopted During RT

While valence is a major factor in determining the consequences of RT, it cannot explain all observed findings. In particular, RT focused on negative content has been found to have constructive consequences in studies of depressive rumination ( Rimes & Watkins, 2005 ; Watkins & Moulds, 2005a ; Watkins & Teasdale, 2001 ; 2004 ) and of defensive pessimism ( Cantor & Norem, 1989 ; Norem & Cantor, 1986a , 1986b ; Norem & Chang, 2002 ; Spencer & Norem, 1996 ). Moreover, simply focusing on positive outcomes in and of itself is not necessarily the most adaptive form of RT, as revealed by the comparison of process versus outcome simulations ( Pham & Taylor, 1999 ; Rivkin & Taylor, 1999 ; Taylor et al., 1998 ; Taylor & Schneider, 1989 ). It is hypothesized that another property that can account for whether RT has constructive or unconstructive consequences is the level of construal during RT. Research on mental representation in the cognitive and social–cognitive literatures makes a distinction between higher level, abstract construals versus lower level, concrete construals (e.g., Dweck & Leggett, 1988 ; Freitas, Gollwitzer, & Trope, 2004 ; Freitas, Salovey, & Liberman, 2001 ; Liberman, Sagristano, & Trope, 2002 ; Markman & McMullen, 2003 ; Mischel & Shoda, 1995 ; Trope, 1989 ; Trope & Liberman, 2003 ). High-level construals are abstract, general, superordinate, and decontextualized mental representations that convey the essential gist and meaning of events and actions, whereas low-level construals are more concrete mental representations that include subordinate, contextual, specific, and incidental details of events and actions. 3 High-level abstract construals are focused on the desirability and importance of outcomes, whereas low-level concrete construals are focused on the feasibility and planning of outcomes. Thus, different levels of construal can be adopted when perceiving one's own and other's behavior: Inferences of global traits that are invariant across different situations (e.g., laziness) constitute relatively high-level, abstract construals of behavior, whereas inferences of situation-specific states (e.g., tiredness), constitute relatively low-level concrete construals of behavior ( Nussbaum, Trope, & Liberman, 2003 ). Similarly, actions, events, and goals can be represented in terms of high-level or low-level construals: Representations of the abstract “why” aspects of an action and of the ends consequential to an action constitute relatively high-level construals, whereas representations of the specific “how” details of the action and of the means to the end constitute relatively low-level construals ( Freitas et al., 2004 ; Trope & Liberman, 2003 ; Vallacher & Wegner, 1987 ).

Across this review, there is evidence that RT characterized by high-level, more abstract construals has more unconstructive consequences relative to RT characterized by low-level, more concrete construals, at least when RT is focused on negatively valenced content (to date, the majority of studies relevant to level of construal in RT have involved negatively valenced RT). First, within experimental studies that manipulate RT, one experimental condition is often characterized by lower level construals that focus on contextual details and the means to desired ends (e.g., experiential rumination, Rimes & Watkins, 2005 ; Watkins & Moulds, 2005a ; Watkins & Teasdale, 2001 , 2004 ; simulation of the process of how to achieve a goal, Taylor et al., 1998 ; mindsets involving imagining how things unfold or how to proceed, Moberly & Watkins, 2006 ; Watkins, 2004a ; Watkins & Baracaia, 2002 ), whereas the other condition is characterized by higher level construals that focus on meanings and implications (e.g., analytical rumination, Ehring et al., 2007 ; Rimes & Watkins, 2005 ; Watkins & Moulds, 2005a ; Watkins & Teasdale, 2001 , 2004 ; outcome simulation, Taylor et al., 1998 ; mindsets involving thinking about causes, meanings, consequences, Moberly & Watkins, 2006 ; Watkins, 2004a ; Watkins & Baracaia, 2002 ). For example, because representations of desired ends and outcomes sought by an action constitute relatively high-level construals, whereas representations of the specific “how” details of the action and of the means to the end constitute relatively low-level construals, process simulations involve relatively lower level construals than do outcome simulations. Critically, the manipulations of RT involving lower level construals produce more constructive consequences than the manipulations of RT involving higher level construals, including better social problem solving, more specific autobiographical memory, less global negative self-judgments ( Rimes & Watkins, 2005 ; Watkins & Baracaia, 2002 ; Watkins & Moulds, 2005a ; Watkins & Teasdale, 2001 , 2004 ), improved self-regulation and academic performance ( Pham & Taylor, 1999 ; Rivkin & Taylor, 1999 ; Taylor et al., 1998 ; Taylor & Schneider, 1989 ), better emotional recovery from prior failure ( Watkins, 2004a ), and upsetting images ( Ehring et al., 2007 ), and reduced emotional vulnerability to subsequent failure ( Moberly & Watkins, 2006 ). Importantly, these manipulations of RT are often matched for degree of negative thought content, such that the distinct functional consequences cannot be due to differences in valence of thought content.

Second, the form of anticipatory RT within the MMAP focused on low-level construals (Plan Rehearsal) was negatively correlated with depression both concurrently and prospectively, whereas the form of anticipatory RT focused on higher level construals (Problem Analysis) was associated with increased anxiety ( Feldman & Hayes, 2005 ). Third, the current construal-level analysis subsumes the reduced concreteness theory of worry, which proposes that worry is predominantly experienced in a more abstract–verbal form rather than in a more concrete–visual imagery form and that this reduced concreteness leads to negative consequences for problem solving and affect regulation ( Borkovec et al., 1998 ; Stober, 1998 ; Stober & Borkovec, 2002 ; Stober, Tepperwien, & Staak, 2000 ). Consistent with this theory, worry seems to be predominantly experienced in a verbal form rather than in images ( Borkovec & Inz, 1990 ; Borkovec et al., 1993 , 1998 ; Borkovec et al., 1983 ; Freeston, Dugas, & Ladouceur, 1996 ; McLaughlin et al., 2007 ). Moreover, elaborations of problems about which participants worry are independently and blindly rated as more abstract and less concrete than those of problems about which participants do not worry ( Borkovec et al., 1998 ; Stöber, 1998 ; Stöber & Borkovec, 2002 ). Within reduced concreteness theory, concrete thought is defined as “distinct, situationally specific, unequivocal, clear, singular” and abstract thought as “indistinct, cross-situational, equivocal, unclear, aggregated” ( Stöber & Borkovec, 2002 , p. 92), which fits within the existing conceptualization of low-level versus high-level construals. Furthermore, reduced concreteness has been found during RT in currently depressed patients ( Cribb, Moulds, & Carter, 2006 ; Watkins & Moulds, 2007 ) and during rumination in undergraduates ( McLaughlin et al., 2007 ), indicating that this analysis applies to other forms of RT than worry.

Fourth, there is indirect evidence that level of construal could contribute to the beneficial effects of defensive pessimism. Defensive pessimists appear to have a strategy of viewing negative futures as temporally close, and this strategy predicts improved task performance, through the mediator of increased preparation ( Sanna, Chang, Carter, & Small, 2006 ). Temporal construal theory proposes that thinking about distant futures involves more high-level construals, whereas thinking about close futures involves more low-level construals ( Trope & Liberman, 2003 ). Lower level construals would in turn lead to more specific preparation for an upcoming task.

One mechanism by which the level of construal may influence the consequences of RT is by influencing the efficacy of problem solving. Both the reduced concreteness theory ( Stöber & Borkovec, 2002 ) and the action identification theory ( Vallacher & Wegner, 1987 ) hypothesize that processing at a lower level of construal provides more elaborated and contextual detail about the specific means, alternatives, and actions by which to best proceed when faced with difficult, novel, or complex situations. Consistent with this hypothesis, lower level construals are associated with better problem solving ( Watkins & Baracaia, 2002 ; Watkins & Moulds, 2005a ).

A second mechanism by which level of construal may influence the consequences of RT is through its effects on self-regulation. Increased focus on a concrete level of construal is hypothesized to facilitate self-regulation in situations where elevated self-focused attention and deliberate efforts to control behavior may be counterproductive, such as choking under pressure and test anxiety ( Leary, Adams, & Tate, 2006 ). Since elevated self-focused attention and increased efforts at self-regulation are often characteristic of RT, in particular of rumination and worry, RT may become more constructive as thinking becomes more concrete. Leary et al. (2006) argued that abstract construals about the evaluative or interpersonal implications of one's behavior interrupt the smooth performance of behaviors, whereas, in contrast, more concrete construals benefit self-regulation by (a) focusing attention on the immediate demands of the present situation, (b) reducing anxiety, and (c) requiring less effort and thus using up fewer self-regulatory resources. For example, a basketball player would perform better when focusing on how to make the shot rather than when thinking about the implications of missing. Consistent with this analysis, the use of concrete construals frees up cognitive resources, reduces anxiety, and/or improves task performance, whether in the form of implementation intentions specifying how and when an action will be performed (“If I encounter situation X, then I'll perform behavior Y”) or via focusing on the sound of one's voice (vs. trying to be persuasive) when giving a speech, especially when the task is considered difficult or occurs under conditions of high cognitive load ( Brandstatter, Lengfelder, & Gollwitzer, 2001 ; Gollwitzer, 1999 ; Gollwitzer & Sheeran, 2006 ; Vallacher, Wegner, & Somoza, 1989 ; Webb & Sheeran, 2003 ).

A third mechanism by which the level of construal may influence the consequences of RT is by influencing the degree of generalization in response to emotional events. Processing characterized by higher level construals produces mental representations that generalize across situations and that do not incorporate specific contextual details. Such generalizations can be beneficial by allowing gainful and useful inferences across different situations beyond available data and by enabling transfer of learning from one situation to another ( Forster & Higgins, 2005 ; Vallacher & Wegner, 1987 ). However, in negative situations, more abstract construals could facilitate negative overgeneralizations where a single failure is explained in terms of a global personal inadequacy (e.g., “I am worthless”) rather than in terms of situation-specific difficulties ( Hamilton, Greenberg, Pyszczynski, & Cather, 1993 ). Such negative generalizations are implicated in the development of depression ( Beck, 1976 ; Beck, Rush, Shaw, & Emery, 1979 ; Carver, 1998 ; Carver & Ganellen, 1983 ; Carver, Lavoie, Kuhl, & Ganellen, 1988 ). Thus, when faced with negative information, more concrete construals are hypothesized to be more adaptive by reducing negative overgeneralizations. Consistent with this hypothesis, more concrete thinking is found to facilitate the interpretation of the causes of negative events as unstable and controllable ( Showers, 1988 ); voluntarily recalling an emotional event in specific detail produces less emotional response than recalling it at a more general level ( Philippot, Baeyens, & Douilliez, 2006 ; Philippot, Schaefer, & Herbette, 2003 ); and practice at recalling specific, contextualized autobiographical memories reduces the negative experience to a subsequent stressful task relative to practice at recalling general, decontextualized memories ( Raes, Hermans, Williams, & Eelen, 2006 ).

Evaluating Models of RT

What theory best accounts for the data and properties described above? A first step toward answering this question is to consider the existing theoretical models of RT and to evaluate how well they account for the different consequences and properties reviewed. Three principal approaches can be identified: the response styles approach ( Nolen-Hoeksema, 1991 , 2004b ), the cognitive processing approach ( Greenberg, 1995 ; Horowitz, 1985 ; Tedeschi & Calhoun, 2004 ), and the discrepancy-focused control theory approach ( Martin & Tesser, 1989 , 1996 ). 4

RST of Rumination

Nolen-Hoeksema's (1991 , 2000 , 2004b ) seminal RST hypothesizes that rumination is a trait-like style of responding to depressed mood that has been found to be consistent across situations and repeated testing ( Nolen-Hoeksema et al., 1993 ) and appears to be a stable individual difference characteristic ( Nolen-Hoeksema & Davis, 1999 ). The ruminative response style is hypothesized to be learnt in childhood, either because it was modeled by parents who themselves had a passive coping style ( Nolen-Hoeksema, 1991 ; Nolen-Hoeksema, Mumme, Wolfson, & Guskin, 1995 ) or because the child failed to learn more active coping strategies for negative affect as a consequence of overcritical, intrusive, and overcontrolling parents ( Nolen-Hoeksema et al., 1995 ), or early physical/sexual abuse. Retrospective studies have found that elevated rumination is associated with self-report of overcontrolling parents ( Spasojevic & Alloy, 2002 ) and reports of physical and sexual abuse ( Conway, Mendelson, Giannopoulos, Csank, & Holm, 2004 ), although, like all retrospective studies, current mood, memory biases, and demand biases could influence the report of past events, raising questions as to veridicality.

The RST provides a detailed analysis of the mechanisms by which RT leads to unconstructive outcomes but was conceived with less explanatory power with regard to explaining how RT can be constructive. The RST emphasizes the importance of repeated and passive focus on depressed symptoms in determining the negative effects of rumination (e.g., Lyubomirsky & Nolen-Hoeksema, 1995 ; Nolen-Hoeksema, 1991 , 2004b ). The RST proposes that ruminative self-focus in response to a depressed mood amplifies a vicious cycle between depressed mood and negative, pessimistic thinking, thereby exacerbating negative mood and negative thinking and impairing problem solving. Research has demonstrated that depressed mood has negative effects on thinking by selectively priming mood-relevant information and activating mood-congruent memories, beliefs, and expectations ( G. H. Bower, 1981 ; Teasdale, 1983 ). In turn, these negative cognitions can then further maintain or exacerbate negative mood, producing a vicious cycle between depressed mood and negative thinking. RST proposes that focus on symptoms further fuels this vicious cycle, consistent with a considerable body of research indicating that self-focus can act to amplify the effect of negative mood on thinking ( Ingram, 1990 ; Ingram & Smith, 1984 ; Pyszczynski & Greenberg, 1987 ). As such, RST provides a good account of how structural factors such as negatively valenced thought content, current dysphoric mood, or negative self-beliefs would result in RT with unconstructive consequences.

However, a major limitation of the RST is that it was not designed to explain RT with constructive outcomes, and, as such, does not directly instantiate how RT could have positive consequences. Nonetheless, by logical extension, one can hypothesize that the amplifying effects of RT could also work for positive valence, such that RT focused on positive thought content would amplify a self-reinforcing cycle between positive mood and more optimistic thinking, consistent with the observed influence of thought valence on the consequences of RT. However, even with this extension to the RST, it cannot account for the evidence that RT focused on negative content can still have constructive consequences, as found in experimental manipulations of depressive rumination, defensive pessimism, or cognitive processing of distressing events. For example, several experimental studies found that RT focused on depressive symptoms has constructive consequences ( Watkins & Moulds, 2005a ; Watkins & Teasdale, 2001 ; 2004 ), inconsistent with the RST. Nor can the RST account for constructive consequences of RT that are not tied to increases in positive affect, since the constructive effects of RT would depend on amplifying the reciprocal cycle between positive mood and optimistic cognition. For example, improvements in problem solving following RT that are not associated with improvements in mood (e.g., Lyubomirsky et al., 1999 ; Watkins & Baracaia, 2002 ) cannot be explained by RST. Furthermore, RST cannot account for the influence of process aspects of RT on its consequences, in particular, the level of construal adopted during RT. A further limitation of RST is that it exclusively focuses on RT in response to sad or depressed mood. Although this is an important focus for RT, RT can also be triggered by and focused on other negative mood states, unresolved goals, and life events ( Lavallee & Campbell, 1995 ; Millar, Tesser, & Millar, 1988 ; Robinson & Alloy, 2003 ), as well as on positive content ( Martin & Tesser, 1996 ).

Cognitive Processing Theories

RT focused on coming to terms with past upsetting events is a key element of the cognitive processing literature. Stressful and traumatic events often contain novel information or give rise to appraisals that are not consistent with prior mental structures such as the beliefs and assumptions that people hold about themselves and the world ( Janoff-Bulman, 1992 ). For example, a violent assault and the increased sense of vulnerability it produces would clash with prior beliefs such as “the world is basically safe” and “bad things don't happen to good people.” Cognitive processing accounts propose that this discrepancy between the meaning of the negative event and pre-existing mental structures makes it difficult to integrate this new information into current mental structures and leads to distress. Recovery from distressing experiences is assumed to require that the person work through and resolve the incongruence between the information acquired from the distressing experience and pre-existing mental structures representing the world ( Horowitz, 1986 ). Within cognitive processing accounts, the discrepancy between the meaning of the event and pre-existing mental structures is proposed to produce RT in the form of repeated intrusions and re-experiencing of the distressing event until the discrepancy is resolved. Thus, cognitive processing accounts have explained the onset and maintenance of RT. However, these accounts have not tended to explicitly instantiate what determines whether RT has constructive or unconstructive outcomes. Indeed, there has been some debate as to whether the RT is a necessary and active part of working through the upsetting event or simply an epiphenomenon of recovery ( Harber & Pennebaker, 1992 ; Horowitz, 1986 ; Janoff-Bulman, 1992 ; Tait & Silver, 1989 ).

Nonetheless, cognitive processing approaches are consistent with structural factors such as valence influencing the consequences of RT. Recent cognitive processing accounts emphasize that a focus on finding benefit when thinking about upsetting and traumatic events results in better outcomes, consistent with the valence of thought content influencing the consequences of RT. In addition, theoretical accounts of cognitive processing suggest that it will be easier to organize and make coherent one single event rather than multiple events simultaneously, because multiple memories will interfere with the processing of each other, take up more central executive resources ( Foa & Kozak, 1986 ), and include more disparate material that does not easily fit into the temporal and spatial sequence necessary for the creation of a coherent story, which is hypothesized to be essential for effective working through of upsetting events ( Foa et al., 1995 ; Klein & Boals, 2001 ; Pennebaker, 1997 ; Pennebaker et al., 1997 ; Pennebaker & Seagal, 1999 ; Sloan & Marx, 2004 ; Smyth et al., 2001 ). Thus, because a negative intrapersonal context increases the availability and accessibility of negative concerns and negative memories ( S. M. Smith & Petty, 1995 ; Teasdale, 1983 ; Teasdale & Barnard, 1993 ; Teasdale & Dent, 1987 ), it may make it harder to effectively process any particular difficult event.

However, cognitive processing theories cannot account for how the level of construal could influence the consequences of RT. A further limitation of cognitive processing accounts is that they have predominantly focused on RT related to traumatic and distressing events, where there is a discrepancy between the meaning of the distressing events and existing beliefs. As such, cognitive processing theories do not account for different consequences of RT that are unrelated to such discrepancies in meaning and emotion, for example, anticipatory RT associated with adaptive planning and preparation or the uptake of health-promoting behaviors.

Control Theory Approaches to RT

Control theory proposes that all behavior, including mental activities, reflects a process of feedback control. Individuals perceive their current state and behavior and then compare these perceptions with salient reference values such as their goals, standards, or desired outcomes. If the comparison indicates a discrepancy between actual state and reference value, such as an unresolved goal, behavior will be adjusted in order to bring it closer to the reference value ( Carver & Scheier, 1982 , 1990 ; Carver & Scheier, 1998 ). In particular, discrepancies between expected rates of progress toward goals are hypothesized to influence behavior and affect. The original control theory approach to RT emphasized that rumination is triggered by a discrepancy in goal progress and that these goals are organized hierarchically ( Martin & Tesser, 1989 , 1996 ). Furthermore, RT focused on the discrepancy in attaining the unresolved goal is intended to serve the function of facilitating progress toward the reference value. Within this account, the RT will continue either until the goal is met or until the individual disengages from and abandons the goal ( Carver & Scheier, 1990 ; Klinger, 1975 ; Martin, Shrira, & Startup, 2004 ; Martin & Tesser, 1989 , 1996 ; Pyszczynski & Greenberg, 1987 ; Wells & Matthews, 1994 ). It is important to recognize that representations of both external stimuli (e.g., a physical situation, a concrete outcome) and internal stimuli (e.g., moods, feelings) can act as reference values for goals, such that RT can be influenced by discrepancies in representations of both external and internal states.

There is accumulating evidence consistent with this goal-discrepancy control theory approach to RT. RT about important people and activities left behind when coming to college was positively predicted by the extent to which these activities remained interrupted at college, that is, the extent these important goals were not attained ( Millar et al., 1988 ). Abstract goals that are more important and meaningful to people, such as attaining happiness, and concrete goals that are linked to these important abstract goals, such as being in a romantic relationship, produce more RT when not attained ( W. D. Mcintosh, Harlow, & Martin, 1995 ; W. D. McIntosh & Martin, 1992 ). In a diary study, negative events that were related to personal goals produced more RT than goal-unrelated negative events ( Lavallee & Campbell, 1995 ).

The tendency toward RT seems to depend on the perseverance of unresolved goal-related thoughts, as evidenced in the Zeigarnik effect, in which recall of interrupted and uncompleted tasks is significantly better than recall of completed tasks ( Kuhl & Beckmann, 1985 ; Kuhl & Helle, 1986 ; Zeigarnik, 1938 ). There is an extensive literature confirming that unresolved and blocked goals increase the priming and accessibility of goal-relevant information as well as the perseverance of goal-related thoughts ( Brunstein & Gollwitzer, 1996 ; Goschke & Kuhl, 1993 ; Martin & Tesser, 1989 ), whereas resolved goals inhibit the priming and accessibility of goal-relevant information, consistent with a control process account of how RT would be initiated and terminated ( Forster, Liberman, & Higgins, 2005 ; R. E. Johnson, Chang, & Lord, 2006 ).

Moreover, principles within control theory can be elaborated to account for the reviewed findings. Critically, unlike the other accounts, the control theory account ( Martin & Tesser, 1989 , 1996 ) explicitly hypothesized that RT can have constructive or unconstructive consequences. Within control theory, RT produces constructive consequences if it helps to resolve the discrepancy between the intended goal and actual current state, whether by aiding progress toward the goal or by helping to modify or abandon the goal ( Klinger, 1975 ; Martin & Tesser, 1989 , 1996 ; Wrosch, Scheier, Miller, Schulz, & Carver, 2003 ). In contrast, RT becomes unconstructive if a person experiences an inability to progress toward reducing the discrepancy and at the same time is unable to give up on the reference value or goal. In such a case, RT would serve only to focus attention on the discrepancy between the desired goal and the actual situation, making the unresolved discrepancy more salient, perpetuating the unresolved issue, and exacerbating negative affect ( Carver & Scheier, 1990 , 1998 ; Klinger, 1975 ; Kuhl & Beckmann, 1985 ; Martin & Tesser, 1989 , 1996 ; Pyszczynski & Greenberg, 1987 ). It is important to distinguish between disengaging from efforts at goal pursuit, whether mentally or physically, and disengaging from the underlying goal: The former combines a lack of goal progress with the ongoing maintenance of the desired but unattained goal, further highlighting the unresolved discrepancy, whereas the latter constructively reduces the goal discrepancy.

To date, control theory accounts have focused on hypothesizing the mechanisms underpinning the onset, frequency, and duration of RT rather than instantiating the mechanisms that determine whether RT is constructive or unconstructive. RT was proposed to be beneficial if individuals “use a form of rumination that can provide a solution for the type of problem they are facing,” although this was not further specified other than to suggest that applying logic to insight problems or insight to logic problems may be unhelpful ( Martin et al., 2004 , p. 171). Nonetheless, there are principles within control theory that can be elaborated to explain how the properties identified in this review can influence the consequences of RT.

First, control theory can account for the findings that structural aspects of RT such as valence of thought content and intrapersonal context influence the consequences of RT. Within control theory, expectancies and beliefs about the self and about the outcomes of behavior are hypothesized to play an important role in determining how a person responds to a discrepancy between the actual state and the desired state, by influencing persistence at goal pursuit, the reference values by which goal progress is judged, the interpretation of feedback, and the judgment of when to abandon a goal ( Carver & Scheier, 1990 , 1998 ; Hyland, 1987 ). More negative expectancies, such as doubts about ability to succeed, will lead to attempts to disengage from goal pursuit as well as a greater perceived discrepancy between desired state and actual state. As noted above, disengaging from goal pursuit will leave an unresolved discrepancy, which, in the absence of abandoning the unresolved goal, will cause RT to have unconstructive consequences. Moreover, an individual's beliefs and moods, particularly those relevant to judging self-worth, will influence their goals and reference values, such that more extreme beliefs about what is required to achieve self-worth will result in (a) harder-to-attain reference values, making discrepancies between the desired state and the actual state harder to resolve and (b) harder-to-abandon goals. For example, negative mood can cause individuals to increase their standards for success, making it harder to resolve a goal discrepancy ( Cervone, Kopp, Schaumann, & Scott, 1994 ), consistent with a control theory account of RT. In the context of RT, this analysis suggests that maladaptive beliefs about what is required to be a worthwhile person, such as high levels of dysfunctional attitudes, will lead to both harder-to-attain goals and reluctance to abandon these goals, trapping an individual in unconstructive RT, consistent with the observed findings (e.g., Ciesla & Roberts, 2007 ).

Moreover, self-representations can influence the ability of individuals to disengage from an unresolved goal by substituting it with positive affirmations on another aspect of self that relates to the same superordinate goal. Affirming valued aspects of the self reduces RT about a frustrated goal ( Koole, Smeets, van Knippenberg, & Dijksterhuis, 1999 ). However, individuals with reduced self-esteem and more dysfunctional self-beliefs have reduced self-affirmational resources in response to difficulties ( Koole et al., 1999 ; Steele, Spencer, & Lynch, 1993 ), making it harder to disengage from unconstructive RT about an interrupted or incomplete goal and move onto more constructive RT ( Di Paula & Campbell, 2002 ; Kuhl & Beckmann, 1985 ; Kuhl & Helle, 1986 ) or to disengage from unsolvable tasks ( Aspinwall & Richter, 1999 ). Since expectancies are examples of positive and negative thoughts, while beliefs and mood are elements of intrapersonal context, control theory thus accommodates the structural aspects of RT identified earlier.

Second, and more pertinently, further elaboration of principles within control theory accounts for the finding that process aspects of RT such as level of construal influence the consequences of RT. Within control theory, it is hypothesized that goals and behaviors are hierarchically organized and can be processed at different levels of abstraction, with more abstract, superordinate goals and standards guiding and informing more specific, subordinate goals and standards. Within this hierarchical organization, pursuit toward abstract goals occurs by specifying reference values at the next lower level of abstraction, all the way down to the concrete representations required to specify the actual behaviors needed to progress toward the goal ( D. E. Broadbent, 1977 ; Carver & Scheier, 1990 , 1998 ; Emmons, 1992 ; Powers, 1973a , 1973b ; Vallacher & Wegner, 1987 ). Carver and Scheier (1990) proposed that the most abstract levels represent a global sense of idealized self, which in turn sets the broad principles that organize goals and behavioral standards across multiple situations (e.g., to be an honest person), corresponding to higher level construals, whereas the more concrete levels represent the specific actions and behavioral programs necessary to implement the principles in a particular situation (e.g., telling the truth to a friend), corresponding to lower level construals. Thus, this hierarchical organization affords the use of high- and low-level construals, consistent with the distinction between abstract versus concrete processing within RT.

Further, control theory hypothesizes that effective self-regulation requires flexible and balanced coordination between the different levels within the goal hierarchy, such that the superordinate level of control adaptively varies in response to situational and task demands. Depending on context, a level of control that is too abstract, too concrete, or that fails to link abstract levels to concrete levels is hypothesized to be detrimental ( Carver & Scheier, 1998 , Chapter 13). Elaborating on key principles within control theory suggests that there are a number of distinct advantages and disadvantages for self-regulation when the level of control is located higher or lower in the goal hierarchy, corresponding to abstract versus concrete levels of construal, respectively.

Thus, one hypothesized advantage of higher level, abstract control is increased consistency and stability of behavior toward long-term goals across time and across different situational demands because higher level control ensures that subordinate goals and actions remain directed toward personally important higher level goals and minimizes interference from incidental influences ( Carver & Scheier, 1998 ; Vallacher & Wegner, 1987 ; 1989 ). In contrast, low-level control is hypothesized to be more sensitive to contextual and situational detail, resulting in increased impulsiveness and distractibility. Consistent with this hypothesis, a habitual tendency toward more abstract construals is associated with more persistent and stable behavior, greater self-motivation, less impulsiveness, and fewer action errors ( Vallacher & Wegner, 1989 ), and adopting high-level construals produces greater self-control on experimental tasks than adopting low-level construals ( Fujita, Trope, Liberman, & Levin-Sagi, 2006 ). A second hypothesized advantage of higher level control is that it provides more flexibility in responding to relatively low-level goals that are unattained because processing at a higher level affords more alternative subgoals and behaviors to resolve the goal discrepancy ( Brunstein & Gollwitzer, 1996 ). For example, if an individual is failing to progress on the daily goal of writing a poem, control at the level of an abstract superordinate goal (e.g., “to be creative”) provides alternative goals and means to resolve this discrepancy (e.g., play music, draw, paint) that are not available if the functionally superordinate goal is just to complete a poem. Thus, this analysis suggests that under some circumstances, for example, when considering long-term goals, RT characterized by higher level, abstract construals will be constructive.

However, a logical elaboration from control theory is that higher level abstract control will become disadvantageous under particular circumstances. First, because pursuit toward abstract goals occurs by specifying reference values at the next lower level, down to the actual concrete behaviors required, the aforementioned advantages of higher level control/abstract construals will only occur when there is sufficiently operationalized specification from the higher levels down to lower levels of representation (see also Carver & Scheier, 1998 ). When programs and sequences of goal-related behaviors are straightforward, familiar, and practiced, an individual will have developed extensive procedural knowledge specifying the links between goals and behaviors across all levels, making higher level control of self-regulation effective ( Anderson, 1983 ; Vera & Simon, 1993 ). However, under circumstances of novelty, unfamiliarity, difficulty, or stress, this specification of reference values down through the control hierarchy can break down, such that the advantages of controlling self-regulation at a higher level are lost. For example, adopting a high level of control focused on a goal such as “be punctual” would not be useful for either a learner driver still getting used to handling a car or for an experienced driver in hazardous, unfamiliar driving conditions such as a snowstorm since, in both cases, there is not well-established specification of how high-level reference values translate into subgoals and concrete behavior. Instead, control of behavior needs to be located at low levels in the hierarchy concerned with concrete and specific actions. Second, when the superordinate abstract goal is ill-defined and it is difficult to specify how it might actually be achieved, control at a higher level in the goal hierarchy is going to be problematic. For example, a goal like “be happy” may be too abstract and vague to provide clear guidance as to how an individual might specify subgoals toward attaining it. Third, processing at a more abstract level may interfere with goal disengagement: The more abstract the level at which a goal is represented in the hierarchy, the more important the goal becomes to the general sense of self, and the harder it becomes to disengage from the goal ( Martin & Tesser, 1996 ; W. D. McIntosh & Martin, 1992 ; Millar et al., 1988 ). Such abstract construals will be unproblematic when there is sufficient progress toward the relevant goal. However, when a goal is difficult or impossible to attain, processing at too abstract a level will make it harder to relinquish the goal, trapping the individual in the invidious state where he or she can neither make progress toward the goal nor abandon it, leading to persistent but unconstructive RT. This analysis therefore suggests that under circumstances of novelty, unfamiliarity, difficulty, or stress, RT characterized by higher level, abstract construals will be unconstructive as it gives limited guidance as to what to do next.

The elaborated control theory therefore proposes that for more difficult and novel tasks, where full specification through the goal–action hierarchy is lacking, control of behavior at more concrete, lower levels in the hierarchy is more functional. Shifting control down to lower levels of abstraction, which corresponds to a more concrete level of construal, is hypothesized to ensure that goals and standards are translated into effective goal pursuit, because processing at a more concrete level serves the functions of determining the specific means and actions by which to best proceed and focuses attention on the immediate environment ( Carver & Scheier, 1998 ; Vallacher & Wegner, 1987 ). Moreover, lower level construals may provide more concrete indicators of progress than might high-level construals ( Emmons, 1992 ): It is easier to determine if one is being successful at pursuing a lower level goal like “keeping your desk clean” than the associated higher level goal of “being more organized.” 5 Further, a more concrete level of construal may make it easier to disengage from an unattainable goal by reducing its personal importance and self-relevance.

Thus, by logically elaborating on principles within control theory, it is hypothesized that higher level, abstract construals promote effective goal progress for unproblematic, familiar, or positive situations, but that lower level, concrete construals are more constructive for difficult or novel situations and unattainable goals. Therefore, the elaborated control theory hypothesizes an interaction between structural aspects (valence) and process aspects (level of construal) in determining the consequences of RT. Critically, this account explains the observed pattern of findings in which adopting a more concrete level of construal during RT about negative content or in the context of negative situations (e.g., focusing on depressed mood, thinking about upsetting events, or planning for stressful events like exams) results in more constructive outcomes than does RT characterized by a more abstract level of construal (e.g., Leary et al., 2006 ; Pham & Taylor, 1999 ; Vallacher et al., 1989 ; Watkins & Moulds, 2005a ; Watkins & Teasdale, 2001 , 2004 ; Webb & Sheeran, 2003 ). However, the corollary prediction that during RT about positive content or in the context of positive situations abstract construals will have more constructive outcomes than those of concrete construals has not been extensively tested. Recent evidence consistent with this prediction is the finding that people with low self-esteem induced to think abstractly about a recent compliment from a romantic partner report greater state self-esteem and greater security in their relationships than do people with low self-esteem induced to think concretely about a recent compliment ( Marigold, Holmes, & Ross, 2007 ).

A related prediction from the elaborated control theory is that by default individuals will adopt more abstract construals but will shift to more concrete construals when faced with difficulties (see also Wegner & Vallacher, 1987 ). Consistent with this hypothesis, individuals tend by default to use more abstract construals, focused on the meanings, consequences, and implications of actions ( Wegner & Vallacher, 1987 ; Wegner, Vallacher, Kiersted, & Dizadji, 1986 ; Wegner, Vallacher, Macomber, Wood, & Arps, 1984 ), yet when faced with difficult, novel, or complex situations, people often move toward more concrete levels of processing ( Beckmann, 1994 ; Vallacher, Wegner, & Frederick, 1987 ; Wegner et al., 1984 ; Wong & Weiner, 1981 ), although there are exceptions, including the tendency toward depressive rumination in response to sad mood ( Nolen-Hoeksema, 1991 ) and occasions when more abstract construals are adopted in response to failure and goal frustration ( Wicklund, 1986 ). Other evidence consistent with this hypothesis is the finding that in neutral and happy moods people adopt a more global, abstract processing style but shift into a more local, concrete processing style in response to sad mood ( Beukeboom & Semin, 2005 , 2006 ; Bless et al., 1996 ; Gasper & Clore, 2002 ; Isbell, 2004 ; Kurman, 2003 ; Storbeck & Clore, 2005 ). This hypothesis predicts that RT will tend to be characterized by higher level, abstract construals when goal progress is unproblematic, but that RT will tend to be characterized by lower level, concrete construals when goal progress is blocked. Because the elaborated control theory hypothesizes that higher level control is the default level of control, it also accounts for the finding that competence, practice, and expertise influence the consequences of RT, for example, the benefit of RT for depressive pessimists. When an individual is more familiar and skilled within a domain, he or she is more likely to have good specification from high levels to low levels in the goal–action hierarchy, reducing the likelihood of higher level control breaking down.

The Control Theory Account: An Integrative Overview and Novel Predictions

One of the main strengths of this elaborated control theory account is its ability to account for the findings reviewed regarding the different consequences of RT, in particular, its ability to accommodate both structural approaches to RT (valence, context) and to expand on these approaches to explain process approaches to RT (level of construal). Moreover, this control theory account of RT is consistent with the extensive literature linking goal discrepancy with RT. A further advantage of the control theory approach is that it can integrate the other theoretical approaches to RT and their associated findings within its conceptual framework. Control theory can explain the findings within cognitive processing accounts, since both theories propose that a key mechanism driving RT is the attempt to reduce discrepancies, whether between current outcome and desired goals or between current informational state and existing mental structures ( Martin & Tesser, 1989 ). Within control theory, the adoption of a higher level goal such as “making sense of events” or “reducing discrepant information” could account for the observations within the cognitive processing account, as explicitly outlined within models of posttraumatic growth ( Tedeschi & Calhoun, 2004 ). Moreover, despite initial suggestions that discrepancies were not necessary for depressive rumination to occur ( Nolen-Hoeksema, 1991 ), theoretical accounts suggest that focus on the causes and consequences of depressed mood is likely to involve focus on unresolved goal discrepancies (e.g., Brunstein & Gollwitzer, 1996 ; Oatley & Johnson-Laird, 1987 ). Moreover, recent findings within RST are consistent with the predictions of control theory: (a) The content of experimentally induced rumination is characterized by thinking about unresolved personal problems ( Lyubomirsky et al., 1999 ); (b) depressive rumination is associated with meta-cognitive beliefs that rumination is useful for understanding depression and solving problems, suggesting that depressive rumination is adopted with the intention of resolving goal-based or meaning-related discrepancies ( Lyubomirsky & Nolen-Hoeksema, 1993 ; Papageorgiou & Wells, 2001 ; Watkins & Baracaia, 2001 ; Watkins & Moulds, 2005b ); and (c) experimentally induced rumination and discrepancy-focused thinking both increase anxiety and depressed mood to an equivalent degree and are indistinguishable in terms of flow of thought content ( Nolen-Hoeksema, 2004a ). Indeed, Treynoret al (2003 , p. 256) interpreted brooding as “a passive comparison of one's current situation with some unachieved standard,” consistent with a control theory account.

A further advantage of the control theory account is that it can account for the adoption of the different structural and process aspects of RT. For example, as noted earlier, there is evidence that in response to difficulties, individuals sometimes adopt a more concrete level of construal ( Vallacher & Wegner, 1987 ) but sometimes adopt more abstract construals ( Wicklund, 1986 ), as exemplified by the abstract RT found during depressive rumination/brooding. Thus, any theory of RT needs to explain the mechanisms underpinning whether RT involves (a) negative or positive thought content and (b) an abstract or concrete level of construal. Structural factors, such as valence, are relatively straightforward to explain across all models of RT: Thought valence will be determined by the nature of the event and the context in which RT occurs as well as by individual beliefs, expectancies, and learning history. In addition, within control theory, goal progress at a rate faster than anticipated produces positive mood and cognition, whereas goal progress slower than anticipated produces negative valence ( Carver & Scheier, 1990 ).

The elaborated control theory account hypothesizes that the level of construal is principally determined by adaptive regulation of level of construal in response to situational demands, such that construal typically becomes more concrete in response to difficulties, but that various situational, motivational, and cognitive factors can interfere with this regulatory process. First, the extent to which goal progress is blocked is hypothesized to influence the level of construal adopted ( Martin & Tesser, 1996 ): When goal progress is moderately thwarted it is still adaptive to shift to lower level construals ( Vallacher & Wegner, 1987 ), whereas more severe blockage, particularly for highly self-relevant goals, leads to higher level construals ( Wicklund, 1986 ), as individuals re-orient to their higher order concerns. Second, self-related beliefs are hypothesized to influence the preference toward more abstract or more concrete levels of construal. For example, meta-cognitive beliefs that it is important to understand and make sense of feelings and problems would encourage the use of higher level construals. Likewise, low, unstable or contingent self-esteem leads to attempts to pursue self-esteem by trying to validate abilities and qualities ( Crocker & Knight, 2005 ; Crocker & Park, 2004 ; Crocker & Wolfe, 2001 ), which typically involves evaluating one's self-worth at a trait level, that is, the use of more abstract construals ( Baumeister & Tice, 1985 ; Lyubomirsky, 2001 ). Further, as the perceived probability of an event reduces, construals become more abstract ( Wakslak, Trope, Liberman, & Alony, 2006 ) such that more negative expectations would engender more abstract construals. Third, effective regulation of level of construal in response to situational demands is hypothesized to require good cognitive and central executive control. Thus, individuals with deficits in executive/inhibitory control, either because of greater cognitive load or reduced cognitive resources, would be impaired at effectively regulating level of construal in response to situational demands. This analysis predicts that individuals with these vulnerability factors will be compromised in their ability to flexibly regulate level of construal in response to situational demands to the extent that they do not show the functional shift toward lower level construals typically observed in response to difficulties ( Bless et al., 1996 ; Gasper & Clore, 2002 ; Isbell, 2004 ; Kurman, 2003 ; Storbeck & Clore, 2005 ). Given that patients with depression and depressive ruminators are observed to have such meta-cognitive beliefs ( Lyubomirsky & Nolen-Hoeksema, 1993 ; Papageorgiou & Wells, 2001 ; Watkins & Baracaia, 2001 ; Watkins & Moulds, 2005b ), reduced self-esteem, and deficits in executive/inhibitory control ( R. N. Davis & Nolen-Hoeksema, 2000 ; Gotlib, Yue, & Joormann, 2005 ; Hertel, 1997 ; Joormann, 2004 , 2006 ), this analysis suggests the level-of-construal dysregulation hypothesis , which predicts that depression-prone groups will be impaired at regulating their level of construal in response to difficulties, leading to an overly abstract level of construal and to RT that has unconstructive consequences. Consistent with this prediction, a recent study found that individuals with mild-to-moderate depressive symptoms generated counterfactual RT about a negative event characterized by more concrete construals than those of non-depressed individuals, whereas individuals with severe depressive symptoms generated counterfactual RT characterized by more abstract construals (e.g., global, characterological judgments). Thus, mild depressive symptoms are associated with the adaptive regulation of level of construal in response to mood, but more extreme depressive symptoms are associated with dysregulation of this process ( Markman & Miller, 2006 ). Thus, this level-of-construal dysregulation hypothesis accounts for why the subset of individuals prone to depression and brooding show a tendency to adopt RT characterized by more abstract construals, despite it having unconstructive consequences.

The further test of the scientific utility of this elaborated control theory approach to RT is its ability to make unique testable predictions that can be evaluated in future research. The current analysis has generated a number of such testable predictions. First, as noted above, the level-of-construal dysregulation hypothesis predicts that whereas the majority of individuals will preferentially adopt higher level construals in unproblematic, familiar, positive, and neutral situations, but will shift to lower level construals in the face of difficulties and negative mood, individuals at risk for depression will continue to preferentially adopt higher level construals even in the face of difficulties and negative mood. Second, as noted earlier, the elaborated control theory predicts an interaction between level of construal and valence in determining the consequences of RT. Lower level construals are predicted to be more adaptive during RT focused on negative content or occurring within a negative context, whereas higher level construals are predicted to be more adaptive during RT focused on positive content or occurring within a positive context. Thus, the use of repeated training paradigms in which individuals learn to adopt a more concrete level of construal in response to emotional events would be predicted to reduce emotional vulnerability to a subsequent negative event but also to reduce positive response to a subsequent positive event. Likewise, since people construe nearer future events in more concrete terms than distant future events ( Forster, Friedman, & Liberman, 2004 ; Liberman & Trope, 1998 ; Trope & Liberman, 2003 ), focusing on nearer future events during negatively valenced RT is predicted to result in more constructive outcomes than focusing on distant future events, with the reverse pattern of findings predicted for positively valenced RT.

Third, this approach has a number of implications for the treatment of psychological disorders, since RT has been demonstrated to contribute to both anxiety and depression ( Harvey et al., 2004 ). It suggests that when an individual starts to dwell on a negative event or difficulty, shifts in how he or she does this could potentially move him or her from RT that exacerbates difficulties to RT that helps recovery. This analysis suggests that the goal of therapy for people with unconstructive RT should not be to reduce their RT but rather to shift them to more constructive forms of RT. Targeting such changes could contribute to more effective and systematic treatments for psychological disorders. This analysis predicts that RT with constructive consequences can be facilitated by (a) reducing the extent and accessibility of negative thought content while increasing the extent and accessibility of positive thought content and (b) encouraging a shift into a more concrete level of construal when focused on difficulties and negative mood (see Teasdale, Segal, & Williams, 1995 , for a related analysis). These predictions are consistent with a number of psychological therapies empirically shown to be effective in treating depression and anxiety. Both cognitive behavior therapy (CBT) and behavioral activation implicitly encourage patients to be more concrete, specific, and detailed in their description and analysis of activities. Further, in both therapies, patients work to build up success, mastery, and pleasurable activities, and, thereby, improve self-esteem and strengthen and make more accessible positive cognition. Mindfulness-based cognitive therapy, which has been demonstrated to significantly reduce rates of relapse in people with a history of recurrent depression in several trials ( Ma & Teasdale, 2004 ; Teasdale et al., 2000 ) and to reduce depressive rumination ( Ramel, Goldin, Carmona, & McQuaid, 2004 ), explicitly uses meditation practice to train patients away from abstract levels of processing and into a more concrete mode of processing ( Segal, Williams, & Teasdale, 2002 ). Moreover, a recent adaptation of CBT that explicitly focuses on shifting processing toward lower level construals has encouraging initial results in the treatment of residual depression, reducing symptoms and depressive rumination ( Watkins et al., 2007 ).

These examples are illustrative rather than exhaustive. Nonetheless, they demonstrate how the control theory can generate unique, testable predictions as well as account for current knowledge. The veracity of the account should be subject to evaluation by the rigorous testing of these and other relevant predictions.

Future Research

Areas for future investigation.

The current review also highlights important gaps in the research on RT. First, the study of RT has been predominantly focused on depression, worry, and trauma. Future research needs to examine the processes of RT with respect to other psychological disorders, other triggering events, and other emotions. Recent findings linking RT prospectively to bulimia and substance abuse in female adolescents ( Nolen-Hoeksema et al., 2007 ) and concurrently to bipolar disorder ( S. L. Johnson et al., in press ) suggest the value of further RT research in these disorders. Second, many of the prospective studies of RT related to psychological disorders have not explicitly reported or controlled for previous episodes of the relevant disorder (e.g., major depression), which could potentially act as a common variable, explaining why elevated RT predicts future symptoms. Third, there is a preponderance of research on RT with unconstructive consequences, which needs to be balanced by more research into the constructive aspects of RT. In particular, more prospective longitudinal studies and experimental studies are necessary to investigate the constructive consequences of RT, especially in the areas of cognitive processing and posttraumatic growth, where most of the evidence is still only cross-sectional. Fourth, such research requires behavioral, physiological, or observer-rated outcome measures that reduce the risk of constructive outcomes resulting from inaccurate, biased, or defensive self-reports.

Fifth, a valuable addition to research in this field will be the development of measures that can assess both constructive and unconstructive aspects of RT, as well as RT across a wider range of situations and moods. The limitations of the RSQ were noted earlier: Future research will usefully assess RT using alternative questionnaires ( Siegle, Moore, & Thase, 2004 ) that do not confound RT with the degree of negative affectivity and that can capture other potentially relevant dimensions such as the duration, controllability, and repetitiveness of RT. Likewise, the assessment of RT through non-self-report measures is a priority, such as developing on-line measures of RT, such as the use of thought sampling, or cognitive-experimental and psychophysiological indices associated with self-reported RT, such as attentional bias ( Joorman et al., 2006 ), sustained pupil dilation to negative information ( Siegle, Granholm, Ingram, & Matt, 2001 ; Siegle, Steinhauer, Carter, Ramel, & Thase, 2003 ), or sustained event-related fMRI amygdala activity in response to emotional words ( Siegle, Steinhauer, Thase, Stenger, & Carter, 2002 ). Sixth, the process of goal disengagement needs more detailed examination. Goal disengagement and goal reengagement are increasingly suggested to be important in determining well-being ( Rasmussen, Wrosch, Scheier, & Carver, 2006 ; Wrosch, Dunne, Scheier, & Schulz, 2006 ; Wrosch & Heckhausen, 1999 ; Wrosch et al., 2003 ; Wrosch, Schulz, & Heckhausen, 2004 ) and, to date, are neglected in the study of RT.

Other Possible Moderators of the Consequences of RT

This review focuses on factors that were robustly demonstrated to moderate the consequences of RT. Nonetheless, there was tentative evidence that several other factors may moderate the consequences of RT. First, two correlational studies suggested that the purpose motivating RT may moderate its consequences: RT motivated by curiosity and by searching for new ideas and experiences was associated with less negative affect/depression than RT motivated by neurotic, threat-related concerns or by the need for certainty ( Segerstrom et al., 2003 ; Trapnell & Campbell, 1999 ). Prospective and experimental studies are necessary to explore whether the purpose of RT may be a potential moderator. Second, rigidity of thought during RT (e.g., perseveration on the same content vs. generation of many different ideas) may be a potential moderator of the consequences of RT. Several studies suggest that the generation of an increased number of different thoughts and ideas is associated with constructive consequences for RT ( Cantor et al., 1987 ; El Leithy et al., 2006 ), whereas RT defined in terms of perseveration and Stagnant Deliberation is associated with increased depression ( Ehring, 2007 ; Feldman & Hayes, 2005 ). Thus, RT that is highly repetitive, “stuck,” and perseverative may be unconstructive. This suggestion parallels Ingram's (1990) proposal that pathological self-focus is characterized by excessive frequency, sustained duration, and rigidity. By extension, it may be useful to investigate whether frequency, duration, and repetitiveness of RT moderate the consequences of RT.

The analysis outlined here builds on many others and represents ongoing efforts to identify the key mechanisms that influence the different consequences of RT. In this article, I review evidence indicating that RT can have unconstructive and constructive consequences. In the course of reviewing the literature on RT, three factors emerged to account for the differential consequences of RT: the valence of thought content, the intrapersonal and situational context of the individual engaged in RT, and the construal level of the RT. Table 3 describes how each of the major classes of RT reviewed earlier can be characterized in terms of these moderating factors. Thus, depressive rumination ( Nolen-Hoeksema, 1991 ) is characterized by negatively valenced thought content (RT about depression), a negative intrapersonal context (depressed mood, negative self-beliefs), and an abstract level of construal (thinking about meanings and implications), with accompanying unconstructive consequences. Several classes of RT have inclusive and broad definitions, such that they cannot be characterized by a particular value for each factor (e.g., Martin & Tesser's, 1989 , 1996 , definition of rumination encompasses positive vs. negative content, abstract vs. concrete construals). Worry has been described as having unconstructive consequences and constructive consequences. Within the current analysis, all worry is characterized by negative valence (thoughts of a real or potential problem), but worry characterized by a concrete level of construal is constructive, whereas worry characterized by an abstract level of construal and negative intrapersonal context (e.g., low problem-solving confidence) is unconstructive. Moreover, although the valence of the context typically matches the valence of thought content, there are exceptions; for example, in problem solving and defensive pessimism, thought content is negative (thoughts of a problem) but intrapersonal context is positive, reflecting high levels of optimism and positive self-belief. This analysis also suggests that there may be two routes by which cognitive processing could be constructive: Following a stressful event (negative situational context), it could be useful to either focus on finding benefits (positive content) in as abstract a way as possible or to focus on the negative experience (negative content) in as concrete and detailed a way as possible. It is important to acknowledge that, although this mapping of function to classes of RT is consistent with all the evidence reviewed, it is not a definitive account but rather a preliminary framework to organize findings across the RT literature, inform re-analysis of extant findings, and generate further hypotheses.

The key messages of this article are twofold. First, the article extends the explanatory power of previous theorizing about RT by elaborating on the original control theory account of rumination and suggests that the process of RT can be best understood within this framework. As well as providing a theoretical framework to guide future research, this approach has considerable implications for understanding how thinking, action, and emotional state interact. Second, this analysis is of particular relevance to answering the important theoretical and applied question of how RT about upsetting events sometimes leads to effective cognitive processing and problem solving yet at other times exacerbates depression and anxiety.

This review was not meant to be, and clearly could not be, exhaustive. Given the breadth of the literature relevant to RT, it is likely that other factors not mentioned here are compatible with this analysis or could influence the consequences of RT. Furthermore, this review has focused on the processes and mechanisms most directly linked to the different consequences of RT, at the loss of detailed consideration of other factors potentially linked to RT. In particular, biological, interpersonal, neuropsychological, and neurological factors, such as the role of neurotransmitters, functional deficits in inhibitory processes, and functional neuroanatomy, have not been reviewed (e.g., Mayberg, 2006 ; Ray et al., 2005 ; Siegle et al., 2001 ). This is not to argue that these factors do not play a role in influencing RT; it is probable that they do; rather it reflects the fact that there is currently little evidence that these processes influence the consequences of RT, which was the focus of this review. Future research would usefully examine these factors in relationship to the consequences of RT and, in particular, with reference to the control theory elaborated here. Nonetheless, I hope that the integrative framework elucidated here provides a novel and useful theoretical organization that will facilitate research on the mechanisms underpinning RT and also provides the first tentative answers to the highly significant question of “What determines whether RT leads to constructive or unconstructive consequences?”

Acknowledgments

This research was supported by Project Grants 065809 and 080099 from the Wellcome Trust, United Kingdom.

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