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Experimental Research on Dreaming: State of the Art and Neuropsychoanalytic Perspectives

Perrine m. ruby.

1 INSERM U1028, Lyon Neuroscience Research Center, Brain Dynamics and Cognition Team, Lyon, France

2 CNRS UMR5292, Lyon Neuroscience Research Center, Brain Dynamics and Cognition Team, Lyon, France

3 University Lyon 1, Lyon, France

Dreaming is still a mystery of human cognition, although it has been studied experimentally for more than a century. Experimental psychology first investigated dream content and frequency. The neuroscientific approach to dreaming arose at the end of the 1950s and soon proposed a physiological substrate of dreaming: rapid eye movement sleep. Fifty years later, this hypothesis was challenged because it could not explain all of the characteristics of dream reports. Therefore, the neurophysiological correlates of dreaming are still unclear, and many questions remain unresolved. Do the representations that constitute the dream emerge randomly from the brain, or do they surface according to certain parameters? Is the organization of the dream’s representations chaotic or is it determined by rules? Does dreaming have a meaning? What is/are the function(s) of dreaming? Psychoanalysis provides hypotheses to address these questions. Until now, these hypotheses have received minimal attention in cognitive neuroscience, but the recent development of neuropsychoanalysis brings new hopes of interaction between the two fields. Considering the psychoanalytical perspective in cognitive neuroscience would provide new directions and leads for dream research and would help to achieve a comprehensive understanding of dreaming. Notably, several subjective issues at the core of the psychoanalytic approach, such as the concept of personal meaning, the concept of unconscious episodic memory and the subject’s history, are not addressed or considered in cognitive neuroscience. This paper argues that the focus on singularity and personal meaning in psychoanalysis is needed to successfully address these issues in cognitive neuroscience and to progress in the understanding of dreaming and the psyche.

The word “dream” is commonly used to express an unattainable ideal or a very deep and strong desire:

I have a dream that my four little children will one day live in a nation where they will not be judged by the color of their skin, but by the content of their character. Martin Luther King

In dream reports, however, one often notices banal situations, strange scenes, or even frightening events. Why is there such a contrast between the popular meaning of the word “dream” and the content of dream reports? Why are some dream scenes so bizarre? Are dreams built from images that arise randomly from the sleeping brain? Or is the emergence and organization of dream images controlled by currently unknown parameters? Does dreaming have a function?

Answering these questions is not easy because dreaming is elusive. We still do not know when it happens during the night, how long it lasts, whether we can recall its entire content, or how to control it. For more than a century, such limited understanding of dreaming has seriously hampered experimental investigations. Nonetheless, scientific research has managed to produce considerable information about the phenomenology and physiology of dreaming and has improved our understanding of this fascinating phenomenon.

Experimental Research on Dreaming

Dreaming and experimental psychology, dream content.

Dreaming was first investigated on an experimental level in the nineteenth century. Calkins ( 1893 ) published the first statistical results about dreaming and argued that some aspects of dream content could be quantified. Later, questionnaires and automatic analysis of the lexical content of dream reports allowed psychologists to show that dream content has some precise phenomenological characteristics. According to psychological studies (Hall and Van de Castle, 1966 ; Schwartz, 1999 ), visual imagery occurs more frequently in dreams than imagery of other senses (audition, olfaction, touch, and taste); the dream drama is mostly lived by the dreamer from a first-person perspective; some elements of real-life events previously experienced by the dreamer often contribute to the scene of the dream; most often, the dream sequence is not within the dreamer’s voluntary control (i.e., the dreamer may be convinced during the dream that the dream’s story is really happening); temporal and spatial incoherencies can occur in the dream story; the dream report is often full of people interacting with each other (e.g., discussions, fights, pursuit, sexuality); and finally, the dream report often contains strong emotions.

Substantial variability of content exists, however, among the same individual’s dreams and among the dreams of different individuals. Further, psychological studies have shown that many internal and external parameters can influence dream content. For example, males report more aggression and violence in their dreams than do females (Nielsen et al., 2003 ; Schredl et al., 2004 ). External stimulation perceived by the dreamer can be incorporated into dreams (Koulack, 1969 ; Saint-Denys, 1867; Hoelscher et al., 1981 ), as illustrated by the famous Dali painting Dream Caused by the Flight of a Bee around a Pomegranate a Second before Awakening . The current concerns of the subject may also be found in the content of his/her dreams (Schwartz, 1999 ; Domhoff and Schneider, 2008 ), and many aspects of the subject’s daily life were found to influence dream content, including news events (Bulkeley and Kahan, 2008 ), musical practice (Uga et al., 2006 ), religious beliefs (Domhoff and Schneider, 2008 ), chronic pain (Raymond et al., 2002 ), mood (Cartwright et al., 1998a ), or a violent living environment (Valli et al., 2005 ). By contrast, congenital or acquired malformations do not seem to significantly influence dream content (Voss et al., 2010 ; Saurat et al., 2011 ).

Based on these results, two opposing hypotheses were formulated: the continuity hypothesis (Schredl and Hofmann, 2003 ) and the discontinuity hypothesis (Rechtschaffen, 1978 ; Kahn et al., 1997 ; Stickgold et al., 2001 ). The former relies on results showing that the themes of an individual’s thoughts during waking life and dreaming are similar; the latter focuses on the fundamentally different structures of thoughts during waking life and dreaming. Voss et al. ( 2010 ) stressed in their recent paper that these hypotheses represent oversimplified approaches to dream analysis and argued that waking and dreaming thoughts were related but structurally independent; in other words, she argued in favor of merging the continuity and discontinuity hypotheses.

Dream report frequency

Dream report frequency (DRF) can vary within subjects and varies substantially among subjects. In a study of 900 German subjects with a large age range from various socioprofessional categories, the mean DRF was approximately 1 dream report per week (Schredl, 2008 ). This result shows that the dream experience is common and familiar to everyone. Psychological studies have demonstrated that many parameters covary with DRF and may thus influence it.

Sleep parameters

First, DRF varies according to the sleep stage preceding awakening (e.g., Dement and Kleitman, 1957b ; Nielsen, 2000 , for a review). More dream reports are obtained after an awakening during rapid eye movement (REM) sleep than after an awakening during non-REM (NREM) sleep. These results inspired the REM sleep hypothesis of dreaming (see the section Dreaming and Neuroscience). Second, DRF increases with the number of awakenings during sleep, according to retrospective self-evaluations of awakenings (Cory and Ormiston, 1975 ; Schredl et al., 2003 ). Such studies showed that the more the subjects tended to awaken during sleep, the higher their DRF. These results support the hypothesis of Koulack and Goodenough ( 1976 ), which proposes that nocturnal awakenings facilitate the encoding of the dream in memory and thus facilitate dream recall upon awakening. However, this hypothesis has not been tested by measuring awakenings with polysomnographic recordings in healthy subjects with various DRFs. Finally, DRF varies according to the method of awakening. Abrupt awakenings lead to more dream reports than gradual awakenings (Shapiro et al., 1963 , 1965 ; Goodenough et al., 1965 ).

Physiological and environmental parameters

Dream report frequency deceases with age (e.g., Schredl, 2008 ) and tends to be slightly higher among females than males (e.g., Schredl, 2008 ; Schredl and Reinhard, 2008 ). Remarkably, Schredl’s ( 2008 ) results revealed that DRF also varied according to the size of the subject’s place of residence.

Psychological parameters

First, increased professional stress or interpersonal stress resulted in an increase in DRF (for a review, see Schredl, 1999 ). Second, an interest in dreams or a positive attitude toward dreams clearly covaries with DRF (Hill et al., 1997 ; Schredl, 1999 ; Schredl et al., 2003 ). The greater an individual’s interest in dreams, the higher his/her DRF. Third, several cognitive abilities have been found to covary with DRF. Contradictory results have been reported for the correlation between DRF and memory abilities (short-term, long-term, visual, verbal, implicit, and explicit; significant positive correlation: Cory and Ormiston, 1975 ; Belicki et al., 1978 ; Butler and Watson, 1985 ; Schredl et al., 1995 ; Solms, 1997 ; no significant correlation: Cohen, 1971 ; Belicki et al., 1978 ; Schredl et al., 1995 , 1997 , 2003 ; Solms, 1997 ) and the correlation between DRF and visual imagery ( significant positive correlation : Hiscock and Cohen, 1973 ; Richardson, 1979 ; Okada et al., 2000 ; no significant correlation : Hill et al., 1997 ; Okada et al., 2000 ). However, several studies have consistently shown that DRF is positively correlated with creativity (Fitch and Armitage, 1989 ; Schredl, 1999 ; Schredl et al., 2003 ) and intelligence scales (multiple-choice vocabulary test, Schonbar, 1959 ; Shipley Intelligence Scale, Connor and Boblitt, 1970 ). Finally, many authors have reported a correlation between DRF and personality traits. Subjects with a high DRF are more likely to have a personality with thinner boundaries (Hartmann described people with thin boundaries as being open, trustworthy, vulnerable, and sensitive; Hartmann, 1989 ; Hartmann et al., 1991 ; Schredl et al., 2003 ), to be more anxious (Schonbar, 1959 ; Tart, 1962 ), to have a higher level of absorption (the absorption scale measures the capacity to become absorptively involved in imaginative and esthetic experiences; Hill et al., 1997 ; Schredl, 1999 ; Schredl et al., 2003 ), to be more open to experience (Hill et al., 1997 ; Schredl et al., 2003 ), and to be less alexithymic (alexithymia is a personality variable that incorporates difficulty identifying and describing feelings, difficulty distinguishing between feelings and the physical sensation of emotional arousal, limited imaginative processes, and an externally oriented cognitive style; De Gennaro et al., 2003 ; Nielsen et al., 2011 ) compared to subjects with a low dream recall frequency. However, those results have not always been reproducible (e.g., Schredl, 2002 for openness to experience; Cory and Ormiston, 1975 ; Hill et al., 1997 for anxiety; Nielsen et al., 1997 for alexithymia) and, according to the recent review by Blagrove and Pace-Schott ( 2010 ), it is difficult to draw conclusions about a possible link between personality traits and DRF.

In conclusion, numerous parameters have been identified that covary with DRF. Schredl stressed in many of his papers that the studied parameters usually explain only a small percentage of the total variance (e.g., Schredl, 2008 ). Thus, the DRF variation profile suggests that the production, encoding and recall of dreams are influenced by numerous parameters that probably interact with each other.

Dreaming and neuroscience

The neuroscientific approach to dreaming arose at the end of the 1950s with the discovery of REM during human sleep by the American physiologist Nathaniel Kleitman and his team (Aserinsky and Kleitman, 1953 ; Dement and Kleitman, 1957a ). During these sleep episodes with saccades, the researchers noticed a decrease in voltage and an increase in frequency in the EEG, accompanied by an increase in cardiac frequency variability and a decrease in body movements. They concluded that these physiological modifications indicate a particular sleep stage, which they called REM sleep. A few years later, the French team led by neurobiologist Michel Jouvet discovered that the lack of movement during REM sleep in cats was due to a general muscular atonia, controlled notably by the locus coeruleus α in the brainstem (Jouvet and Michel, 1959 ; Berger, 1961 later showed that muscular atonia during REM sleep also occurs in humans). Interestingly, the inability to move during REM sleep indicates deep sleep and paradoxically, the fast EEG activity of REM sleep resembles EEG activity in wakefulness. Jouvet concluded that this particular physiological state is associated with a “third state” of the brain (in addition to the brain states associated with wakefulness and NREM sleep) which he called “paradoxical sleep” instead of “REM sleep” (Jouvet et al., 1959 ; Jouvet, 1992 ). Several years later, Fisher et al. ( 1965 ) discovered another physiological characteristic of REM sleep: the penile erection.

During the same period, the American team noticed that a subject awakened during REM sleep very often reported a dream (80% of awakenings in REM sleep vs. 6% of awakenings in NREM sleep are followed by a dream report, according to Dement and Kleitman, 1957b ). Researchers concluded that dreaming occurs during REM sleep. The eye movements of REM sleep would allow the dreamer to scan the imaginary scene of the dream (the scanning hypothesis); the cerebral cortex activation revealed by the rapid EEG would allow intense cognitive activity, creating the complex stories of a dream; and the lack of muscle tone would prevent the dreamer from acting out his dreams. From that time on, researchers investigated REM sleep to obtain answers about dreaming.

In the 1990s, researchers used functional neuroimaging techniques such as positron emission tomography (PET) to investigate brain activity during REM sleep in humans. This new approach enabled researchers to demonstrate that the functional organization of the brain during REM sleep is different from the functional organization of the brain during wakefulness (Maquet et al., 1996 ; Braun et al., 1998 ). In comparison to wakefulness, brain activity during REM sleep is decreased in some brain regions (e.g., in the dorsolateral prefrontal cortex; Braun et al., 1998 ) and increased in other regions (e.g., in the occipital and temporal cortex, the hippocampus and parahippocampus, the anterior cingulate, the precentral and postcentral gyri, the superior parietal cortex, and the pons; Braun et al., 1998 ; Maquet et al., 2000 ). Looking more generally for brain activity correlating with REM sleep (the vigilance states considered included wakefulness, slow-wave sleep, and REM sleep), Maquet et al. ( 1996 ) found negative correlations in the precuneus, posterior cingulate cortex, temporoparietal junction, and dorsolateral prefrontal cortex and positive correlations in the amygdala, anterior cingulate, postcentral gyrus, thalamus, and pons (see Schwartz and Maquet, 2002 ; Maquet et al., 2005 ; Nir and Tononi, 2010 for reviews). Based on these results, researchers argued that the particular functional organization of the brain during REM sleep could explain the phenomenological characteristics of dream reports (Hobson and Pace-Schott, 2002 ; Schwartz and Maquet, 2002 ; Maquet et al., 2005 ; Nir and Tononi, 2010 ). They considered that brain activity increases and decreases during REM sleep could be interpreted on the basis of what we know about brain activity during wakefulness. In this context, the increased occipital cortex activity during REM sleep could explain the visual component of dream reports because neuroimaging results during wakefulness showed that visual imagery with the eyes closed activates the occipital cortex (Kosslyn and Thompson, 2003 ). The decreased activity in the temporoparietal junction during REM sleep may explain why dreams are mainly experienced in the egocentric coordinates of the first-person; indeed, during wakefulness, activity in the temporoparietal junction was reported to be greater for allocentric vs. egocentric representation (e.g., Ruby and Decety, 2001 ; Zacks et al., 2003 ) and for third- vs. first-person perspective (e.g., Ruby and Decety, 2003 , 2004 ). The increased activity in the hippocampus during REM sleep could explain why dreams are often composed of known images or characters, as the hippocampus is known to be associated with the encoding and retrieval of lived events during wakefulness (e.g., Piolino et al., 2009 ). The decreased activity in the lateral prefrontal cortex during REM sleep could explain why dream stories lack consistency, why the dreamer’s perception of time is altered, why the dream story is beyond the control of the dreamer and why the dreamer is convinced that the dream story is really happening. Indeed, during wakefulness, the lateral prefrontal cortex is involved in executive function, cognitive control, and working memory (Petrides, 2005 ; Koechlin and Hyafil, 2007 ). The increased activity in the medial prefrontal cortex during REM sleep could explain the attribution of thoughts, beliefs, and emotions to the characters in the dream because, during wakefulness, the medial prefrontal cortex is known to participate in mind reading (Ruby et al., 2007 , 2009 ; Legrand and Ruby, 2009 ). The increased activity in the motor cortex (precentral gyrus) during REM sleep could explain the movements of the characters’ bodies in the dream because, during wakefulness, motor imagery, and the imagination of someone’s action from the third-person perspective involve the precentral gyrus (Decety et al., 1994 ; Ruby and Decety, 2001 ). Finally, the amygdala’s activity during REM sleep could explain why emotions, especially fear, are often mentioned in dream reports; indeed, the amygdala is involved in the processing of emotional stimuli during wakefulness (Adolphs, 2008 ).

In conclusion, results from experimental psychology and neuroscience allow us to better understand the phenomenology of dreaming and the cerebral correlates of some characteristics of dream reports. Still, what do they tell us about the role of dreaming? What are the current hypotheses about dream function(s)?

Hypotheses about dream function(s)

No function.

At the end of the twentieth century, the neurologist Alan Hobson, who was profoundly anti-psychoanalysis, proposed a theory that deprived dreaming of any function. Hobson argued that dreaming is an epiphenomenon of REM sleep: “Because dreams are so difficult to remember, it seems unlikely that attention to their content could afford much in the way of high-priority survival value. Indeed, it might instead be assumed that dreaming is an epiphenomenon of REM sleep whose cognitive content is so ambiguous as to invite misleading or even erroneous interpretation” (Hobson et al., 1998 ).

Psychological individualism

In contrast, other teams, like Michel Jouvet’s, believed that dreaming serves a vital function. In 1979, Jouvet’s team blocked muscular atonia during REM sleep in a cat by damaging the locus coeruleus α in its brainstem. This lesion resulted in the appearance of movements during REM sleep. Movies from the Jouvet lab show sleeping cats performing complex motor actions (with altered control and coordination) resembling those of wakefulness, such as fur licking, growling, chasing prey, mastication, and fighting. From these videos, the authors concluded that the cat was acting out its dream, and they called this non-physiological state “oneiric behavior” (Sastre and Jouvet, 1979 ). These results led Jouvet to propose that dreaming plays a role in reinforcing a species’ typical behavior. Later in his career, Jouvet moved toward a hypothesis focusing on the role of dreaming in the individual dimension. He speculated that dreams (note that, for Jouvet, dreams and paradoxical sleep were equivalent) could be involved in psychological individualism and in the stability of the dreamer’s personality (Jouvet, 1991 , 1992 , 1998 ). According to Jouvet, “the brain is the sole organ of homeotherms that do not undergo cell division. We thus have to explain how certain aspects of psychological heredity (found in homozygote twins raised in different surroundings) may persist for a whole life (psychological individuation). A definitive genetic programming during development (by neurogenesis) is unlikely due to the plasticity of the nervous system. That is why we have to consider the possibility of an iterative genetic programming. The internal mechanisms (synchronous) of paradoxical sleep (SP) are particularly adapted to such programming. This would activate an endogenous system of stimulation that would stimulate and stabilize receptors genetically programmed by DNA in some neuronal circuits. The excitation of these neurons during SP leads to oniric behaviors that could be experimentally revealed – the lists of these behaviors are specific to each individual and indirect data suggest a genetic component of this programming. Amongst the mechanisms allowing the iterative programming of SP, sleep is particularly important. Security – and hence the inhibition of the arousal system – is a sine qua non-condition for genetic programming to take place. In that sense, sleep could very well be the guardian of dreaming” (Jouvet, 1991 ). In other words, Jouvet’s hypothesis is that paradoxical sleep restores neuronal circuitry that was modified during the day to preserve the expression of the genetic program that codes for psychological characteristics. This process would ensure the stability of personality across time.

The threat simulation theory

The Finnish psychologist Antti Revonsuo recently proposed a hypothesis called threat simulation theory, which explains the fearful characteristics of dream content (Revonsuo, 2000 ; Valli and Revonsuo, 2009 ). According to this theory, dreams serve as virtual training places to improve threat avoidance or threat fighting ability. The theory postulates that such nocturnal training makes the dreamer more efficient at resolving threatening situations during wakefulness.

Emotional regulation

Cartwright et al. ( 1998a , b ) defended the idea that dreaming is involved in emotional regulation. Her team showed that, in healthy subjects, the depression level before sleep was significantly correlated with affect in the first REM report. Her team also observed that low scorers on the depression scale displayed a flat distribution of positive and negative affect in dreams, whereas those with a depressed mood before sleep showed a pattern of decreasing negative and increasing positive affect in dreams reported from successive REM periods (Cartwright et al., 1998a ). These results led Cartwright’s team to suggest that dreaming may actively moderate mood overnight in normal subjects. The team strengthened this hypothesis by showing that among subjects who were depressed because of a divorce, those who reported more negative dreams at the beginning of sleep and fewer at the night’s end were more likely to be in remission 1 year later than subjects who had fewer negative dreams at the beginning of sleep and more at the end of the night (Cartwright et al., 1998b ). The researchers concluded that negative dreams early in the night may reflect a within-sleep mood regulation process, whereas those that occur later may indicate a failure in the completion of this process.

Memory consolidation

Finally, a current mainstream hypothesis in cognitive neuroscience credits sleep and dreaming with a role in memory consolidation (for a recent review, see Diekelmann and Born, 2010 ). Numerous studies have shown that brain activity during training is replayed during post-training sleep (e.g., using a serial reaction time task Maquet et al., 2000 , demonstrated replay during REM sleep; using a maze exploration task Peigneux et al., 2004 , demonstrated replay during slow-wave sleep). Decreased performance during the post-training day in sleep-deprived subjects further suggested that the replay of brain activity at night contributes to memory consolidation (e.g., Maquet et al., 2003 ). Only recently, however, have experimental results in humans argued in favor of a role of dreaming per se in memory consolidation. In one study, subjects were trained on a virtual navigation task before taking a nap. Post-nap tests showed that subjects who dreamed about the task performed better than subjects who did not dream (note that only 4 out of 50 subjects dreamed about the task in this study; Wamsley et al., 2010 ). Using a different approach, Nielsen and colleagues provided additional arguments supporting a link between dreams and memory (Nielsen et al., 2004 ; Nielsen and Stenstrom, 2005 ). This team demonstrated that dreams preferably incorporate events that the dreamer lived the day before and events that the dreamer lived 7 days before the dream (U shaped curve). Animal studies have shown that after associative learning, the excitability of hippocampal cells increases (which leads to an increase in neuronal plasticity) and then returns to baseline 7 days after training (Thompson et al., 1996 ). The similarity between the delay of episodic event incorporation into dreams and the delay of post-training cellular plasticity in the hippocampus led the Canadian team to suggest a link between dreaming and episodic memory consolidation.

In summary, the preceding section describes the current state of the art on dreaming, its phenomenology and cerebral correlates and hypotheses about its functions. Some substantial advances have been made, but much remains to be understood.

Unresolved Issues

The link between oneiric behaviors and dream reports.

A piece of evidence in favor of a strong link between REM sleep and dreaming is the oneiric behavior (the appearance of complex motor behaviors when motor inhibition is suppressed during REM sleep) discovered by Sastre and Jouvet ( 1979 ) in cats and reproduced by Sanford et al. ( 2001 ) in rats. Researchers interpreted these results as the animal acting out its dream. However, as animals do not talk, the link between oneiric behavior and dream recall cannot be tested experimentally. This limitation seriously hampers our understanding of dreaming. In humans, complex motor behaviors (e.g., talking, grabbing, and manipulating imaginary objects, walking, and running) can also occur during REM sleep in a pathological context. This syndrome is called REM sleep behavior disorder (RBD). It can be caused by substance withdrawal (e.g., alcohol, Nitrazepam) or intoxication (e.g., caffeine, tricyclic antidepressants) or by various diseases (e.g., Parkinson’s and Alzheimer’s diseases, pontine neoplasms). According to physicians experts on this syndrome, some patients report dreams that are consistent with their behaviors in REM sleep (Mahowald and Schenck, 2000 ). According to the literature, however, such matches seem to be loose and not systematic. Only one study has tested whether observers can link dream content to sleep behaviors in RBD (Valli et al., 2011 ). In this study, each video recording of motor manifestations was combined with four dream reports, and seven judges had to match the video clip with the correctly reported dream content. The authors found that reported dream content can be linked to motor behaviors at a level better than chance. However, only 39.5% of video-dream pairs were correctly identified. Note, however, that because the authors obtained only movements and not behavioral episodes for many RBD patients, the link between videos and dream reports was unfairly difficult to make.

It is important to note that motor behavior during sleep can happen outside of REM sleep. Sleepwalking and sleep terrors, which occur during NREM sleep, are usually not considered dream enactments. However, we know that dreams can happen during NREM sleep, and many patients report dreamlike mentation after awakening from sleepwalking or sleep terrors (71%, according to Oudiette et al., 2009 ). In addition, Oudiette et al. ( 2009 ) reported that the dreamlike mentation can correspond with the sleep behavior in NREM sleep. Consequently, the authors concluded that sleepwalking may represent an acting out of corresponding dreamlike mentation.

Recent research suggests that any kind of motor behavior during sleep can be considered an oneiric behavior. One of the challenges for future research is to test the strength of the link between these oneiric behaviors and dream reports in a controlled and systematic way.

Neurophysiological correlates of dreaming

Despite the numerous neuroimaging studies of sleep in humans, the neurophysiological correlates of dreaming remain unclear.

Indeed, dreaming can happen during NREM sleep, and although NREM brain activity differs substantially from REM sleep brain activity (Maquet et al., 2000 ; Buchsbaum et al., 2001 ), some NREM dreams are phenomenologically indistinguishable from REM dreams (Hobson, 1988 ; Cavallero et al., 1992 ; Cicogna et al., 1998 ; Wittmann et al., 2004 ). This phenomenon is difficult to understand given what we currently know about the sleeping brain and about dreaming. One explanation may rely on the possibility that brain activity during sleep is not as stable as we think.

Brain activity during REM sleep in humans is considered to be well understood (Hobson and Pace-Schott, 2002 ; Schwartz and Maquet, 2002 ; Nir and Tononi, 2010 ), but several results question this notion. First, contrary to the common belief that dorsolateral prefrontal cortex activity decreases during REM sleep, several studies have reported increased activity in the dorsolateral prefrontal cortex during REM sleep (Hong et al., 1995 , 2009 ; Nofzinger et al., 1997 ; Kubota et al., 2011 ). Second, brain activity during REM sleep is heterogeneous. The mean regional cerebral blood flow during 1 min of REM sleep (e.g., as reported in Maquet et al., 1996 ) and the regional cerebral blood flow associated with the rapid eye movements of REM sleep (Hong et al., 2009 ; Miyauchi et al., 2009 ) highlight different brain regions. Finally, few congruencies have been noted in the results of studies investigating brain activity during REM sleep (Hong et al., 1995 , 2009 ; Maquet et al., 1996 , 2000 ; Braun et al., 1997 , 1998 ; Nofzinger et al., 1997 ; Peigneux et al., 2001 ; Wehrle et al., 2005 ; Miyauchi et al., 2009 ; Kubota et al., 2011 ), even between studies using the same technique and the same contrasts (e.g., Braun et al., 1998 ; Maquet et al., 2000 ), or between studies investigating the same REM event (e.g., brain activity associated with rapid eyes movements, as in Peigneux et al., 2001 ; Wehrle et al., 2005 ; Hong et al., 2009 ; Miyauchi et al., 2009 ). Furthermore, few brain regions are consistently reported across the majority of the studies. This inconsistency suggests great intra- and intersubject variability in brain activity during REM sleep in humans. A challenge for future research will be to find out whether the variability in brain activity during REM sleep can be explained by the variability in dream content.

Because dream reports can be collected after awakenings from any sleep stage, one may hypothesize that the brain activity that subserves dreaming (if such brain activity is reproducible across dreams) is quite constant throughout the night and can be observed during all sleep stages. Some results have supported this hypothesis and encouraged further attention in this direction. Buchsbaum et al. ( 2001 ), for example, reported that metabolism in the primary visual areas and certain parts of the lateral temporal cortex does not fluctuate much across REM and slow-wave sleep. Similarly, Nielsen’s team found that dream recall (vs. no dream recall) was associated with decreased alpha (8–12 Hz) power in the EEG preceding awakening, regardless of the sleep stage (Stage 2 or REM sleep; Esposito et al., 2004 ). Interestingly, some authors have suggested that decreased power in the alpha band during wakefulness reflects search and retrieval processes in long-term memory (for a review, see Klimesch, 1999 ).

Processes of selection and organization of dream representations

Nielsen’s team found that episodic events from the 1, 7, and 8 days before a dream were more often incorporated into the dream than were events from 2 or 6 days before the dream (Nielsen et al., 2004 ; results reproduced by Blagrove et al., 2011 ). This result tells us that internal processes control and shape dream content and thus help us to constrain and shape hypotheses about the function and biological basis of dreaming.

At the end of the nineteenth century, Saint-Denys (1867) showed that a sensory stimulus (e.g., the scent of lavender) presented to a sleeping subject without his or her knowledge could induce the incorporation of an event associated with the stimulus (e.g., holidays spent near a lavender field) into the dream, regardless of the delay between the dream and the association stimulus/events (lavender scent/holidays). The author demonstrated that the external world can influence dream content in a direct or indirect way.

Finally, it appears that both external and internal parameters can shape or govern dream content. Nonetheless, few of these parameters are known, and some regularities in the phenomenology of dreams suggest that more influencing parameters remain to be discovered. For example, some individuals experience recurring themes, characters, or places in their dreams. In line with this observation, Michael Schredl’s team showed that the content and style of a person’s life strongly influence dream content (Schredl and Hofmann, 2003 ). However, the rule(s) governing which lived events are incorporated into dreams remain unknown. Do the representations constituting the dream emerge randomly from the brain, or do they surface according to certain parameters? Similarly, is the organization of the dream’s representations chaotic, or is it determined by rules? Does dreaming have a meaning? What is/are the function(s) of dreaming?

Dreaming, Psychoanalysis, and Neuropsychoanalysis

Psychoanalysis, which was developed by the neurologist Sigmund Freud in the beginning of the twentieth century, proposes answers to the questions raised above. Indeed, his theory of the human mind comprises hypotheses about the rules of selection and organization of the representations that constitute dreams.

At the beginning of the twentieth century, Freud presented the concept of the unconscious. He proposed that a part of our mind is made up of thoughts, desires, emotions, and knowledge that we are not aware of, but that nevertheless profoundly influence and guide our behaviors. In his books (e.g., Freud, 1900, 1920 ), Freud proposes that the unconscious mind comes out in slips and dreams. Its expression, however, is coded within dreams (the work of dream), and unconscious thoughts are distorted before they emerge in the conscious mind of the sleeping subject (manifest content of the dream). As a consequence, the dreamer is not disturbed by repressed and unacceptable thoughts (latent content of the dream) and can continue sleeping (this is the reason why Freud considered dreams the guardians of sleep). Hence, according to Freud, decoding dreams’ latent content provides an access to the unconscious mind.

In Freud’s theory of the mind, unconscious thoughts and feelings may cause the patient to experience life difficulties and/or maladjustment, and free unconscious thoughts can help the patient gain insight into his/her situation. As a consequence, Freud developed techniques to decode dreams and provide a way for an analyst to look inside the words and unconscious images of the patient, and to free them through patient insight. One of these techniques is called free association, and is regarded as an essential part of the psychoanalytic therapy process. In order for an analyst to get to the latent content of a dream, he requires the patient to discuss the dream’s manifest content and encourage free association about the dream. Free association is the principle that the patient is to say anything and everything that comes to mind. This includes decensoring his/her own speech so that he/she truly expresses everything. Over time, the therapist or analyst will draw associations between the many trains of uncensored speech the patient shares during each session. This can lead to patient insight into their unconscious thoughts or repressed memories, and the accomplishment of their ultimate goal of “freedom from the oppression of the unconscious” (Trull, 2005 ).

Hence, Freud considered that dreams, as well as slips, have a meaning and can be interpreted, so that one is justified in inferring from them the presence of restrained or repressed intentions (Freud, 1900, 1920 ). Note that, in Freud’s theory of the mind, the words “meaning” and “intention” are closely linked: “Let us agree once more on what we understand by the ‘meaning’ of a psychic process. A psychic process is nothing more than the purpose which it serves and the position which it holds in a psychic sequence. We can also substitute the word ‘purpose’ or ‘intention’ for ‘meaning’ in most of our investigations” (Freud, 1920 ).

In other words, according to Freud, decoding dreams with the free association method provides an access to what makes each of us so special, uncorvering the forces that guide one’s behavior. It gives access to an unknown dimension of ourselves that is fundamental in understanding who we are. It provides access to personal meaning.

This hypothesis, attributing significant importance and meaning to dreams, has rarely been considered by neuroscientists who often consider Freud’s work and theory unscientific.

However, this situation may change as the relationship between psychoanalysis and neuroscience evolves. The starting point was the creation of the International Society for Neuropsychoanalysis in 2000. It was founded by neuropsychologist and psychoanalyst Mark Solms with the intention to promote interactions and collaborations between psychoanalysis and neuroscience. The challenge was serious, as illustrated by neuroscientist Alan Hobson’s aggressiveness in the famous dream debate (Alan Hobson vs. Mark Solms) entitled “Should Freud’s dream theory be abandoned?” held in Tucson, Arizona, in 2006 during the Towards a Science of Consciousness meeting (scientific arguments can be found in Solms, 2000 and Hobson et al., 2000 ). Alan Hobson tried to convince the assembly that Freud was 100% wrong and that Freud’s dream theory was misguided and misleading and should be abandoned. He aimed to demonstrate that Freud’s dream theory is incompatible with what we know about how the brain works. He added that Freud’s dream theory was not scientific because it was not testable or falsifiable. Finally, he presented his model of dreaming, the activation-synthesis hypothesis (Hobson and McCarley, 1977 ; Hobson et al., 2000 ): “The Activation-Synthesis model of dream construction proposed that the phasic signals arising in the pontine brainstem during REM sleep and impinging upon the cortex and limbic forebrain led directly to the visual and motor hallucinations, emotion, and distinctively bizarre cognition that characterize dream mentation. In doing so, these chaotically generated signals arising from the brain stem acted as a physiological Rorschach test, initiating a process of image and narrative synthesis involving associative and language regions of the brain and resulting in the construction of the dream scenarios.” In contrast, Mark Solms demonstrated that what is currently known about the dreaming brain is at least broadly consistent with Freud’s dream theory. He argued that it is generally accepted that brain stem activation is necessary, but not sufficient, to explain the particular characteristics of dream consciousness. What does explain the particular characteristics of dream consciousness, according to Solms, are the following features of brain activity during REM sleep (Braun et al., 1997 ): the activation of core forebrain emotion and instinctual drive mechanisms, i.e., the limbic and paralimbic brain areas (the anterior cingulate, insula, hippocampus, parahippocampal gyrus, and temporal pole), and of the posterior perceptual system (the fusiform gyrus, superior, inferior and middle temporal gyrus, and angular gyrus) and the deactivation of executive dorsolateral frontal control mechanisms (the dorsolateral prefrontal cortex). He further argued that his lesion studies (Solms, 1997 ) are congruent with neuroimaging results because they showed that a total cessation of dreaming results from lesions in the medial part of the frontal lobe and in the temporoparietal junction (whereas no cessation of dreaming was observed for core brainstem lesions or for dorsolateral prefrontal lesions). Finally he emphasized that the activation of motivational mechanisms (such as drives and basic emotions) and of posterior perceptual system associated with deactivation of the executive control (i.e., reality oriented regulatory mechanism) during REM sleep, is broadly consistent with Freud’s dream theory which claims that our instinctual drive states (notably appetitive and libidinal drive system) are relatively disinhibited during sleep. Note that experimental results demonstrating the existence of unconscious representations that guide behavior (e.g., Shevrin and Fritzler, 1968 ; Bunce et al., 1999 ; Arminjon, 2011 , for a review) could also have been cited in support of Freud’s dream theory. This debate was a success for Mark Solms and neuropsychoanalysis. Indeed, at the end of the debate, approximately 100 people voted “no” (i.e., “Freud’s dream theory should not be abandoned”), approximately 50 people voted “yes” and 50 voted “I don’t know”.

Solms’ ( 1997 , 2000 ) approach to dreaming and his experimental results fundamentally challenged our current understanding of dreaming. He proposes that dreaming and REM sleep are controlled by different brain mechanisms. According to Solms, REM sleep is controlled by cholinergic brain stem mechanisms, whereas dreaming is mediated by forebrain mechanisms that are probably dopaminergic. This implies that dreaming can be activated by a variety of NREM triggers. Several experimental results support this hypothesis.

First, behavioral studies have demonstrated that the link between REM sleep and dream reports is lax. Subjects awakened during NREM sleep can recall dreams at a high rate (Foulkes, 1962 : 74% of awakenings in NREM sleep were followed by dream reports; Cavallero et al., 1992 : 64%; Wittmann et al., 2004 : 60%); dreams can be recalled after a nap consisting only of NREM sleep (Salzarulo, 1971 ; Palagini et al., 2004 ); and some individuals never recall dreams, even when awakened from REM sleep (Pagel, 2003 ). In addition, in healthy subjects with a normal dream recall frequency (around 1 dream recall per week, Schredl, 2008 ), dream recall after an awakening during REM sleep is not systematic: 5–30% of awakenings in REM sleep are not followed by a dream recall, according to the literature (e.g., Dement and Kleitman, 1957a , b ; Foulkes, 1962 ; Hobson, 1988 ). Finally, 5–10% of NREM dreams cannot be distinguished from REM dreams based on their content (Hobson, 1988 ; Cavallero et al., 1992 ; Cicogna et al., 1998 ; Wittmann et al., 2004 ).

Second, as Solms ( 2000 ) argued, the amount of dream recall can be modulated by dopamine agonists (Scharf et al., 1978 ; Nausieda et al., 1982 ) without concomitant modification of the duration and frequency of REM sleep (Hartmann et al., 1980 ). Dream recall can be suppressed by focal brain lesions (at the temporo-parieto-occipital junction and ventromedial prefrontal cortex; Solms, 1997 , 2000 ). These lesions do not have any appreciable effects on REM frequency, duration, or density (Kerr et al., 1978 ; Michel and Sieroff, 1981 ). Finally, some clinical studies suggest that a dream can be triggered by nocturnal seizures in NREM sleep, i.e., by focal brain stimulation. Some cases of recurring nightmares caused by epileptiform activity in the temporal lobe have indeed been reported (Solms, 2000 ).

Conclusion: Collaboration between Neuroscience and Psychoanalysis Would Benefit Dream Research

Considering the issues that remain unresolved (e.g., neurophysiologic variability, parameter(s) influencing the emergence of representations in dreams, the meaning of dreams), a psychoanalytic perspective would certainly benefit dream research by providing new directions/leads and helping to reach a comprehensive understanding of dreaming.

On the one hand, psychological research has demonstrated that dream content is influenced by one’s personal life, especially personal concerns (Schwartz, 1999 ; Schwartz and Maquet, 2002 ; Schredl and Hofmann, 2003 ), and some neuroscientists have hypothesized that dreaming is involved in psychological individualism. Thus, both psychology and neuroscience have provided results and hypotheses that validate the possibility that dreaming has something to do with personal and meaningful issues. On the other hand, Freud argued that the unconscious, which guides behaviors and desires, express itself during dreams. The two disciplines’ (cognitive neuroscience and psychoanalysis) convergence on dreaming thus seems obvious; however, very little collaboration has occurred to date.

Note that some experimental studies in psychology have considered the psychoanalytic perspective. For example, Greenberg et al. ( 1992 ) attempted “a research-based reconsideration of the psychoanalytical theory of dreaming.” They evaluated the presence of problems (defined as an expression of negative feeling or any situation evoking such feeling or requiring some change or adaptation) during dreaming and pre- and post-sleep wakefulness in two subjects. They showed that problems occurred very frequently in the manifest dream content and that these problems were nearly systematically related to the problems noted during pre-sleep wakefulness. In addition, they observed that effective dreams (i.e., dreams that presented some solution to the individuals’ problems) were followed by a waking state in which the impact of the problems was diminished, whereas ineffective dreams were followed by the persistence of the problems. This study thus confirmed that personal concerns influence dream content. In addition it provided new results suggesting that dreaming may have some psychological problem-solving function (this result recalls the neuroscientific findings that sleep has a cognitive problem-solving function associated with brain reorganization; e.g., Wagner et al., 2004 ; Darsaud et al., 2011 ). Greenberg et al.’s ( 1992 ) study managed to quantify personal issues and clearly broadened the cognitive neuroscience perspective on dreaming. To proceed further, approaches integrating psychoanalysis and neuroscience must now be developed. Several subjective issues at the core of the psychoanalytic approach, such as the concept of personal meaning, the concept of unconscious episodic memory and the subject’s history, are not addressed or considered in cognitive neuroscience. This limitation hampers the understanding of psychological and neurophysiological functioning in humans. These issues must be addressed, and the expertise of psychoanalysts in singularity and personal meaning is needed to do so in neuroscience and to further the understanding of dreaming and of the psyche.

Conflict of Interest Statement

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

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ORIGINAL RESEARCH article

Impact of weekly physical activity on stress response: an experimental study.

\r\nRicardo de la Vega

  • 1 Department of Physical Education, Sport and Human Movement, Autonomous University of Madrid, Madrid, Spain
  • 2 Didactic and Behavioral Analysis in Sport Research Group, Faculty of Sport Sciences, University of Extremadura, Cáceres, Spain
  • 3 Sport of Studies Center, Rey Juan Carlos University, Madrid, Spain

The aim of this research is focused on analyzing the alteration of the psychophysiological and cognitive response to an objective computerized stress test (Determination Test - DT-, Vienna test System ® ), when the behavioral response is controlled. The sample used was sports science students (N = 22), with a mean age of 22.82 (M age = 22.82; SD years = 3.67; M PhysicalActivity hours/Week = 7.77; SD hours / week = 3.32) A quasi-experimental design was used in which the response of each participant to the DT test was evaluated. The variable “number of hours of physical activity per week” and the variable “level of behavioral response to stress” were controlled. Before and after this test, the following parameters were measured: activation and central fatigue (Critical Flicker Fusion Threshold (CFF Critical flicker fusion ascending and Critical flicker fusion descending; DC potential), and perceived exertion (Central Rating of Perceived Exertion and Peripheral Rating of Perceived Exertion). Significant differences were found in all of the measures indicated. The usefulness of this protocol and the measures used to analyze the stress response capacity of the study subjects are discussed.

Introduction

The analysis of psychophysiological fatigue is considered very important in different contexts ( Lohani et al., 2019 ). In this sense, the consideration of the study of humans’s response to external and internal loads ( Wijesuriya et al., 2007 ; Wilson et al., 2007 ) has become one of the most important research topics. The external loads exerted on the individual are added to their skills and coping strategies, resulting in a level of tolerance and adaptation to each situation ( Folkman and Lazarus, 1988 ). Along the last decades, distinctions are often made between physical and mental fatigue role, indicating clear methodologies for the analysis of physiological fatigue, but with clear limitations in the study of central fatigue, because this is measurable only indirectly, which emphasizes the importance of developing new central fatigue analysis procedures ( Bittner et al., 2000 ).

Throughout the decades of research on this topic, different strategies have been used to evaluate the adaptation to these external and internal loads ( Lazarus, 1990 ; Amann, 2011 ). Thus, for example, a multitude of self-reports and standardized tests have been used ( Britner et al., 2003 ), to which physiological and biological measures have been added ( Arza et al., 2019 ). However, relatively low attention is usually given to the Central Nervous System (CNS)-related mechanisms, which play a major role on the development of fatigue ( Tarvainen et al., 2014 ), but are rarely monitored in the sport and physical activity field ( Valenzuela et al., 2020 ). Most of the studies related to central fatigue to date have focused on the effect it has on performing strenuous physical tasks ( Amann and Dempsey, 2008 ), although over the last few years there has been a notable increase in interest in studying the role of central fatigue in explaining human performance ( Inzlicht and Marcora, 2016 ). In this sense, the psychobiological model based on motivational intensity theory has gained special strength ( Gendolla and Richter, 2010 ). This model emphasizes that perception of effort and potential motivation are the central determinants of task engagement. Both variables are taken into consideration in our research, controlling the involvement in the task (motivation), by applying a computerized test, and analyzing the perception of both central and peripheral effort as detailed in the methodological section.

Two of these measures, which focus the methodological attention of this research due to its great potential in the study of this topic, are the Critical Flicker Fusion Threshold (CFFT), evaluated using one Flicker Fusion instrument ( Vicente-Rodríguez et al., 2020 ), and the DC Potential, evaluated using the OmegaWave technology. The neuro-physiological basis of flicker perception is complex but well established ( Görtelmeyer and Zimmermann, 1982 ). In particular, flickering light directly influences cortical activity. The CFFT was measured using two red light- emitting diodes in binocular foveal fixation. The continuous psychophysical method of limits was employed to determine CFFT ( Woodworth and Schlosberg, 1954 ). The utility of CFFT in sport has been focused on the relationship of arousal level with CNS ( Görtelmeyer and Zimmermann, 1982 ). Increase in CFFT suggests an increase in cortical arousal and sensory sensitivity. By contrast, a decrease of CFFT suggests a reduction in the efficiency of the system to process information ( Li et al., 2004 ; Clemente and Díaz, 2019 ). On the other hand, for the evaluation of the brain’s direct current (DC) potentials -slow potentials that reflect alterations in brain excitability- OmegaWave technology has gained strength in recent years ( Naranjo-Orellana et al., 2020 ; Valenzuela et al., 2020 ). This device not only measures the Heart Rate Variability (HRV) but it also simultaneously a brainwave signal (DC potential) in order to complement the information obtained from HRV to assess the athlete’s functional state ( Naranjo-Orellana et al., 2020 ). DC potentials—frequency ranges between 0 and 0.5 Hz, are correlated with different brain processes, such as take consciousness during decision making ( Guggisberg and Mottaz, 2013 ) high alertness states ( Bachmann, 1984 ), arousal state ( Haider et al., 1981 ), or attention ( Rösler et al., 1997 ).

To date, most studies conducted in the evaluation of central fatigue have shown that the greatest disturbances are produced by tasks that require efforts at maximum speed that involve a large amount of force ( Davranche and Pichon, 2005 ; Clemente and Díaz, 2019 ). However, there are very few studies that have analyzed central fatigue through controlled analysis of a task that primarily involves central fatigue ( Fuentes et al., 2019 ). In this sense, the aim is to apply a computerized test (DT, Vienna Test System), that allows evaluating people’s tolerance to stress and central fatigue by applying a standardized protocol, in physical activity practitioners. The knowledge in this field is really limited, for this reason we developed the present research with the aim of studying the modifications in CFFT and DC potentials in a sample group of regular physical activity. The first hypothesis establishes that the computerized stress task increases the participants’ perception of central fatigue, while keeping the perception of peripheral fatigue stable. As a consequence, the second hypothesis establishes that differences will be found in the “post” situation in the CFFT measures and in the central physiological indicators, which would indicate a relationship between the subjective and objective measures of central fatigue.

Materials and Methods

This study followed a quasi-experimental design ( Montero and León, 2007 ) and it received the approval of the University Ethical Commission in compliance with the Helsinki Declaration. All subjects were informed about the procedure and gave their written consent to participate. This study was carried out complying with the Standards for Ethics in Sport and Exercise Science Research ( Harriss et al., 2019 ).

Participants

The participants included 22 individuals from Madrid (Spain), 18 of whom were male and 4 females. These participants were aged between 18 and 36 years ( M years = 22.82, SD years = 3.67). All of the participants regularly engaged in physical activity, between 4 and 14 h per week ( M hours / week = 7.77, SD hours / week = 3.32). The inclusion criteria was that they performed physical activity at least 3 times a week and 150 min of moderate/vigorous physical activity. The exclusion criteria was not correctly performing the proposed measurements. Four participants were excluded from the study for not completing the measurements correctly. Intentional sampling methods were used ( Montero and León, 2007 ). Due to the impossibility of continuing with the data collection due to the Alert State decreed by the Spanish Government as a result of COVID-19, the sample had to be closed with the participants who had passed all the tests before March 2020.

Instrumentation and Study Variables

The number of hours of physical activity per week and the scores obtained on the DT test were used as controlled variables. This allows us to know that the differences found are not due to the ability to respond to stress, or to the weekly amount of physical exercise performed. Therefore, only the subjects in which there were no statistically significant differences in their weekly level of physical exercise, nor in the scores obtained in the DT test, were used.

To carry out this research, three measurement systems have been used: OmegaWave device, Flicker Fusion Unit (Vienna Test System), and the Determination Test (Vienna Test System). OmegaWave is a device that assesses the physiological readiness of athletes by examining the autonomic balance through HRV and brain‘s energy balance via DC potential ( Gómez-Oliva et al., 2019 ), Elastic chest band MEDITRACE (dominant hand and forehead). Coach + application (OmegaWave Ltd, Espoo, Finland) was used on Ipad mini 2 32GB. The Vienna Test System is an instrument for computerized psychological assessments that allows the objective evaluation of different psychological parameters. The Determination Test (DT Vienna test system) ( Whiteside, 2002 ; Whiteside et al., 2003 ) was used to determine neuropsychological fatigue. The test studied the attentional capacity, reactive stress tolerance, reaction speed among continuously, and quickly changing acoustic and visual stimuli. The test is simple, the difficulty of the task lies in the different modality of the arriving stimuli and their speed. This way we measure those cognitive abilities of the people involved that are needed for the distinction of colors and sounds, the perception of the characteristics of stimuli, their memorization, and finally, the selection of the adequate answer. The stimuli coming during the test are not predictable. Instead, the subjects need to react to them randomly ( Schuhfried, 2013 ). We study four key variables: the average value of reaction speed (sec), the number of correct answers (raw score), which reflects the ability of the respondent to precisely and quickly select the adequate answer even under pressure. Furthermore, we also examine the number of incorrect answers (raw score) which can show us how likely the respondent is to get confused under stress and pressure; finally, the high number of missed answers (raw score) reveals that the respondent is not capable of maintaining his/her attention under stress and is prone to giving up these situations ( Neuwirth and Benesch, 2012 ). The duration of this test was 6 min.

Before and after the stress test the following parameters were analyzed in this order:

Parameters analyzed through OmegaWave Coach + device ® (OmegaWave Ltd, Espoo, Finland):

– Hear Rate Variability (HRV). Square root of the mean of the squares of successive RR interval differences (RMSSD), Standard deviation of all normal to normal RR intervals (SDNN), and Standard deviation of successive squares of intervals RR (SDSD). OmegaWave is a device that assesses the physiological readiness of athletes by examining autonomic balance through HRV and brain‘s metabolic state via DC potential ( Ilyukhina and Zabolotskikh, 2020 ). Elastic chest band MEDITRACE (dominant hand and forehead). Coach + application (Omegawave Ltd., Espoo, Finland) was used on Ipad mini 2 32GB. For calculating HRV it be used the Root Mean Square of the Successive Differences score (RMSSD) ( Ilyukhina et al., 1982 ). It was used before and after the stress test.

– DC potential dynamics. DC Potential represent changes in the brain’s metabolic balance in response to increased exercise intensity or psychological challenges and are linked to cognitive and mental load ( Wagshul et al., 2011 ; Ilyukhina, 2015 ).

– CNS System Readiness ( Ilyukhina, 1986 ). It’s indicated by a floating grade from 1.0 to 7.0, where 7.0 is the optimal state. This index represents the state of the brain’s energy level and is composed of three factors (in order of significance): stabilization point of DC potential (mV), stabilization time (reduces system readiness state of 1.0–7.0, if not optimal), and curve shape (reduces system readiness state of 1.0–7.0, if not optimal).

– Stabilization point of DC Potential (mV) ( Ilyukhina et al., 1982 ; Ilyukhina, 2013 ): The first priority in DC analysis is the stabilization point of DC Potential. In the literature, especially by Ilyukhina, this point is defined as Level of Operational Rest. In 1982, the combined work of Ilyukhina and Sychev was published which outlined quantitative parameters of LOR for the assessment of the healthy human’s adaptation and compensatory−adaptive abilities to physical and mental loads in sports.

– Stabilization time ( Ilyukhina and Zabolotskikh, 1997 ). The second priority of analysis is to look at the stabilization time. measured in minutes. The spontaneous relaxation speed represents neuroreflex reactivity (neural control of baroreflex arch) of cardiovascular and respiratory systems. This measure associated with psycho-emotional dynamic and stability. Normal stabilization time occurs within 2 min and represents optimal balance within stress-regulation systems.

– Curve Shape: The curve shape is composed of two elements: Difference between measurement start mV and end mV values ( Table 1 ). The optimal shape of the curve should show a smooth transition from a higher initial value (active wakefulness) to a lower stabilization value (operational rest DC potential form represents the dynamic interaction within stress-regulation systems). DC potential form can indicate the level of CNS activation balance.

Parameters analyzed though Flicker Fusion unit (Vienna Test System ® ):

– Critical flicker fusion ascending (Hz) (CFFA) and Critical flicker fusion descending (Hz) (CFFD). Cortical arousal was measured using the critical flicker fusion threshold (Hz) (CFFT) in a viewing chamber (Vienna Test System ® ), following the procedure of previous studies ( Clemente et al., 2016 ). An increase in CFFT suggests an increase in cortical arousal and information processing; a decrease in CFFT values below the baseline reflects a reduction in the efficiency of information processing and central nervous system fatigue ( Whiteside, 2002 ). It was used before and after the stress test.

Parameters analyzed though DT test (Vienna Test System ® ):

– We study four key variables: the average value of reaction speed (msec), the number of correct answers (raw score), which reflects the ability of the respondent to precisely and quickly select the adequate answer even under pressure. Furthermore, we also examine the number of incorrect answers (raw score) which can show us how likely the athlete is to get confused under stress and pressure; finally, the high number of missed answers (raw score) reveals that the respondent is not capable of maintaining his/her attention under stress and is prone to giving up these situations ( Neuwirth and Benesch, 2012 ). The duration of this test was 6 min without instructions.

Parameters analyzed by self-report instruments:

– Central Rating of Perceived Exertion (RPEC) and Peripheral Rating of Perceived Exertion (RPEP). The Rating of Perceived Exertion ( Borg, 1998 ), was used as a measure of central (cardiorespiratory) and peripheral (local-muscular, metabolic) exertion before and after the stress test ( Bolgar et al., 2010 ; Cárdenas et al., 2017 ). The RPE is a 15 point category-ratio; the odd numbered categories have verbal anchors. Beginning at 6, “no exertion at all,” and goes up to 20, “maximal exertion.” Before testing, subjects were instructed on the use of the RPE scale ( Noble and Robertson, 1996 ). We use the scale with the clear differentiation between central as peripheral perceived exertion following the recommendations of the medical staff and under the guideline of Borg ( Borg, 1982 ), for applied studies.

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Table 1. Simplified curve change mV reduction algorithm.

The participants were contacted and informed about the measurement protocol and of the date and time of the data collection. All of the measurements were collected during the same day. The total data collection time per participant was approximately 45 min. The order of measurements was the following: CFFT, DC Potential, RPE, DT test, RPE, CFFT, and DC Potential.

Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 21 (SPSS Inc., Chicago, Ill., United States). Means and SDs were calculated using traditional statistical techniques. Normality was tested with the Shapiro-Wilk test. As the distributions were not adjusted to the normal, non-parametric tests were used. A Wilcoxon sign ranges test for intragroup comparisons were conducted to analyze differences between pre and post-test. A Rho Spearman coefficient was used to know the correlations between variables. The Effect Size was tested using the formula = Z/ N for non-parametric tests ( Tomczak and Tomcak, 2014 ). Following the considerations of Cohen (1988) , the effect size is considered small when the value is inferior to 0.10, medium when it varies between 0.10 and 0.30 and high when it is superior to 0.50. The significance level was set at p < 0.05.

Descriptive Analysis, Normality Test According N, Wilcoxon Test, and Effect Sizes

Firstly, the normality tests were realized with the Shapiro-Wilk test. It was determined that most of the variables were not normal, due to which non-parametric statistical tests were applied. In relation to the descriptive analyzes of the study variables, shown in Table 2 , after applying the stressor via the DT test, worse values were obtained in all the variables measured. This reflects the alterations in the central response evaluated. Regarding the Wilcoxon rank test that was used to analyze whether there were differences between the scores obtained before and after applying the stressor (DT test), significant differences were found in the variables OverallDc ( p < 0.05), Flicker ascending ( p < 0.01), Flicker descending ( p < 0.01), Central RPE ( p < 0.01) and Physical RPE ( p < 0.01), while not finding significant differences in the rest of the variables ( Table 2 ).

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Table 2. Descriptive analysis of the measured variables.

Correlation Analysis

A Spearman bivariate correlation analysis was performed. Spearman’s Rho coefficient was used, since the distribution was non-parametric. Note that significant correlations were found ( Table 3 ) entre OverallDC con DCSSatabilizationLevel ( p = 0.000; r = 0.791 ∗∗ ); OWCNS ( p = 0.005; r = 0.581 ∗∗ ); OWDCC ( p = 0.013; r = 0.522 ∗ ); Flicker Descending ( p = 0.044; r = 0.432 ∗ ). DCSStabilizationLevel con OWCNS ( p = 0.000; r = 0.766 ∗∗ ); Flicker Descending ( p = 0.049; r = 0.424 ∗ ). DCSStabilizationTime con OWCNS ( p = 0.005; r = 0.572 ∗ ); OWDCC ( p = 0.046; r = 0.430 ∗ ); Flicker Ascending ( p = 0.006; r = 0.563 ∗∗ ). OWCNS correlated with Flicker Ascending ( p = 0.018; r = 0.499 ∗ ), and SDSD with Flicker Descending score ( p = 0.046; r = −0.430 ∗ ).

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Table 3. Rho Spearman coefficient.

The objective of the present research was to study the modification of DC potentials and the CFFT scores after the computerized stress test (DT). The analysis of the subjective cognitive responses about fatigue after DT test reveals significant differences in the participants, both at a physical and central level. As regards the first hypothesis, it is partially fulfilled. There are significant differences in central perceived fatigue, with a very high effect size, which supports the hypothesis and emphasizes the usefulness of the established research protocol. However, significant differences also appear in peripheral perceived fatigue, which is beyond the initial approaches. This result is of special interest because it allows to consider the relationship between both types of perceived fatigue ( Bittner et al., 2000 ; Clemente et al., 2016 ). These results, taking into account that the participants did the test sitting down, emphasize the effect achieved through the protocol used to generate stress in them, without significant differences in the performance achieved in the task. Previous research carried out with the DT test already points in this same direction ( Ong, 2015 ). The differences found in the perception of physical fatigue even without previous movement are interesting. Similar results are found in studies carried out in contexts such as chess ( Fuentes et al., 2019 ), where central fatigue due to the demands of each game also leads to physical fatigue of the players. This fact seems relevant insofar as the studies should incorporate measures of both dimensions to be able to explain a higher percentage of variance of the results found.

As regards the second hypothesis, the decrease of CFFD values indicates that it has a negative effect generating central fatigue and an alteration in cortical activation ( Li et al., 2004 ; Clemente, 2016 ). These results confirm the alterations in cortical activation found in physiological efforts of high intensity and of short duration, such as sprints at maximum speed ( Clemente et al., 2011 ). This same trend is also observed in research focused on generating a high level of stress in soldiers, which emphasizes the usefulness of using the DT test to create stress in the participants ( Clemente et al., 2016 ). In line with the ideas defended by Clemente (2016) , decreased in CFFD scores seem to be linked to high sympathetic autonomous nervous system activation, which could also affect higher cognitive functions, such as executive processes (i.e., making complex decisions, memory, and attention processes) ( Shields et al., 2016 ). These same considerations can also be made with respect to the significant differences found in CFFA scores. Higher scores are found after the stress test, which implies that the participants have needed more time to respond to the flicker task as consequence of central fatigue ( Fuentes et al., 2019 ; Lohani et al., 2019 ).

Regarding the results obtained in the Overall DC scores, the significant differences show a pattern of alteration as a consequence of the stress test. As Naranjo-Orellana et al. (2020) point out, the OW test obtains good reliability and validity values using the heart rate variability as a measure in conjunction with the DC Potential (stabilitation DC, stabilitation time, and curve shape). Changes in the DC potentials have been reported to be reflective of performance in different brain processes ( Haider et al., 1981 ; Valenzuela et al., 2020 ). The lower scores obtained after the stress test could indicate, as with the CFF scores, an increase in central fatigue detected by the OmegaWave system ( Valenzuela et al., 2020 ). This result, in any case, needs to be analyzed in detail in future research.

Therefore, monitoring the DC potentials and the CFF scores could be useful to control the cognitive load of the different tasks that having a high mental demand.

Due to the exceptional circumstances of data collection in the present study, some of the study limitations were the sample size and the small number of women who participated in it. Future research works should expand the sample power, as well as determine its effect in a sedentary sample.

To conclude, this is the first study that has jointly analyzed the scores obtained in the analysis of low-frequency brain waves (DC potentials), together with those obtained in the Flicker test. In this sense, although the performance in a specific task seems similar, the demand it has for the person must be evaluated, being useful the use of research protocols similar to the ones we have used. The results open a new field where both measurements could be interesting and useful to assess the cognitive demands of persons.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.

Ethics Statement

The studies involving human participants were reviewed and approved by the University Ethical Commission in compliance with the Helsinki Declaration. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

RV: conceptualization, investigation, resources, writing—review and editing, and project administration. RV, ML-R, and RJ-C: methodology, data curation, writing—original draft preparation, visualization, supervision, and formal analysis. ML-R and RJ-C: software and validation.

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.

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Keywords : central fatigue, omega wave, cognitive response, psychophysiology, stress

Citation: de la Vega R, Jiménez-Castuera R and Leyton-Román M (2021) Impact of Weekly Physical Activity on Stress Response: An Experimental Study. Front. Psychol. 11:608217. doi: 10.3389/fpsyg.2020.608217

Received: 19 September 2020; Accepted: 04 December 2020; Published: 12 January 2021.

Reviewed by:

Copyright © 2021 de la Vega, Jiménez-Castuera and Leyton-Román. 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: Marta Leyton-Román, [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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  • Beauty sleep:...

Beauty sleep: experimental study on the perceived health and attractiveness of sleep deprived people

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  • Peer review
  • John Axelsson , researcher 1 2 ,
  • Tina Sundelin , research assistant and MSc student 2 ,
  • Michael Ingre , statistician and PhD student 3 ,
  • Eus J W Van Someren , researcher 4 ,
  • Andreas Olsson , researcher 2 ,
  • Mats Lekander , researcher 1 3
  • 1 Osher Center for Integrative Medicine, Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden
  • 2 Division for Psychology, Department of Clinical Neuroscience, Karolinska Institutet
  • 3 Stress Research Institute, Stockholm University, Stockholm
  • 4 Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, and VU Medical Center, Amsterdam, Netherlands
  • Correspondence to: J Axelsson john.axelsson{at}ki.se
  • Accepted 22 October 2010

Objective To investigate whether sleep deprived people are perceived as less healthy, less attractive, and more tired than after a normal night’s sleep.

Design Experimental study.

Setting Sleep laboratory in Stockholm, Sweden.

Participants 23 healthy, sleep deprived adults (age 18-31) who were photographed and 65 untrained observers (age 18-61) who rated the photographs.

Intervention Participants were photographed after a normal night’s sleep (eight hours) and after sleep deprivation (31 hours of wakefulness after a night of reduced sleep). The photographs were presented in a randomised order and rated by untrained observers.

Main outcome measure Difference in observer ratings of perceived health, attractiveness, and tiredness between sleep deprived and well rested participants using a visual analogue scale (100 mm).

Results Sleep deprived people were rated as less healthy (visual analogue scale scores, mean 63 (SE 2) v 68 (SE 2), P<0.001), more tired (53 (SE 3) v 44 (SE 3), P<0.001), and less attractive (38 (SE 2) v 40 (SE 2), P<0.001) than after a normal night’s sleep. The decrease in rated health was associated with ratings of increased tiredness and decreased attractiveness.

Conclusion Our findings show that sleep deprived people appear less healthy, less attractive, and more tired compared with when they are well rested. This suggests that humans are sensitive to sleep related facial cues, with potential implications for social and clinical judgments and behaviour. Studies are warranted for understanding how these effects may affect clinical decision making and can add knowledge with direct implications in a medical context.

Introduction

The recognition [of the case] depends in great measure on the accurate and rapid appreciation of small points in which the diseased differs from the healthy state Joseph Bell (1837-1911)

Good clinical judgment is an important skill in medical practice. This is well illustrated in the quote by Joseph Bell, 1 who demonstrated impressive observational and deductive skills. Bell was one of Sir Arthur Conan Doyle’s teachers and served as a model for the fictitious detective Sherlock Holmes. 2 Generally, human judgment involves complex processes, whereby ingrained, often less consciously deliberated responses from perceptual cues are mixed with semantic calculations to affect decision making. 3 Thus all social interactions, including diagnosis in clinical practice, are influenced by reflexive as well as reflective processes in human cognition and communication.

Sleep is an essential homeostatic process with well established effects on an individual’s physiological, cognitive, and behavioural functionality 4 5 6 7 and long term health, 8 but with only anecdotal support of a role in social perception, such as that underlying judgments of attractiveness and health. As illustrated by the common expression “beauty sleep,” an individual’s sleep history may play an integral part in the perception and judgments of his or her attractiveness and health. To date, the concept of beauty sleep has lacked scientific support, but the biological importance of sleep may have favoured a sensitivity to perceive sleep related cues in others. It seems warranted to explore such sensitivity, as sleep disorders and disturbed sleep are increasingly common in today’s 24 hour society and often coexist with some of the most common health problems, such as hypertension 9 10 and inflammatory conditions. 11

To describe the relation between sleep deprivation and perceived health and attractiveness we asked untrained observers to rate the faces of people who had been photographed after a normal night’s sleep and after a night of sleep deprivation. We chose facial photographs as the human face is the primary source of information in social communication. 12 A perceiver’s response to facial cues, signalling the bearer’s emotional state, intentions, and potential mate value, serves to guide actions in social contexts and may ultimately promote survival. 13 14 15 We hypothesised that untrained observers would perceive sleep deprived people as more tired, less healthy, and less attractive compared with after a normal night’s sleep.

Using an experimental design we photographed the faces of 23 adults (mean age 23, range 18-31 years, 11 women) between 14.00 and 15.00 under two conditions in a balanced design: after a normal night’s sleep (at least eight hours of sleep between 23.00-07.00 and seven hours of wakefulness) and after sleep deprivation (sleep 02.00-07.00 and 31 hours of wakefulness). We advertised for participants at four universities in the Stockholm area. Twenty of 44 potentially eligible people were excluded. Reasons for exclusion were reported sleep disturbances, abnormal sleep requirements (for example, sleep need out of the 7-9 hour range), health problems, or availability on study days (the main reason). We also excluded smokers and those who had consumed alcohol within two days of the protocol. One woman failed to participate in both conditions. Overall, we enrolled 12 women and 12 men.

The participants slept in their own homes. Sleep times were confirmed with sleep diaries and text messages. The sleep diaries (Karolinska sleep diary) included information on sleep latency, quality, duration, and sleepiness. Participants sent a text message to the research assistant by mobile phone (SMS) at bedtime and when they got up on the night before sleep deprivation. They had been instructed not to nap. During the normal sleep condition the participants’ mean duration of sleep, estimated from sleep diaries, was 8.45 (SE 0.20) hours. The sleep deprivation condition started with a restriction of sleep to five hours in bed; the participants sent text messages (SMS) when they went to sleep and when they woke up. The mean duration of sleep during this night, estimated from sleep diaries and text messages, was 5.06 (SE 0.04) hours. For the following night of total sleep deprivation, the participants were monitored in the sleep laboratory at all times. Thus, for the sleep deprivation condition, participants came to the laboratory at 22.00 (after 15 hours of wakefulness) to be monitored, and stayed awake for a further 16 hours. We therefore did not observe the participants during the first 15 hours of wakefulness, when they had had a slightly restricted sleep, but had good control over the last 16 hours of wakefulness when sleepiness increased in magnitude. For the sleep condition, participants came to the laboratory at 12.00 (after five hours of wakefulness). They were kept indoors two hours before being photographed to avoid the effects of exposure to sunlight and the weather. We had a series of five or six photographs (resolution 3872×2592 pixels) taken in a well lit room, with a constant white balance (×900l; colour temperature 4200 K, Nikon D80; Nikon, Tokyo). The white balance was differently set during the two days of the study and affected seven photographs (four taken during sleep deprivation and three during a normal night’s sleep). Removing these participants from the analyses did not affect the results. The distance from camera to head was fixed, as was the focal length, within 14 mm (between 44 and 58 mm). To ensure a fixed surface area of each face on the photograph, the focal length was adapted to the head size of each participant.

For the photo shoot, participants wore no makeup, had their hair loose (combed backwards if long), underwent similar cleaning or shaving procedures for both conditions, and were instructed to “sit with a straight back and look straight into the camera with a neutral, relaxed facial expression.” Although the photographer was not blinded to the sleep conditions, she followed a highly standardised procedure during each photo shoot, including minimal interaction with the participants. A blinded rater chose the most typical photograph from each series of photographs. This process resulted in 46 photographs; two (one from each sleep condition) of each of the 23 participants. This part of the study took place between June and September 2007.

In October 2007 the photographs were presented at a fixed interval of six seconds in a randomised order to 65 observers (mainly students at the Karolinska Institute, mean age 30 (range 18-61) years, 40 women), who were unaware of the conditions of the study. They rated the faces for attractiveness (very unattractive to very attractive), health (very sick to very healthy), and tiredness (not at all tired to very tired) on a 100 mm visual analogue scale. After every 23 photographs a brief intermission was allowed, including a working memory task lasting 23 seconds to prevent the faces being memorised. To ensure that the observers were not primed to tiredness when rating health and attractiveness they rated the photographs for attractiveness and health in the first two sessions and tiredness in the last. To avoid the influence of possible order effects we presented the photographs in a balanced order between conditions for each session.

Statistical analyses

Data were analysed using multilevel mixed effects linear regression, with two crossed independent random effects accounting for random variation between observers and participants using the xtmixed procedure in Stata 9.2. We present the effect of condition as a percentage of change from the baseline condition as the reference using the absolute value in millimetres (rated on the visual analogue scale). No data were missing in the analyses.

Sixty five observers rated each of the 46 photographs for attractiveness, health, and tiredness: 138 ratings by each observer and 2990 ratings for each of the three factors rated. When sleep deprived, people were rated as less healthy (visual analogue scale scores, mean 63 (SE 2) v 68 (SE 2)), more tired (53 (SE 3) v 44 (SE 3)), and less attractive (38 (SE 2) v 40 (SE 2); P<0.001 for all) than after a normal night’s sleep (table 1 ⇓ ). Compared with the normal sleep condition, perceptions of health and attractiveness in the sleep deprived condition decreased on average by 6% and 4% and tiredness increased by 19%.

 Multilevel mixed effects regression on effect of how sleep deprived people are perceived with respect to attractiveness, health, and tiredness

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A 10 mm increase in tiredness was associated with a −3.0 mm change in health, a 10 mm increase in health increased attractiveness by 2.4 mm, and a 10 mm increase in tiredness reduced attractiveness by 1.2 mm (table 2 ⇓ ). These findings were also presented as correlation, suggesting that faces with perceived attractiveness are positively associated with perceived health (r=0.42, fig 1 ⇓ ) and negatively with perceived tiredness (r=−0.28, fig 1). In addition, the average decrease (for each face) in attractiveness as a result of deprived sleep was associated with changes in tiredness (−0.53, n=23, P=0.03) and in health (0.50, n=23, P=0.01). Moreover, a strong negative association was found between the respective perceptions of tiredness and health (r=−0.54, fig 1). Figure 2 ⇓ shows an example of observer rated faces.

 Associations between health, tiredness, and attractiveness

Fig 1  Relations between health, tiredness, and attractiveness of 46 photographs (two each of 23 participants) rated by 65 observers on 100 mm visual analogue scales, with variation between observers removed using empirical Bayes’ estimates

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Fig 2  Participant after a normal night’s sleep (left) and after sleep deprivation (right). Faces were presented in a counterbalanced order

To evaluate the mediation effects of sleep loss on attractiveness and health, tiredness was added to the models presented in table 1 following recommendations. 16 The effect of sleep loss was significantly mediated by tiredness on both health (P<0.001) and attractiveness (P<0.001). When tiredness was added to the model (table 1) with an estimated coefficient of −2.9 (SE 0.1; P<0.001) the independent effect of sleep loss on health decreased from −4.2 to −1.8 (SE 0.5; P<0.001). The effect of sleep loss on attractiveness decreased from −1.6 (table 1) to −0.62 (SE 0.4; P=0.133), with tiredness estimated at −1.1 (SE 0.1; P<0.001). The same approach applied to the model of attractiveness and health (table 2), with a decrease in the association from 2.4 to 2.1 (SE 0.1; P<0.001) with tiredness estimated at −0.56 (SE 0.1; P<0.001).

Sleep deprived people are perceived as less attractive, less healthy, and more tired compared with when they are well rested. Apparent tiredness was strongly related to looking less healthy and less attractive, which was also supported by the mediating analyses, indicating that a large part of the found effects and relations on appearing healthy and attractive were mediated by looking tired. The fact that untrained observers detected the effects of sleep loss in others not only provides evidence for a perceptual ability not previously subjected to experimental control, but also supports the notion that sleep history gives rise to socially relevant signals that provide information about the bearer. The adaptiveness of an ability to detect sleep related facial cues resonates well with other research, showing that small deviations from the average sleep duration in the long term are associated with an increased risk of health problems and with a decreased longevity. 8 17 Indeed, even a few hours of sleep deprivation inflict an array of physiological changes, including neural, endocrinological, immunological, and cellular functioning, that if sustained are relevant for long term health. 7 18 19 20 Here, we show that such physiological changes are paralleled by detectable facial changes.

These results are related to photographs taken in an artificial setting and presented to the observers for only six seconds. It is likely that the effects reported here would be larger in real life person to person situations, when overt behaviour and interactions add further information. Blink interval and blink duration are known to be indicators of sleepiness, 21 and trained observers are able to evaluate reliably the drowsiness of drivers by watching their videotaped faces. 22 In addition, a few of the people were perceived as healthier, less tired, and more attractive during the sleep deprived condition. It remains to be evaluated in follow-up research whether this is due to random error noise in judgments, or associated with specific characteristics of observers or the sleep deprived people they judge. Nevertheless, we believe that the present findings can be generalised to a wide variety of settings, but further studies will have to investigate the impact on clinical studies and other social situations.

Importantly, our findings suggest a prominent role of sleep history in several domains of interpersonal perception and judgment, in which sleep history has previously not been considered of importance, such as in clinical judgment. In addition, because attractiveness motivates sexual behaviour, collaboration, and superior treatment, 13 sleep loss may have consequences in other social contexts. For example, it has been proposed that facial cues perceived as attractive are signals of good health and that this recognition has been selected evolutionarily to guide choice of mate and successful transmission of genes. 13 The fact that good sleep supports a healthy look and poor sleep the reverse may be of particular relevance in the medical setting, where health estimates are an essential part. It is possible that people with sleep disturbances, clinical or otherwise, would be judged as more unhealthy, whereas those who have had an unusually good night’s sleep may be perceived as rather healthy. Compared with the sleep deprivation used in the present investigation, further studies are needed to investigate the effects of less drastic acute reductions of sleep as well as long term clinical effects.

Conclusions

People are capable of detecting sleep loss related facial cues, and these cues modify judgments of another’s health and attractiveness. These conclusions agree well with existing models describing a link between sleep and good health, 18 23 as well as a link between attractiveness and health. 13 Future studies should focus on the relevance of these facial cues in clinical settings. These could investigate whether clinicians are better than the average population at detecting sleep or health related facial cues, and whether patients with a clinical diagnosis exhibit more tiredness and are less healthy looking than healthy people. Perhaps the more successful doctors are those who pick up on these details and act accordingly.

Taken together, our results provide important insights into judgments about health and attractiveness that are reminiscent of the anecdotal wisdom harboured in Bell’s words, and in the colloquial notion of “beauty sleep.”

What is already known on this topic

Short or disturbed sleep and fatigue constitute major risk factors for health and safety

Complaints of short or disturbed sleep are common among patients seeking healthcare

The human face is the main source of information for social signalling

What this study adds

The facial cues of sleep deprived people are sufficient for others to judge them as more tired, less healthy, and less attractive, lending the first scientific support to the concept of “beauty sleep”

By affecting doctors’ general perception of health, the sleep history of a patient may affect clinical decisions and diagnostic precision

Cite this as: BMJ 2010;341:c6614

We thank B Karshikoff for support with data acquisition and M Ingvar for comments on an earlier draft of the manuscript, both without compensation and working at the Department for Clinical Neuroscience, Karolinska Institutet, Sweden.

Contributors: JA designed the data collection, supervised and monitored data collection, wrote the statistical analysis plan, carried out the statistical analyses, obtained funding, drafted and revised the manuscript, and is guarantor. TS designed and carried out the data collection, cleaned the data, drafted, revised the manuscript, and had final approval of the manuscript. JA and TS contributed equally to the work. MI wrote the statistical analysis plan, carried out the statistical analyses, drafted the manuscript, and critically revised the manuscript. EJWVS provided statistical advice, advised on data handling, and critically revised the manuscript. AO provided advice on the methods and critically revised the manuscript. ML provided administrative support, drafted the manuscript, and critically revised the manuscript. All authors approved the final version of the manuscript.

Funding: This study was funded by the Swedish Society for Medical Research, Rut and Arvid Wolff’s Memory Fund, and the Osher Center for Integrative Medicine.

Competing interests: All authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any company for the submitted work; no financial relationships with any companies that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: This study was approved by the Karolinska Institutet’s ethical committee. Participants were compensated for their participation.

Participant consent: Participant’s consent obtained.

Data sharing: Statistical code and dataset of ratings are available from the corresponding author at john.axelsson{at}ki.se .

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode .

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Experimental psychology, the journal for experimental research in psychology.

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As its name implies, Experimental Psychology publishes innovative, original, high-quality experimental research in psychology.

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Message From Your New Editor Raymond M. Klein Experimental Psychology, Vol. 68, No. 4, pp. 173-174

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(A)symmetries in Memory and Directed Forgetting of Political Stimuli Andrew Franks, Hajime Otani, and Gavin T. Roupe Experimental Psychology, Vol. 70, No. 2, pp. 68-80

Probing the Dual-Route Model of the SNARC Effect by Orthogonalizing Processing Speed and Depth Daniele Didino, Matthias Brandtner, Maria Glaser, and André Knops Experimental Psychology, Vol. 70, No. 1, pp. 1–13

experimental research psychology paper

Raymond Klein

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Swasti Arora Department of Psychology and Neuroscience Faculty of Science Dalhousie University 1355 Oxford St. Halifax, Nova Scotia B3H 4R2 Canada Send email

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Ullrich Ecker School of Psychological Science University of Western Australia Perth 6009 Australia Send email Associate Professor at the University of Western Australia. His main interests lie in episodic memory, working memory, feature binding, memory updating, as well as the processing of misinformation and its effects on memory and reasoning. He uses mainly behavioural experimentation, augmented by neuroimaging methods (event-related potentials, fMRI) and computational modelling.

Gesa Hartwigsen Lise Meitner Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences Stephanstraße 1a 04103 Leipzig Germany Send email Lise Meitner Research Group Leader at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig (Germany). Her main interest is the potential for adaptive systems plasticity in neural networks for cognitive functions, with a focus on the healthy and lesioned language network. Her group combines neurostimulation and neuroimaging techniques to probe interactions between domain-specific and domain-general networks.

Manuel Perea University of València Av. Blasco Ibáñez, 21 46010 Valencia Spain Send email Professor of Psychology at the University of Valencia (Spain). His main fields of interest are psychology of language, lexical-semantic memory, and cognitive neuroscience.

James R. Schmidt Université de Bourgogne LEAD-CNRS UMR 5022 Pole AAFE 11 Esplanade Erasme 21000 Dijon France Send email Full Professor at the Université de Bourgogne, working in the Laboratoire d'Etude de l'Apprentissage et du Développement (LEAD; Laboratory for Research on Learning and Development). His main research interests are implicit learning, music learning, cognitive control, and neural networks.

Alexander Schütz University of Marburg Department of Psychology Gutenbergstr. 18 35032 Marburg Germany Send email Professor of Experimental Psychology at the University of Marburg (Germany). His main research interests are visual perception, eye movements and their interaction in active perception.

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Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Writing the Experimental Report: Overview, Introductions, and Literature Reviews

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Written for undergraduate students and new graduate students in psychology (experimental), this handout provides information on writing in psychology and on experimental report and experimental article writing.

Experimental reports (also known as "lab reports") are reports of empirical research conducted by their authors. You should think of an experimental report as a "story" of your research in which you lead your readers through your experiment. As you are telling this story, you are crafting an argument about both the validity and reliability of your research, what your results mean, and how they fit into other previous work.

These next two sections provide an overview of the experimental report in APA format. Always check with your instructor, advisor, or journal editor for specific formatting guidelines.

General-specific-general format

Experimental reports follow a general to specific to general pattern. Your report will start off broadly in your introduction and discussion of the literature; the report narrows as it leads up to your specific hypotheses, methods, and results. Your discussion transitions from talking about your specific results to more general ramifications, future work, and trends relating to your research.

Experimental reports in APA format have a title page. Title page formatting is as follows:

  • A running head and page number in the upper right corner (right aligned)
  • A definition of running head in IN ALL CAPS below the running head (left aligned)
  • Vertically and horizontally centered paper title, followed by author and affiliation

Please see our sample APA title page .

Crafting your story

Before you begin to write, carefully consider your purpose in writing: what is it that you discovered, would like to share, or would like to argue? You can see report writing as crafting a story about your research and your findings. Consider the following.

  • What is the story you would like to tell?
  • What literature best speaks to that story?
  • How do your results tell the story?
  • How can you discuss the story in broad terms?

During each section of your paper, you should be focusing on your story. Consider how each sentence, each paragraph, and each section contributes to your overall purpose in writing. Here is a description of one student's process.

Briel is writing an experimental report on her results from her experimental psychology lab class. She was interested in looking at the role gender plays in persuading individuals to take financial risks. After her data analysis, she finds that men are more easily persuaded by women to take financial risks and that men are generally willing to take more financial risks.

When Briel begins to write, she focuses her introduction on financial risk taking and gender, focusing on male behaviors. She then presents relevant literature on financial risk taking and gender that help illuminate her own study, but also help demonstrate the need for her own work. Her introduction ends with a study overview that directly leads from the literature review. Because she has already broadly introduced her study through her introduction and literature review, her readers can anticipate where she is going when she gets to her study overview. Her methods and results continue that story. Finally, her discussion concludes that story, discussing her findings, implications of her work, and the need for more research in the area of gender and financial risk taking.

The abstract gives a concise summary of the contents of the report.

  • Abstracts should be brief (about 100 words)
  • Abstracts should be self-contained and provide a complete picture of what the study is about
  • Abstracts should be organized just like your experimental report—introduction, literature review, methods, results and discussion
  • Abstracts should be written last during your drafting stage

Introduction

The introduction in an experimental article should follow a general to specific pattern, where you first introduce the problem generally and then provide a short overview of your own study. The introduction includes three parts: opening statements, literature review, and study overview.

Opening statements: Define the problem broadly in plain English and then lead into the literature review (this is the "general" part of the introduction). Your opening statements should already be setting the stage for the story you are going to tell.

Literature review: Discusses literature (previous studies) relevant to your current study in a concise manner. Keep your story in mind as you organize your lit review and as you choose what literature to include. The following are tips when writing your literature review.

  • You should discuss studies that are directly related to your problem at hand and that logically lead to your own hypotheses.
  • You do not need to provide a complete historical overview nor provide literature that is peripheral to your own study.
  • Studies should be presented based on themes or concepts relevant to your research, not in a chronological format.
  • You should also consider what gap in the literature your own research fills. What hasn't been examined? What does your work do that others have not?

Study overview: The literature review should lead directly into the last section of the introduction—your study overview. Your short overview should provide your hypotheses and briefly describe your method. The study overview functions as a transition to your methods section.

You should always give good, descriptive names to your hypotheses that you use consistently throughout your study. When you number hypotheses, readers must go back to your introduction to find them, which makes your piece more difficult to read. Using descriptive names reminds readers what your hypotheses were and allows for better overall flow.

In our example above, Briel had three different hypotheses based on previous literature. Her first hypothesis, the "masculine risk-taking hypothesis" was that men would be more willing to take financial risks overall. She clearly named her hypothesis in the study overview, and then referred back to it in her results and discussion sections.

Thais and Sanford (2000) recommend the following organization for introductions.

  • Provide an introduction to your topic
  • Provide a very concise overview of the literature
  • State your hypotheses and how they connect to the literature
  • Provide an overview of the methods for investigation used in your research

Bem (2006) provides the following rules of thumb for writing introductions.

  • Write in plain English
  • Take the time and space to introduce readers to your problem step-by-step; do not plunge them into the middle of the problem without an introduction
  • Use examples to illustrate difficult or unfamiliar theories or concepts. The more complicated the concept or theory, the more important it is to have clear examples
  • Open with a discussion about people and their behavior, not about psychologists and their research

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Writing Research Papers

  • Research Paper Structure

Whether you are writing a B.S. Degree Research Paper or completing a research report for a Psychology course, it is highly likely that you will need to organize your research paper in accordance with American Psychological Association (APA) guidelines.  Here we discuss the structure of research papers according to APA style.

Major Sections of a Research Paper in APA Style

A complete research paper in APA style that is reporting on experimental research will typically contain a Title page, Abstract, Introduction, Methods, Results, Discussion, and References sections. 1  Many will also contain Figures and Tables and some will have an Appendix or Appendices.  These sections are detailed as follows (for a more in-depth guide, please refer to " How to Write a Research Paper in APA Style ”, a comprehensive guide developed by Prof. Emma Geller). 2

What is this paper called and who wrote it? – the first page of the paper; this includes the name of the paper, a “running head”, authors, and institutional affiliation of the authors.  The institutional affiliation is usually listed in an Author Note that is placed towards the bottom of the title page.  In some cases, the Author Note also contains an acknowledgment of any funding support and of any individuals that assisted with the research project.

One-paragraph summary of the entire study – typically no more than 250 words in length (and in many cases it is well shorter than that), the Abstract provides an overview of the study.

Introduction

What is the topic and why is it worth studying? – the first major section of text in the paper, the Introduction commonly describes the topic under investigation, summarizes or discusses relevant prior research (for related details, please see the Writing Literature Reviews section of this website), identifies unresolved issues that the current research will address, and provides an overview of the research that is to be described in greater detail in the sections to follow.

What did you do? – a section which details how the research was performed.  It typically features a description of the participants/subjects that were involved, the study design, the materials that were used, and the study procedure.  If there were multiple experiments, then each experiment may require a separate Methods section.  A rule of thumb is that the Methods section should be sufficiently detailed for another researcher to duplicate your research.

What did you find? – a section which describes the data that was collected and the results of any statistical tests that were performed.  It may also be prefaced by a description of the analysis procedure that was used. If there were multiple experiments, then each experiment may require a separate Results section.

What is the significance of your results? – the final major section of text in the paper.  The Discussion commonly features a summary of the results that were obtained in the study, describes how those results address the topic under investigation and/or the issues that the research was designed to address, and may expand upon the implications of those findings.  Limitations and directions for future research are also commonly addressed.

List of articles and any books cited – an alphabetized list of the sources that are cited in the paper (by last name of the first author of each source).  Each reference should follow specific APA guidelines regarding author names, dates, article titles, journal titles, journal volume numbers, page numbers, book publishers, publisher locations, websites, and so on (for more information, please see the Citing References in APA Style page of this website).

Tables and Figures

Graphs and data (optional in some cases) – depending on the type of research being performed, there may be Tables and/or Figures (however, in some cases, there may be neither).  In APA style, each Table and each Figure is placed on a separate page and all Tables and Figures are included after the References.   Tables are included first, followed by Figures.   However, for some journals and undergraduate research papers (such as the B.S. Research Paper or Honors Thesis), Tables and Figures may be embedded in the text (depending on the instructor’s or editor’s policies; for more details, see "Deviations from APA Style" below).

Supplementary information (optional) – in some cases, additional information that is not critical to understanding the research paper, such as a list of experiment stimuli, details of a secondary analysis, or programming code, is provided.  This is often placed in an Appendix.

Variations of Research Papers in APA Style

Although the major sections described above are common to most research papers written in APA style, there are variations on that pattern.  These variations include: 

  • Literature reviews – when a paper is reviewing prior published research and not presenting new empirical research itself (such as in a review article, and particularly a qualitative review), then the authors may forgo any Methods and Results sections. Instead, there is a different structure such as an Introduction section followed by sections for each of the different aspects of the body of research being reviewed, and then perhaps a Discussion section. 
  • Multi-experiment papers – when there are multiple experiments, it is common to follow the Introduction with an Experiment 1 section, itself containing Methods, Results, and Discussion subsections. Then there is an Experiment 2 section with a similar structure, an Experiment 3 section with a similar structure, and so on until all experiments are covered.  Towards the end of the paper there is a General Discussion section followed by References.  Additionally, in multi-experiment papers, it is common for the Results and Discussion subsections for individual experiments to be combined into single “Results and Discussion” sections.

Departures from APA Style

In some cases, official APA style might not be followed (however, be sure to check with your editor, instructor, or other sources before deviating from standards of the Publication Manual of the American Psychological Association).  Such deviations may include:

  • Placement of Tables and Figures  – in some cases, to make reading through the paper easier, Tables and/or Figures are embedded in the text (for example, having a bar graph placed in the relevant Results section). The embedding of Tables and/or Figures in the text is one of the most common deviations from APA style (and is commonly allowed in B.S. Degree Research Papers and Honors Theses; however you should check with your instructor, supervisor, or editor first). 
  • Incomplete research – sometimes a B.S. Degree Research Paper in this department is written about research that is currently being planned or is in progress. In those circumstances, sometimes only an Introduction and Methods section, followed by References, is included (that is, in cases where the research itself has not formally begun).  In other cases, preliminary results are presented and noted as such in the Results section (such as in cases where the study is underway but not complete), and the Discussion section includes caveats about the in-progress nature of the research.  Again, you should check with your instructor, supervisor, or editor first.
  • Class assignments – in some classes in this department, an assignment must be written in APA style but is not exactly a traditional research paper (for instance, a student asked to write about an article that they read, and to write that report in APA style). In that case, the structure of the paper might approximate the typical sections of a research paper in APA style, but not entirely.  You should check with your instructor for further guidelines.

Workshops and Downloadable Resources

  • For in-person discussion of the process of writing research papers, please consider attending this department’s “Writing Research Papers” workshop (for dates and times, please check the undergraduate workshops calendar).

Downloadable Resources

  • How to Write APA Style Research Papers (a comprehensive guide) [ PDF ]
  • Tips for Writing APA Style Research Papers (a brief summary) [ PDF ]
  • Example APA Style Research Paper (for B.S. Degree – empirical research) [ PDF ]
  • Example APA Style Research Paper (for B.S. Degree – literature review) [ PDF ]

Further Resources

How-To Videos     

  • Writing Research Paper Videos

APA Journal Article Reporting Guidelines

  • Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 3.
  • Levitt, H. M., Bamberg, M., Creswell, J. W., Frost, D. M., Josselson, R., & Suárez-Orozco, C. (2018). Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA Publications and Communications Board task force report . American Psychologist , 73 (1), 26.  

External Resources

  • Formatting APA Style Papers in Microsoft Word
  • How to Write an APA Style Research Paper from Hamilton University
  • WikiHow Guide to Writing APA Research Papers
  • Sample APA Formatted Paper with Comments
  • Sample APA Formatted Paper
  • Tips for Writing a Paper in APA Style

1 VandenBos, G. R. (Ed). (2010). Publication manual of the American Psychological Association (6th ed.) (pp. 41-60).  Washington, DC: American Psychological Association.

2 geller, e. (2018).  how to write an apa-style research report . [instructional materials]. , prepared by s. c. pan for ucsd psychology.

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  • What Types of References Are Appropriate?
  • Evaluating References and Taking Notes
  • Citing References
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  • Writing Research Papers Videos

Experimental Method In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups .

What is an Experiment?

An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

An advantage is that experiments should be objective. The researcher’s views and opinions should not affect a study’s results. This is good as it makes the data more valid  and less biased.

There are three types of experiments you need to know:

1. Lab Experiment

A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions.

A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where accurate measurements are possible.

The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.

Participants are randomly allocated to each independent variable group.

Examples are Milgram’s experiment on obedience and  Loftus and Palmer’s car crash study .

  • Strength : It is easier to replicate (i.e., copy) a laboratory experiment. This is because a standardized procedure is used.
  • Strength : They allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.
  • Limitation : The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e., low ecological validity. This means it would not be possible to generalize the findings to a real-life setting.
  • Limitation : Demand characteristics or experimenter effects may bias the results and become confounding variables .

2. Field Experiment

A field experiment is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.

However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables .

Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.

An example is Holfing’s hospital study on obedience .

  • Strength : behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
  • Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
  • Limitation : There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

3. Natural Experiment

A natural experiment in psychology is a research method in which the experimenter observes the effects of a naturally occurring event or situation on the dependent variable without manipulating any variables.

Natural experiments are conducted in the day (i.e., real life) environment of the participants, but here, the experimenter has no control over the independent variable as it occurs naturally in real life.

Natural experiments are often used to study psychological phenomena that would be difficult or unethical to study in a laboratory setting, such as the effects of natural disasters, policy changes, or social movements.

For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of children who have been adopted, fostered, or returned to their mothers with a control group of children who had spent all their lives in their biological families.

Here is a fictional example of a natural experiment in psychology:

Researchers might compare academic achievement rates among students born before and after a major policy change that increased funding for education.

In this case, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. The researchers would not be able to manipulate the independent variable, but they could observe its effects on the dependent variable.

  • Strength : behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e., very high ecological validity.
  • Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
  • Strength : It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress .
  • Limitation : They may be more expensive and time-consuming than lab experiments.
  • Limitation : There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

Key Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. EVs should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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Experimental Psychology: 10 Examples & Definition

experimental psychology exmaples and definition, explained below

Experimental psychology refers to studying psychological phenomena using scientific methods. Originally, the primary scientific method involved manipulating one variable and observing systematic changes in another variable.

Today, psychologists utilize several types of scientific methodologies.

Experimental psychology examines a wide range of psychological phenomena, including: memory, sensation and perception, cognitive processes, motivation, emotion, developmental processes, in addition to the neurophysiological concomitants of each of these subjects.

Studies are conducted on both animal and human participants, and must comply with stringent requirements and controls regarding the ethical treatment of both.

Definition of Experimental Psychology

Experimental psychology is a branch of psychology that utilizes scientific methods to investigate the mind and behavior.

It involves the systematic and controlled study of human and animal behavior through observation and experimentation .

Experimental psychologists design and conduct experiments to understand cognitive processes, perception, learning, memory, emotion, and many other aspects of psychology. They often manipulate variables ( independent variables ) to see how this affects behavior or mental processes (dependent variables).

The findings from experimental psychology research are often used to better understand human behavior and can be applied in a range of contexts, such as education, health, business, and more.

Experimental Psychology Examples

1. The Puzzle Box Studies (Thorndike, 1898) Placing different cats in a box that can only be escaped by pulling a cord, and then taking detailed notes on how long it took for them to escape allowed Edward Thorndike to derive the Law of Effect: actions followed by positive consequences are more likely to occur again, and actions followed by negative consequences are less likely to occur again (Thorndike, 1898).

2. Reinforcement Schedules (Skinner, 1956) By placing rats in a Skinner Box and changing when and how often the rats are rewarded for pressing a lever, it is possible to identify how each schedule results in different behavior patterns (Skinner, 1956). This led to a wide range of theoretical ideas around how rewards and consequences can shape the behaviors of both animals and humans.

3. Observational Learning (Bandura, 1980) Some children watch a video of an adult punching and kicking a Bobo doll. Other children watch a video in which the adult plays nicely with the doll. By carefully observing the children’s behavior later when in a room with a Bobo doll, researchers can determine if television violence affects children’s behavior (Bandura, 1980).

4. The Fallibility of Memory (Loftus & Palmer, 1974) A group of participants watch the same video of two cars having an accident. Two weeks later, some are asked to estimate the rate of speed the cars were going when they “smashed” into each other. Some participants are asked to estimate the rate of speed the cars were going when they “bumped” into each other. Changing the phrasing of the question changes the memory of the eyewitness.

5. Intrinsic Motivation in the Classroom (Dweck, 1990) To investigate the role of autonomy on intrinsic motivation, half of the students are told they are “free to choose” which tasks to complete. The other half of the students are told they “must choose” some of the tasks. Researchers then carefully observe how long the students engage in the tasks and later ask them some questions about if they enjoyed doing the tasks or not.

6. Systematic Desensitization (Wolpe, 1958) A clinical psychologist carefully documents his treatment of a patient’s social phobia with progressive relaxation. At first, the patient is trained to monitor, tense, and relax various muscle groups while viewing photos of parties. Weeks later, they approach a stranger to ask for directions, initiate a conversation on a crowded bus, and attend a small social gathering. The therapist’s notes are transcribed into a scientific report and published in a peer-reviewed journal.

7. Study of Remembering (Bartlett, 1932) Bartlett’s work is a seminal study in the field of memory, where he used the concept of “schema” to describe an organized pattern of thought or behavior. He conducted a series of experiments using folk tales to show that memory recall is influenced by cultural schemas and personal experiences.

8. Study of Obedience (Milgram, 1963) This famous study explored the conflict between obedience to authority and personal conscience. Milgram found that a majority of participants were willing to administer what they believed were harmful electric shocks to a stranger when instructed by an authority figure, highlighting the power of authority and situational factors in driving behavior.

9. Pavlov’s Dog Study (Pavlov, 1927) Ivan Pavlov, a Russian physiologist, conducted a series of experiments that became a cornerstone in the field of experimental psychology. Pavlov noticed that dogs would salivate when they saw food. He then began to ring a bell each time he presented the food to the dogs. After a while, the dogs began to salivate merely at the sound of the bell. This experiment demonstrated the principle of “classical conditioning.”

10, Piaget’s Stages of Development (Piaget, 1958) Jean Piaget proposed a theory of cognitive development in children that consists of four distinct stages: the sensorimotor stage (birth to 2 years), where children learn about the world through their senses and motor activities, through to the the formal operational stage (12 years and beyond), where abstract reasoning and hypothetical thinking develop. Piaget’s theory is an example of experimental psychology as it was developed through systematic observation and experimentation on children’s problem-solving behaviors .

Types of Research Methodologies in Experimental Psychology 

Researchers utilize several different types of research methodologies since the early days of Wundt (1832-1920).

1. The Experiment

The experiment involves the researcher manipulating the level of one variable, called the Independent Variable (IV), and then observing changes in another variable, called the Dependent Variable (DV).

The researcher is interested in determining if the IV causes changes in the DV. For example, does television violence make children more aggressive?

So, some children in the study, called research participants, will watch a show with TV violence, called the treatment group. Others will watch a show with no TV violence, called the control group.

So, there are two levels of the IV: violence and no violence. Next, children will be observed to see if they act more aggressively. This is the DV.

If TV violence makes children more aggressive, then the children that watched the violent show will me more aggressive than the children that watched the non-violent show.

A key requirement of the experiment is random assignment . Each research participant is assigned to one of the two groups in a way that makes it a completely random process. This means that each group will have a mix of children: different personality types, diverse family backgrounds, and range of intelligence levels.

2. The Longitudinal Study

A longitudinal study involves selecting a sample of participants and then following them for years, or decades, periodically collecting data on the variables of interest.

For example, a researcher might be interested in determining if parenting style affects academic performance of children. Parenting style is called the predictor variable , and academic performance is called the outcome variable .

Researchers will begin by randomly selecting a group of children to be in the study. Then, they will identify the type of parenting practices used when the children are 4 and 5 years old.

A few years later, perhaps when the children are 8 and 9, the researchers will collect data on their grades. This process can be repeated over the next 10 years, including through college.

If parenting style has an effect on academic performance, then the researchers will see a connection between the predictor variable and outcome variable.

Children raised with parenting style X will have higher grades than children raised with parenting style Y.

3. The Case Study

The case study is an in-depth study of one individual. This is a research methodology often used early in the examination of a psychological phenomenon or therapeutic treatment.

For example, in the early days of treating phobias, a clinical psychologist may try teaching one of their patients how to relax every time they see the object that creates so much fear and anxiety, such as a large spider.

The therapist would take very detailed notes on how the teaching process was implemented and the reactions of the patient. When the treatment had been completed, those notes would be written in a scientific form and submitted for publication in a scientific journal for other therapists to learn from.

There are several other types of methodologies available which vary different aspects of the three described above. The researcher will select a methodology that is most appropriate to the phenomenon they want to examine.

They also must take into account various practical considerations such as how much time and resources are needed to complete the study. Conducting research always costs money.

People and equipment are needed to carry-out every study, so researchers often try to obtain funding from their university or a government agency. 

Origins and Key Developments in Experimental Psychology

timeline of experimental psychology, explained below

Wilhelm Maximilian Wundt (1832-1920) is considered one of the fathers of modern psychology. He was a physiologist and philosopher and helped establish psychology as a distinct discipline (Khaleefa, 1999).  

In 1879 he established the world’s first psychology research lab at the University of Leipzig. This is considered a key milestone for establishing psychology as a scientific discipline. In addition to being the first person to use the term “psychologist,” to describe himself, he also founded the discipline’s first scientific journal Philosphische Studien in 1883.

Another notable figure in the development of experimental psychology is Ernest Weber . Trained as a physician, Weber studied sensation and perception and created the first quantitative law in psychology.

The equation denotes how judgments of sensory differences are relative to previous levels of sensation, referred to as the just-noticeable difference (jnd). This is known today as Weber’s Law (Hergenhahn, 2009).    

Gustav Fechner , one of Weber’s students, published the first book on experimental psychology in 1860, titled Elemente der Psychophysik. His worked centered on the measurement of psychophysical facets of sensation and perception, with many of his methods still in use today.    

The first American textbook on experimental psychology was Elements of Physiological Psychology, published in 1887 by George Trumball Ladd .

Ladd also established a psychology lab at Yale University, while Stanley Hall and Charles Sanders continued Wundt’s work at a lab at Johns Hopkins University.

In the late 1800s, Charles Pierce’s contribution to experimental psychology is especially noteworthy because he invented the concept of random assignment (Stigler, 1992; Dehue, 1997).

Go Deeper: 15 Random Assignment Examples

This procedure ensures that each participant has an equal chance of being placed in any of the experimental groups (e.g., treatment or control group). This eliminates the influence of confounding factors related to inherent characteristics of the participants.

Random assignment is a fundamental criterion for a study to be considered a valid experiment.

From there, experimental psychology flourished in the 20th century as a science and transformed into an approach utilized in cognitive psychology, developmental psychology, and social psychology .

Today, the term experimental psychology refers to the study of a wide range of phenomena and involves methodologies not limited to the manipulation of variables.

The Scientific Process and Experimental Psychology

The one thing that makes psychology a science and distinguishes it from its roots in philosophy is the reliance upon the scientific process to answer questions. This makes psychology a science was the main goal of its earliest founders such as Wilhelm Wundt.

There are numerous steps in the scientific process, outlined in the graphic below.

an overview of the scientific process, summarized in text in the appendix

1. Observation

First, the scientist observes an interesting phenomenon that sparks a question. For example, are the memories of eyewitnesses really reliable, or are they subject to bias or unintentional manipulation?

2. Hypothesize

Next, this question is converted into a testable hypothesis. For instance: the words used to question a witness can influence what they think they remember.

3. Devise a Study

Then the researcher(s) select a methodology that will allow them to test that hypothesis. In this case, the researchers choose the experiment, which will involve randomly assigning some participants to different conditions.

In one condition, participants are asked a question that implies a certain memory (treatment group), while other participants are asked a question which is phrased neutrally and does not imply a certain memory (control group).

The researchers then write a proposal that describes in detail the procedures they want to use, how participants will be selected, and the safeguards they will employ to ensure the rights of the participants.

That proposal is submitted to an Institutional Review Board (IRB). The IRB is comprised of a panel of researchers, community representatives, and other professionals that are responsible for reviewing all studies involving human participants.

4. Conduct the Study

If the IRB accepts the proposal, then the researchers may begin collecting data. After the data has been collected, it is analyzed using a software program such as SPSS.

Those analyses will either support or reject the hypothesis. That is, either the participants’ memories were affected by the wording of the question, or not.

5. Publish the study

Finally, the researchers write a paper detailing their procedures and results of the statistical analyses. That paper is then submitted to a scientific journal.

The lead editor of that journal will then send copies of the paper to 3-5 experts in that subject. Each of those experts will read the paper and basically try to find as many things wrong with it as possible. Because they are experts, they are very good at this task.

After reading those critiques, most likely, the editor will send the paper back to the researchers and require that they respond to the criticisms, collect more data, or reject the paper outright.

In some cases, the study was so well-done that the criticisms were minimal and the editor accepts the paper. It then gets published in the scientific journal several months later.

That entire process can easily take 2 years, usually more. But, the findings of that study went through a very rigorous process. This means that we can have substantial confidence that the conclusions of the study are valid.

Experimental psychology refers to utilizing a scientific process to investigate psychological phenomenon.

There are a variety of methods employed today. They are used to study a wide range of subjects, including memory, cognitive processes, emotions and the neurophysiological basis of each.

The history of psychology as a science began in the 1800s primarily in Germany. As interest grew, the field expanded to the United States where several influential research labs were established.

As more methodologies were developed, the field of psychology as a science evolved into a prolific scientific discipline that has provided invaluable insights into human behavior.

Bartlett, F. C., & Bartlett, F. C. (1995).  Remembering: A study in experimental and social psychology . Cambridge university press.

Dehue, T. (1997). Deception, efficiency, and random groups: Psychology and the gradual origination of the random group design. Isis , 88 (4), 653-673.

Ebbinghaus, H. (2013). Memory: A contribution to experimental psychology.  Annals of neurosciences ,  20 (4), 155.

Hergenhahn, B. R. (2009). An introduction to the history of psychology. Belmont. CA: Wadsworth Cengage Learning .

Khaleefa, O. (1999). Who is the founder of psychophysics and experimental psychology? American Journal of Islam and Society , 16 (2), 1-26.

Loftus, E. F., & Palmer, J. C. (1974).  Reconstruction of auto-mobile destruction : An example of the interaction between language and memory.  Journal of Verbal Learning and Verbal behavior , 13, 585-589.

Pavlov, I.P. (1927). Conditioned reflexes . Dover, New York.

Piaget, J. (1959).  The language and thought of the child  (Vol. 5). Psychology Press.

Piaget, J., Fraisse, P., & Reuchlin, M. (2014). Experimental psychology its scope and method: Volume I (Psychology Revivals): History and method . Psychology Press.

Skinner, B. F. (1956). A case history in scientlfic method. American Psychologist, 11 , 221-233

Stigler, S. M. (1992). A historical view of statistical concepts in psychology and educational research. American Journal of Education , 101 (1), 60-70.

Thorndike, E. L. (1898). Animal intelligence: An experimental study of the associative processes in animals. Psychological Review Monograph Supplement 2 .

Wolpe, J. (1958). Psychotherapy by reciprocal inhibition. Stanford, CA: Stanford University Press.

Appendix: Images reproduced as Text

Definition: Experimental psychology is a branch of psychology that focuses on conducting systematic and controlled experiments to study human behavior and cognition.

Overview: Experimental psychology aims to gather empirical evidence and explore cause-and-effect relationships between variables. Experimental psychologists utilize various research methods, including laboratory experiments, surveys, and observations, to investigate topics such as perception, memory, learning, motivation, and social behavior .

Example: The Pavlov’s Dog experimental psychology experiment used scientific methods to develop a theory about how learning and association occur in animals. The same concepts were subsequently used in the study of humans, wherein psychology-based ideas about learning were developed. Pavlov’s use of the empirical evidence was foundational to the study’s success.

Experimental Psychology Milestones:

1890: William James publishes “The Principles of Psychology”, a foundational text in the field of psychology.

1896: Lightner Witmer opens the first psychological clinic at the University of Pennsylvania, marking the beginning of clinical psychology.

1913: John B. Watson publishes “Psychology as the Behaviorist Views It”, marking the beginning of Behaviorism.

1920: Hermann Rorschach introduces the Rorschach inkblot test.

1938: B.F. Skinner introduces the concept of operant conditioning .

1967: Ulric Neisser publishes “Cognitive Psychology” , marking the beginning of the cognitive revolution.

1980: The third edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III) is published, introducing a new classification system for mental disorders.

The Scientific Process

  • Observe an interesting phenomenon
  • Formulate testable hypothesis
  • Select methodology and design study
  • Submit research proposal to IRB
  • Collect and analyzed data; write paper
  • Submit paper for critical reviews

Dave

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

  • Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Positive Punishment Examples
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Chris

Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

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  • Case Studies
  • Literature Reviews
  • Your Own Study/Experiment

Are you searching for a great topic for your psychology paper ? Sometimes it seems like coming up with topics of psychology research is more challenging than the actual research and writing. Fortunately, there are plenty of great places to find inspiration and the following list contains just a few ideas to help get you started.

Finding a solid topic is one of the most important steps when writing any type of paper. It can be particularly important when you are writing a psychology research paper or essay. Psychology is such a broad topic, so you want to find a topic that allows you to adequately cover the subject without becoming overwhelmed with information.

In some cases, such as in a general psychology class, you might have the option to select any topic from within psychology's broad reach. Other instances, such as in an  abnormal psychology  course, might require you to write your paper on a specific subject such as a psychological disorder.

As you begin your search for a topic for your psychology paper, it is first important to consider the guidelines established by your instructor.

Research Topics Within Specific Branches of Psychology

The key to selecting a good topic for your psychology paper is to select something that is narrow enough to allow you to really focus on the subject, but not so narrow that it is difficult to find sources or information to write about.

One approach is to narrow your focus down to a subject within a specific branch of psychology. For example, you might start by deciding that you want to write a paper on some sort of social psychology topic. Next, you might narrow your focus down to how persuasion can be used to influence behavior .

Other social psychology topics you might consider include:

  • Prejudice and discrimination (i.e., homophobia, sexism, racism)
  • Social cognition
  • Person perception
  • Social control and cults
  • Persuasion, propaganda, and marketing
  • Attraction, romance, and love
  • Nonverbal communication
  • Prosocial behavior

Psychology Research Topics Involving a Disorder or Type of Therapy

Exploring a psychological disorder or a specific treatment modality can also be a good topic for a psychology paper. Some potential abnormal psychology topics include specific psychological disorders or particular treatment modalities, including:

  • Eating disorders
  • Borderline personality disorder
  • Seasonal affective disorder
  • Schizophrenia
  • Antisocial personality disorder
  • Profile a  type of therapy  (i.e., cognitive-behavioral therapy, group therapy, psychoanalytic therapy)

Topics of Psychology Research Related to Human Cognition

Some of the possible topics you might explore in this area include thinking, language, intelligence, and decision-making. Other ideas might include:

  • False memories
  • Speech disorders
  • Problem-solving

Topics of Psychology Research Related to Human Development

In this area, you might opt to focus on issues pertinent to  early childhood  such as language development, social learning, or childhood attachment or you might instead opt to concentrate on issues that affect older adults such as dementia or Alzheimer's disease.

Some other topics you might consider include:

  • Language acquisition
  • Media violence and children
  • Learning disabilities
  • Gender roles
  • Child abuse
  • Prenatal development
  • Parenting styles
  • Aspects of the aging process

Do a Critique of Publications Involving Psychology Research Topics

One option is to consider writing a critique paper of a published psychology book or academic journal article. For example, you might write a critical analysis of Sigmund Freud's Interpretation of Dreams or you might evaluate a more recent book such as Philip Zimbardo's  The Lucifer Effect: Understanding How Good People Turn Evil .

Professional and academic journals are also great places to find materials for a critique paper. Browse through the collection at your university library to find titles devoted to the subject that you are most interested in, then look through recent articles until you find one that grabs your attention.

Topics of Psychology Research Related to Famous Experiments

There have been many fascinating and groundbreaking experiments throughout the history of psychology, providing ample material for students looking for an interesting term paper topic. In your paper, you might choose to summarize the experiment, analyze the ethics of the research, or evaluate the implications of the study. Possible experiments that you might consider include:

  • The Milgram Obedience Experiment
  • The Stanford Prison Experiment
  • The Little Albert Experiment
  • Pavlov's Conditioning Experiments
  • The Asch Conformity Experiment
  • Harlow's Rhesus Monkey Experiments

Topics of Psychology Research About Historical Figures

One of the simplest ways to find a great topic is to choose an interesting person in the  history of psychology  and write a paper about them. Your paper might focus on many different elements of the individual's life, such as their biography, professional history, theories, or influence on psychology.

While this type of paper may be historical in nature, there is no need for this assignment to be dry or boring. Psychology is full of fascinating figures rife with intriguing stories and anecdotes. Consider such famous individuals as Sigmund Freud, B.F. Skinner, Harry Harlow, or one of the many other  eminent psychologists .

Psychology Research Topics About a Specific Career

​Another possible topic, depending on the course in which you are enrolled, is to write about specific career paths within the  field of psychology . This type of paper is especially appropriate if you are exploring different subtopics or considering which area interests you the most.

In your paper, you might opt to explore the typical duties of a psychologist, how much people working in these fields typically earn, and the different employment options that are available.

Topics of Psychology Research Involving Case Studies

One potentially interesting idea is to write a  psychology case study  of a particular individual or group of people. In this type of paper, you will provide an in-depth analysis of your subject, including a thorough biography.

Generally, you will also assess the person, often using a major psychological theory such as  Piaget's stages of cognitive development  or  Erikson's eight-stage theory of human development . It is also important to note that your paper doesn't necessarily have to be about someone you know personally.

In fact, many professors encourage students to write case studies on historical figures or fictional characters from books, television programs, or films.

Psychology Research Topics Involving Literature Reviews

Another possibility that would work well for a number of psychology courses is to do a literature review of a specific topic within psychology. A literature review involves finding a variety of sources on a particular subject, then summarizing and reporting on what these sources have to say about the topic.

Literature reviews are generally found in the  introduction  of journal articles and other  psychology papers , but this type of analysis also works well for a full-scale psychology term paper.

Topics of Psychology Research Based on Your Own Study or Experiment

Many psychology courses require students to design an actual psychological study or perform some type of experiment. In some cases, students simply devise the study and then imagine the possible results that might occur. In other situations, you may actually have the opportunity to collect data, analyze your findings, and write up your results.

Finding a topic for your study can be difficult, but there are plenty of great ways to come up with intriguing ideas. Start by considering your own interests as well as subjects you have studied in the past.

Online sources, newspaper articles, books , journal articles, and even your own class textbook are all great places to start searching for topics for your experiments and psychology term papers. Before you begin, learn more about  how to conduct a psychology experiment .

What This Means For You

After looking at this brief list of possible topics for psychology papers, it is easy to see that psychology is a very broad and diverse subject. While this variety makes it possible to find a topic that really catches your interest, it can sometimes make it very difficult for some students to select a good topic.

If you are still stumped by your assignment, ask your instructor for suggestions and consider a few from this list for inspiration.

  • Hockenbury, SE & Nolan, SA. Psychology. New York: Worth Publishers; 2014.
  • Santrock, JW. A Topical Approach to Lifespan Development. New York: McGraw-Hill Education; 2016.

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

  • Open access
  • Published: 18 March 2024

Linking undergraduates’ future work self and employability: a moderated mediation model

  • Yaju Ma 1 ,
  • Lingyan Hou 2 ,
  • Wenjing Cai 3 ,
  • Xiaopei Gao 2 &
  • Lin Jiang 3  

BMC Psychology volume  12 , Article number:  160 ( 2024 ) Cite this article

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Metrics details

The career intentions of students play a crucial role in shaping the growth of the hospitality and tourism industry. Previous research underlines the significance of future work self in predicting outcomes related to one’s career. However, there is limited knowledge regarding the precise ways, timing, and conditions under which the future work self of undergraduate students can enhance their employability.

This paper aims to address the existing research gap by employing career construction theory and self-determination theory to propose a moderated mediation model—i.e., career exploration serves as a mediator and job market knowledge functions as a moderator in the relationship between future work self and employability. We conducted two independent studies (i.e., an experimental study and a time-lagged field study) to test the proposed model. Specifically, in Study 1 we employed an experimental research design to recruit 61 students majoring in tourism management to participate. They were randomly assigned to two scenarios (future work self: high vs. low), and we manipulated different levels of future work self by means of scenario descriptions. In Study 2, we used the time-lagged research design to collect data via submitting questionnaires among 253 Chinese undergraduates who majored in hospitality and tourism at a university in the middle area of China.

The results indicate a positive correlation between undergraduates’ future work self and their employability. Furthermore, this relationship is mediated by a mediator of career exploration. It is important to note that this mediating relationship is also contingent upon the moderator variable of undergraduates’ job market knowledge when considering the impact of career exploration on employability.

The findings contribute to enriching the current understanding of the positive effects of future work self on undergraduates’ desirable outcomes in employability.

Peer Review reports

Introduction

Human resources, as a significant determinant of the growth of the hospitality industry, highlights the importance of undergraduates specializing in the hospitality and tourism industry [ 1 ]. A recent review correspondingly indicated that students’ career intentions determine the need for hospitality and tourism management programs from the perspective of the hospitality and tourism industry [ 2 ]. In light of the uncertainties surrounding employment opportunities and the substantial decrease in job openings during the transition from school to work due to the COVID-19 pandemic [ 3 ], universities are prioritizing the development of undergraduates’ employability to help them gain employment and be successful in their chosen occupations after graduation [ 4 ], especially for educating undergraduates’ majored in hospitality and tourism relevant areas. Given the heightened risk of COVID-19 transmission within the hospitality sector [ 5 ], these undergraduates’ pessimistic perception about the current and future workforce of the tourism industry significantly influences their career attitudes and behaviors toward their future jobs [ 6 , 7 ]. In addition, some research findings have suggested that newcomers, especially graduates, encounter higher occurrences of job mismatching and underemployment [ 8 ]. Consequently, with the goal of increasing undergraduates’ capabilities to transition from school to work, scholars have empirically demonstrated that developing students’ personal characteristics (e.g., proactivity, career adaptability, and knowledge, skills, and attitudes) increases their employability in the future job market [ 9 ].

Scholars argue the necessity for universities to not only focus on improving students’ employability skills but also on fostering their career motivation [ 10 ]. For students who have not yet entered the workplace, their future work selves—i.e., their thoughts and hopes about their future jobs—are the driving force behind their proactive career preparations and early job search behaviors [ 11 ]. Future work self is an important motivational resource for proactive career behavior, individuals with a high level of future work self tend to engage in proactive career behaviors (e.g., career planning) toward a better employment status in the future [ 12 ]. Existing research suggests that motivation is a significant and substantial predictor of student employability [ 13 , 14 ]. However, empirical research on the relationship between students’ future work self, an important motivational resource, and perceived employability is still lacking, and the mechanism of whether and how future work self act on students’ perceived employability remain unclear. Thus, this study aims to address the following research question:  whether, how and when undergraduates’ future work self contributes to their perceived employability .

Career construction theory proposes that the concept of future work self serves as a source of motivation, encouraging individuals to invest more effort in career-related behaviors by developing goals and strategies for their future work [ 11 , 15 , 16 ]. Meanwhile, self-determination theory emphasizes that intrinsic motivation (i.e., future work self) is positively associated with key attitudes and behaviors [ 17 ]. Specifically, a strong future work self, which represents a significant intrinsic motivation, enables individuals to adopt an exploratory approach in navigating the uncertainty surrounding their future work. This is achieved by developing and accomplishing self-derived goals and strategies that contribute to positive work and career outcomes [ 18 ]. Therefore, we expect that undergraduates with a salient future work self would have a self-starting motive to explore their career toward boosting their success in the future job market (i.e., employability). Furthermore, as career construction theory’s suggestion that environmental factors can influence an individual’s career development [ 19 ], those who are aware of relevant contextual cues can actively process career-related information and advance their careers [ 20 ]. Accordingly, students who possess comprehensive knowledge about the job market are more likely to be concerned about their future career trajectory and engage in more activities related to career exploration, all in the pursuit of increasing their employability in the future job market, compared to those lacking such knowledge [ 21 ]. Taking all of this into consideration, we propose our hypothesized model in Fig.  1 .

figure 1

Conceptual model

With this research, we make two main contributions to the literature. First, by linking the positive association between the future work self and undergraduates’ perceived employability, we enrich the current academic understandings of career construction theory [ 19 ] and self-determination theory [ 22 ]. That is, we empirically link the motivational benefits of the future work self to undergraduates’ employability in the future job market. Meanwhile, with the guidance of two distinct theoretical perspectives [ 23 , 24 , 25 ], we propose and test career exploration, a central process in students’ career development [ 26 ], as a key mediator in explaining the relationship between the future work self and employability. In this vein, the results enhance the present comprehension of the influence that the future work self has on employability. Second, by examining job market knowledge as a boundary condition for the indirect relationship between future work self and employability in undergraduates’ career exploration, we included the potential moderator of the acquisition of job-related knowledge in the study, which helped to elucidate the role of future work self in relation to career exploration from the perspective of individual dependency characteristics.

Literature Review and Hypothesis Development

Future work self and perceived employability.

In the age of VUCA, the flexible employment relationship and the blurring of organizational boundaries are making individuals’ careers discontinuous and “boundaryless” [ 27 ]. As career paths become more uncertain, individuals need to engage in increasingly proactive career behaviors to enhance their employability [ 28 ] and access jobs and careers that match their values and needs [ 29 ]. To better prepare for the transition from school to work [ 30 ] and to develop employability in a changing organizational environment, it is critical for students to proactively shape their career future and actively manage their careers [ 31 ].

With reference to the research of Rothwell, Herbert, & Rothwell (2008), we defined the employability as the ability perceived by university students to maintain existing jobs and obtain desired jobs. It is categorized into internal and external dimensions [ 32 ]. Specifically, internal employability refers to the self-evaluation and career value perception felt by employees in the organization, while external employability refers to the willingness and ability of employees to transfer to other organizations, reflecting the value of employees in the external labor market [ 33 ]. Scholars have also suggested that the perception of employability is influenced by self-concept [ 34 ]. In other words, students’ personal traits play a significant role in predicting their future employability perception [ 35 ]. When students become job seekers, they begin to focus more on their future career direction than before and are concerned about their future employability [ 36 ]. We consider that students’ perceived employability while constructing their careers is closely related to their future work selves.

The future work self is a conceptual representation of an individual’s aspirations and hopes for their future self in the work domain [ 24 ]. Compared to the general concept of the “possible self”, the future work self is future-oriented, work-related, and includes the two attributes of salience and elaboration [ 37 ]. Specifically, future work self-salience refers to the degree to which the person’s future work self is clear and imaginable. Future work self-elaboration can be extrapolated from the complex and detailed descriptions of representations of the future self [ 24 ]. According to career construction theory, individuals should consider their past memory, current experience, and future aspirations to make their career behavioral choices [ 19 ]. The future work self potentially expands undergraduates’ aspirations and develops their thinking about future career possibilities [ 16 ], which enables them to proactively prepare for enhancing their employability [ 38 ]. According to self-determination theory, the future work self serves as a motivational career resource that can motivate students to engage in current goal-setting and goal-striving behaviors to achieve a desired future [ 22 , 39 ]. Students who have a higher level of salience and elaboration about their future work self can not only clearly depict the image of their future work [ 40 ] but also take the initiative to learn job-related knowledge and skills needed in career development [ 12 ] to purposefully enhance a series of comprehensive abilities and strengthen their employability [ 41 ]. It follows that the future work self plays a motivating role in increasing students’ perceived employability. Accordingly, we propose the hypothesis as followed:

Hypothesis 1. Future work self is positively related to an undergraduate’s perceived employability.

The mediating role of career exploration

Career exploration is the most essential stage in the career development of students [ 42 ]. Sufficient and proactive exploration contributes to better self-awareness [ 43 ] and greater career-related outcomes. Career exploration involves the exploration of the self and the employment environment, which focuses on carrying out career options, developing abilities, accumulating experiences, and reaching goals [ 26 ]. Students who actively explore their internal and external surroundings can consciously relate their motivations [ 44 ], interests, and abilities to acceptable occupational roles and engage in more goal-oriented behaviors than those who do not [ 45 ]. Through explorations of the self and the environment, students gain a full understanding of their internal characteristics and occupational traits [ 20 ], which helps them seize job opportunities and sustain their competitiveness in the labor market [ 46 ].

Drawing upon career construction theory, we examine the role of the future work self in inspiring career exploration and, subsequently, driving perceived employability among students. Career construction theory posits that career development is an action-oriented process in which individuals establish careers and design their own lives [ 47 ]. Individuals who are willing or flexible to make changes are more likely to engage in career-related activities [ 19 ]. Meanwhile, the career construction model of adaption divides the adaptive construct process into four links: adaptive readiness, adaptability resources, adapting responses and adaptation results [ 19 ]. Career exploration is an important expression of adaptive response, which can help students better cope with career development tasks and changes in the job market environment [ 11 , 23 ]. Specifically, future work self helps students envision desirable futures and highlights discrepancies between current and ideal states. Recognizing these differences enables individuals to visualize the potential challenges they may encounter in pursuing their future career goals and to proactively explore opportunities in their career development process to prepare for these challenges [ 48 ]. Additionally, individuals actively use career resources to adapt to the demands of the dynamic work environment while constructing their careers [ 19 ]. Based on this framework, the future work self enables students to explore and rediscover themselves through the process of identity construction and to actively work toward a future that is consistent with their goals [ 48 ]. As it constitutes the positive possible selves and is a motivational resource in the context of work [ 11 ], studies have suggested that the future work self is positively linked to proactive career behaviors [ 16 , 49 ], such as career planning and skill development. Moreover, when shaping their future work self, students are more likely to seek relevant information and suggestions on environmental clues [ 15 ], which not only provides a clearer image of their occupational self-concept [ 50 ] but also forms “personalized” career planning for constructing themselves [ 51 ]. Therefore, it can be inferred that there is a positive correlation between the future work self and career exploration.

In today’s complex, dynamically shifting labor market, it is crucial to hold a positive perception of employability. A proper assessment of employability can help students proactively choose the right career path that suits their career planning [ 52 ] and cope with work-related challenges and unexpected job transitions [ 53 ]. Scholars have shown that career preparatory behaviors (e.g., exploration) can lead to the development of career-related ideas and attitudes [ 54 ]. According to self-determination theory, when faced with an event that has a significant impact on their career, individuals are motivated to explore new ideas, adjust their behavior and engage with ongoing change to cope with the changing environment and achieve positive career-related outcomes [ 25 ]. When students experience a period of career role transformation and the transition from education to social work, they need to engage in more career exploration activities to actively seek career-relevant experiences [ 55 ], construct their possible selves, and clarify their career path [ 56 ]. Through career exploration, students re-examine themselves, strengthen their skills and formulate strategies to achieve goals [ 57 ], which in turn enhance their employability. This suggests that career exploration is positively associated with perceived employability.

From the standpoint of career construction theory, the concept of the future work self is seen as a valuable source of motivation that empowers individuals to invest greater effort in career-related actions and achieve favorable career results by continually developing and exploring future work objectives and strategies [ 11 , 15 , 16 , 58 ]. Concurrently, according to the self-determination theory, individuals are inclined to actively explore and shape their present roles, leading to positive career outcomes, when they experience strong intrinsic motivation, such as that provided by the future work self [ 18 , 26 ]. Thus, the future work self motivates students to consider their future aspirations, promotes meaningful career exploration behaviors, and thus enhances perceived employability. This means that the future work self is positively associated with career exploration, which, in turn, is positively related to perceived employability. Therefore, we propose the hypothesis as followed:

Hypothesis 2. Career exploration mediates the relationship between future work self and perceived employability.

The moderating role of job market knowledge

From the perspective of career exploration, students exhibit variations in their career motivations. Research indicates that students’ engagement in career preparatory activities is influenced by their personal resources [ 59 ]. These resources can both trigger and constrain career preparatory behaviors [ 59 ], thereby impacting their career development and overall well-being [ 60 ]. As a personally relevant resource, job market knowledge plays an important role in judging the employment situation, making career decisions, and promoting career success [ 61 ]. Job market knowledge refers to the degree to which students are familiar with current labor market developments and future trends [ 62 ]. Research has demonstrated that students who acquire more job market knowledge in their education can be self-motivated to perform specific actions related to career development [ 63 ]. Thus, we posit that the positive impact of career exploration on the perception of employability can be reinforced when students possess a high level of job market knowledge.

According to career construct theory, the environment in which a career develops provides the driving force and guidance for how individuals construct their careers [ 19 ]. Being attentive to contextual cues allows individuals to actively process career-related information and make progress in their careers [ 20 ]. Accordingly, students who have well-equipped job market knowledge are more concerned about their future career direction, engage in more career exploration activities, and become more proactive in developing their careers than those who do not. This well-equipped understanding of the job market serves as a valuable resource, aiding students in better understanding themselves and the external environment [ 64 ], and as a result, enhancing their employability prospects. In contrast, when students possess less job market knowledge, they are blindly optimistic about the labor market and are more reluctant to break out of their comfort zone to carry out career strategies [ 65 ] and enhance their career competencies. As such, due to a lack of awareness of the job market, they exhibit fewer career exploration behaviors and are reserved in boosting their employability [ 66 ]. From the above analysis, we infer that the relationship between career exploration and perceived employability is enhanced when students have a higher level of job market knowledge. Accordingly, we propose the hypothesis as followed:

Hypothesis 3. Job market knowledge moderates the positive relationship between career exploration and perceived employability, such that when an undergraduate’s job market knowledge is higher, this relationship becomes stronger.

The moderated mediation model

Based on the aforementioned hypothesis, we argue that job market knowledge moderates the indirect effects of the future work self on perceived employability through career exploration. Specifically, the influence of the future work self on perceived employability, via career exploration, is amplified when a student possesses a greater level of job market knowledge. According to career construction theory [ 19 ], students with sufficient job market knowledge have a clearer orientation of their future selves in the context of work [ 11 ]. Furthermore, they are more likely to engage in extensive career exploration in order to continuously comprehend their interests [ 67 ], motivations and career aspirations [ 57 ] compared to those who lack such knowledge. Consequently, their perceived employability is enhanced through the creation of a wider spectrum of future possibilities. Conversely, when students possess less job market knowledge, i.e., when they have less knowledge about the future labor market and the current employment situation, they have a vague self-image related to their future jobs [ 38 ] and engage in less career exploration behavior, thus limiting the development of their perceived employability. Drawing on these findings, we propose the hypothesis as followed:

Hypothesi s 4 . Job market knowledge positively moderates the indirect relationship between future work self and perceived employability through career exploration, such that the relationship becomes stronger when an undergraduate’s job market knowledge is higher.

Sampling and procedure

We recruited a total of 65 students majoring in tourism management from a junior college located in central China to participate in a scenario experiment. After excluding four participants who failed the attention check question, we derived data from a valid sample of 61 individuals. Among these participants ( N  = 61), 21 were males (34.4%) and 40 were females (65.6%). Their average age was 19.31 years ( SD  = 1.36). Participants were randomly assigned to two scenarios (future work self: high vs. low). We manipulated different levels of future work self by means of scenario descriptions. After reading the experimental material on future work self, participants were asked to complete the future work self scale based on the scenario material read above. Immediately following this, participants were asked to report information on other variables (career exploration and employability) and provide demographic information based on their true feelings in the scenario. At the end of the experiment, participants were rewarded with a bonus pack.

Manipulation and measures

We developed experimental materials for future work self based on the research by Strauss and Parker [ 68 ]. The specific content of the experimental material was in the appendix.

Future work self. After reading the experimental materials, participants were asked to complete the 5-item scale developed by Strauss et al. (2012) which was widely used to measure future work self in previous studies [ 11 ]. A representative item was “I am very clear about who and what I want to become in my future work (1  =  strongly disagree, 7  =  strongly agree). ” The Cronbach’s α was 0.95.

Career exploration. We used the 12-item scale by Stumpf, Colarelli, and Hartman (1983) to access career exploration [ 69 ]. A 7-point scale was used (1  =  strongly disagree to 7  =  strongly agree) to show the extent to which participants agreed with each item (e.g., “I prepared mentally for my work”). The Cronbach’s α was 0.66.

Perceived employability. We used a 16-item scale developed by Rothwell, Herbert, and Rothwell (2008) to measure student’s employability [ 32 ]. A sample item was “The knowledge and skills I possess are what employers are looking for (1  =  strongly disagree, 7  =  strongly agree). ” The Cronbach’s α was 0.85.

Manipulation check

The results of the ANOVA indicated that participant’s perceived level of future work self was significantly higher in the high level of future work self condition ( M  = 6.49, SD  = 0.47) than in the low level of future work self condition ( M  = 2.50, SD  = 0.45), and the difference between the two conditions was significant (F(1, 59) = 1143.33, p  < 0.001, \({\eta }_{p}^{2}\) = 0.95). Thus, we successfully manipulated the future work self.

Hypotheses testing

Descriptive statistics such as mean, standard deviation and correlation coefficients of the variables were given in Table  1 .

First, we conducted a one-way ANOVA with future work self as the independent variable and employability as the dependent variable. The results showed that different levels of future work self had significantly different effects on students’ perceived employability (F(1, 59) = 14.42, p  < 0.001, \({\eta }_{p}^{2}\) = 0.20). Specifically, the high level of future work self condition ( M  = 3.59, SD  = 0.61) led to the higher level of perceived employability compared to the low level of future work self condition ( M  = 2.94, SD  = 0.91). Therefore, hypothesis 1 was verified.

Second, we used PROCESS to conduct mediation effect test. The results showed that future work self was significantly and positively correlated with career exploration ( b  = 0.43, p  < 0.01). Career exploration was significantly and positively associated with employability ( b  = 0.93, p  < 0.001). Bootstrapping results from a sample of 5,000 showed that the indirect effect of future work self on perceived employability via career exploration was 0.40. And the bootstrapped confidence interval [95% CI: (0.12,0.68)] did not include zero. Thus, the mediating effect was significant, supporting hypothesis 2.

Discussion of study 1

A scenario-based experimental approach was employed in Study 1 to test the model. The experimental results showed that the main and mediating effects of the theoretical hypotheses model proposed in this study were valid. The experimental study tested the causal relationships between the independent and mediating variables, and between the dependent and outcome variables, further enhancing the validity of the findings of the study on the mechanism of the influence of future work self on students’ perceived employability. To further test the impact of the moderating variables, Study 2, a questionnaire study, was conducted.

We used the time-lagged research design to collect data via submitting questionnaires among Chinese undergraduates at a university in the middle area of China. One of the authors, as a teaching assistant of a career development course at this university, extended an invitation to the undergraduate students to complete the questionnaires in the classroom. Specifically, the author introduced the topic of this study to 495 students who majored in hospitality and tourism, and asked them to participate in this study. After receiving conformation to participate from 288 undergraduates, the author submitted the online questionnaire to them. Specifically, the questionnaire was uploaded to Wenjuanxing which is an online questionnaire system widely used in academic study in China. The author subsequently shared the questionnaire link, generated by Wenjuanxing, with the students on WeChat, the most prominent Chinese social media platform, and invited them to participate in filling out the questionnaire on WeChat. The time-lagged research design was employed in the current study with an eight-week time interval. At time 1 (T1), these undergraduates were asked to report their future work self, career exploration, job market knowledge, and their demographic information. Eight weeks later, at time 2 (T2), they were asked to rate their perception about their employability. After matching their two sets of responses, a valid sample of 253 undergraduates were used in the study. Of those reporting, participants included 153 males (60.5%) and 100 females (39.5%), and their average age was 21.68 years old ( SD  = 3.20). The sample consisted of 70.8% undergraduate students ( N  = 179), 18.5% master students ( N  = 47) and 10.7% doctoral students ( N  = 27).

All the measurement scales are mature English scales, and the translation-back translation method is used to ensure the Chinese versions can accurately express the original concepts [ 70 ]. Before the formal distribution of the questionnaires, we invited undergraduates to take a pre-survey and revised certain items that were inaccurately stated, inappropriate and difficult to understand based on undergraduates’ feedbacks and experts’ advice.

Future work self and career exploration scales used in Study 2 were all consistent with those used in Study 1. The Cronbach’s α for the two scales were 0.91 and 0.92. We employed the three-item scale developed by Hodzic, Ripoll, Lira, and Zenasni (2015) to measure undergraduate’s employability [ 71 ]. The scale combined with the Likert-7 point scoring method has been widely used to evaluate employability ( 1 = strongly disagree to 7 = strongly agree ). A representative item was “in the current job market situation, I think it is possible to find an interesting job.” The Cronbach’s α of this scale was 0.90. Job market knowledge was measured using a three-item scale compiled by Hirschi, Nagy, Baumeler, Johnston, and Spurk (2018) [ 72 ]. Participants were asked to rate a 5-point Likert scale of 1 = strongly disagree to 5 = strongly agree. The representative item was “I have a good knowledge of the job market.” The Cronbach’s α for job market knowledge was 0.96.

We controlled respondents’ age, gender, and education level. According to previous studies, an individual’s employability increases with age [ 73 ] and the level of education [ 74 ]. We also noticed that women appear to be more confident in their employment opportunities when they are unemployed [ 75 ]. Therefore, we statistically controlled these variables for their potential influences. In addition, since researchers have suggested that such environmental-oriented factor as supports from family and schools may exert influences on students’ career-related behaviors and attitudes during school-to-work transition (e.g., perceived employability, and career explorations) [ 76 ], we in the current study controlled career support from school by using the 6-item scale from Sturges et al. (2002) [ 77 ]. A representative item was “I have been given training to help develop my career.” The Cronbach’s α of this scale was 0.77.

Analytical strategy

Firstly, we conducted reliability and validity tests on the data using SPSS 26.0 and AMOS 26.0. Next, we use hierarchical regression to test for mediating and moderating effects with SPSS 26.0 to support the hypotheses. Finally, to further elucidate the indirect effect and the validation of the result, we used the PROCESS procedure by Hayes developed in SPSS [ 78 ] to generate a confidence interval (CI) using a bootstrap program with 5000 sample size.

Confirmatory factor analysis

Before testing hypotheses, we adopted AMOS 26.0 to conduct confirmatory factor analysis on four variables: future work self, career exploration, job market knowledge, and perceived employability to examine the discriminant validity among the variables. As shown in Table  2 , the four-factor model was significantly better than the other competing models and demonstrated a good fit (χ 2 / df  = 2.79, CFI  = 0.93, RMSEA  = 0.08, IFI  = 0.93, TLI  = 0.91), which indicated that the variables of the measurement model have good discriminant validity.

Collinearity evaluation is also carried out to find out whether there is collinearity in the model. To test collinearity, VIF calculation is needed for each construct. If the VIF score is higher than 5, then the model has collinearity in the educational psychology domain [ 79 ]. The results of the collinearity assessments showed that all VIF scores were less than 4.4, meaning that no pathological collinearity issue existed in the model.

In addition, since the data was collected form only one source (i.e., students), we conducted two methods to identify the potential for common method bias (CMB). We first followed the explanatory factor analysis from Harman (1976) [ 80 ], and the results showed that one factor accounted for 31.25%, which is below the accepted threshold of 40%. Meanwhile, we conducted the test of the one-factor measurement model [ 81 ], generating a poor fit to the data. Taken together, CMB is not a serious problem in our study.

Descriptive statistics

Table 3 presents the results of descriptive statistics such as the mean, standard deviation, and correlation coefficient of the variables. In line with our expectations, the results of Pearson correlation analysis show that future work self is significantly related to career exploration ( r  = 0.61, p  < 0.01) and is positively related to perceived employability ( r  = 0.48, p  < 0.01). Moreover, career exploration presents a positive relationship with participants’ perceived employability ( r  = 0.51, p  < 0.01). These results give initial support for the hypotheses.

In the current paper, the hypotheses were verified by means of the hierarchical regression approach and Bootstrap method. We used SPSS 26.0 and the SPSS macro program PROCESS for data analysis. Table 4 reports our results and the specific analysis results are as follows.

As shown in Model 4 in Table  4 , when participants’ gender, age, and education were controlled for, the positive effect of future work self on undergraduate’s perceived employability is significant ( b  = 0.35, p  < 0.001). Thus, the results support hypothesis 1.

After considering all the control variables, the results shown in Table  4 Model 2 indicate that future work self is significantly and positively associated with career exploration ( b  = 0.421, p  < 0.001) As shown in Table  4 Model 5, career exploration positively affects undergraduates’ perceived employability after controlling for future work self ( b  = 0.40, p  < 0.001). Further, we adopted the Bootstrap method to probe the indirect effects [ 82 ] and set 5000 bootstrapped samples. The results show that the indirect effect of future work self on undergraduate’s perceived employability via career exploration was 0.15 with a 95% confidence interval of [0.08,0.24], and the upper and lower intervals do not contain zero which suggests mediation is indicated. Therefore, hypothesis 2 is supported.

Hypothesis 3 proposes that job market knowledge moderates the relationship between career exploration and perceived employability. As shown in Table  4 Model 7, the interaction term of “career exploration” × “job market knowledge” is significantly and positively related to the perceived employability ( b  = 0.14, p  < 0.01). To further test the moderating effect of job market knowledge, interaction effects are plotted at high (+ 1 SD) and low (-1 SD) levels of job market knowledge. As showed in Fig.  2 , a simple slope test reveals that career exploration shows a significant tendency to enhance perceived employability at high levels ( b  = 0.61, t  = 6.40, p  < 0.001) and low levels ( b  = 0.35, t  = 3.88, p  < 0.001) of job market knowledge. Thus, hypothesis 3 is supported.

figure 2

Interaction between career exploration and job market knowledge on perceived employability

Further, we employed the Bootstrap method to test for moderated mediation effect and set 5000 repeated sampling times to obtain an indirect effect and 95% confidence intervals for future work self on perceived employability when job market knowledge is one standard deviation higher or lower than the mean. As can be seen from Table  5 , for undergraduates with less job market knowledge, the indirect effect is 0.13, and the bootstrapped confidence interval (95% CI: [0.01, 0.23]) excludes zero. For undergraduates with medium job market knowledge, the indirect effect was 0.19, with a 95% confidence interval [0.07,0.27], excluding zero. For undergraduates with high job market knowledge, the indirect effect is 0.26, and a 95% confidence interval is [0.12, 0.32] excluding zero. The indirect effect of the difference between two conditions (high and low conditions of job market knowledge) is 0.16 with a 95% confidence interval of [0.01, 0.30]. The interval excludes 0 and the difference is significant. In conclusion, job market knowledge significantly moderates the indirect effect of future work self on undergraduates’ perceived employability. Hypothesis 4 is verified.

Discussion of study 2

Study 2 used a questionnaire method to test the overall model and the data results supported the hypotheses of this study. The findings revealed that the positive relationship between future work self and employability was mediated by career exploration. In addition, job market knowledge positively moderated the indirect relationship between future work self and perceived employability through career exploration, such that the relationship became stronger when an undergraduate’s job market knowledge is higher.

Discussions

Overview of findings.

Aiming at examining how and when undergraduates’ future work self contributes to their perceived employability by utilizing career construction theory and self-determination theory, the current research conducts two independent studies (i.e., an experimental study and a time-lagged field study) to investigate the role of career exploration as a mediator and job market knowledge as a moderator. The results indicate that undergraduates’ future work self is positively related to their perceived employability through increasing their career exploration. In addition, when undergraduates’ job market knowledge is high, their career exploration is more likely to boost their employability, and their future work self is also more likely to improve their employability via enhancing their career exploration.

Theoretical implications

By employing career construction theory and self-determination theory, we provide theoretical implications. First, the results demonstrate that individuals’ positive self-concepts have significant and beneficial effects on the development of undergraduates’ perceived employability [ 83 ]. Specifically, the typical self-concept—i.e., the future work self—highlighting future orientations represents individuals’ strong motivations, perceptions, and behaviors [ 84 ], which effectively guides individuals’ processing of self-relevant information toward a better outcome [ 85 ]. In the domain of career development, since graduates’ future work selves serve as guides or references for developing their career-related abilities, knowledge and skills in the future workplace [ 28 ], undergraduates with a high level of future work self have identity-based motivation toward career planning, skill development, and networking [ 16 ]. That is, their current career-related behavior is consistent with their characteristics and aimed toward the attainment of their desired future, such as being employable [ 86 ]. These findings are aligned with career construction theory [ 19 ], suggesting that positive self-concepts tend to expand undergraduates’ aspirations and develop their thinking about future career possibilities [ 16 ]. It, thus, significantly allows them to redefine their future self and proactively promote their employability [ 38 ]. At the same time, these results are consistent with self-determination theory [ 22 ], which posits that positive intrinsic motivation (e.g., future work self) is an effective predictor of positive job and career outcomes (e.g., employability) [ 17 ].

Meanwhile, by integrating insights from career construction theory and self-determination theory, we propose and find a mediated relationship between the future work self, career exploration, and employability. That is, we contribute to unfolding the black box of behavioral processes by demonstrating that graduates’ future work self could trigger career explorative behaviors toward enhancing employability. Consistent with career construction theory and self-determination theory, which posit that career exploration is a key mediator in explaining the relationship between students’ career motivation and positive career outcomes [ 23 , 24 , 25 ], our findings indicate that individual self-factors with proactive motivations generate internal goals that boost career development behaviors [ 11 , 16 ], which are conducive to positive career outcomes, such as employability [ 18 , 46 ].

Our results also demonstrate that graduates’ job market knowledge positively moderates the relationship between the future work self and employability via career exploration. Specifically, an undergraduate who has a higher level of both future work self and job market knowledge is more likely to engage in career explorative behaviors, which in turn increases his or her employability in the future job market. These findings extend previous studies on treating career-related knowledge and skills as personally relevant resources by demonstrating that obtaining job market knowledge can strengthen individuals’ career behaviors and outcomes (e.g., judging the employment situation, making career decisions, and promoting career success) [ 61 ]. Existing research drawing on career construction theory has suggested that some environmental factors can be used to actively process career-related information and advance individuals’ career development [ 20 ]; that is, well-examined boundary conditions are context-oriented [ 87 ]. In the current study, we go one step further by showing that this motivational process (i.e., the future work self boosts employability by increasing career exploration) is further strengthened in the presence of boundary conditions such as individuals’ knowledge about their future jobs from the perspective of personal-dependent characteristics. Thus, we enrich the current understanding that undergraduates should have a comprehensive thought on their jobs and better understand what competencies, knowledge and skills are necessary to successfully search for a job to sustain their future employability.

Practical implications

According to the findings in the current studies, there are some practical implications that can be provided. First, universities should emphasize on developing undergraduates’ future work self which is amenable to intervention and change [ 16 , 88 ]. Specifically, counseling interventions and strategies can be designed for use with undergraduates facing career transitions, such as offering courses regarding to planning careers in the future and job searching strategies.

On the one hand, according to the mediator of career exploration, we encourage students to develop such proactive behaviors as exploring their future career. For example, after making a list of some possibilities of future jobs, students should engage more in activities of proposed career options (e.g., getting involved in the workplace for valuable insight into a career workday). On the other hand, it is suggested that educators and counselors guide students to identify the discrepancies between their current states and future resource requirements, which in turn stimulate students to take steps to cope with these challenges towards a more promising future. Meanwhile, educators and counselors could provide more external opportunities (e.g., building professional network) to evaluate students’ career interests, enhance students’ career abilities for their future their career choices. In this vein, students could discover the jobs that are available to them after their graduation from universities.

Finally, some job lessons should be integrated into current course designs [ 21 ]. For example, teachers in universities should be trained to employ active learning methods in class towards supporting students to develop a realistic perception of the job market in their community as well as to their own interests and strength. In this vein, undergraduates can be well prepared to make realistic and personal decisions regarding their educational and professional future.

Limitations and future research

Some limitations can be noted in the current research. First, we collected data from one source (i.e., undergraduates). Although we have tested that CMV is not a potential problem in the study, we encouraged future research to invite others to rate undergraduates’ employability (e.g., teachers), which would increase the objectiveness of the results. A related limitation is about broadening the samples. Specifically, since an increasing number of Chinese undergraduates are pursuing a master’s degree, it is highly recommended to replicate our results among postgraduates. Thus, the generalization of the results reported in the current study awaits further empirical examination.

In addition, to further improve the overall robustness and rigor of the current research, it is highly recommended to rate students’ employability by other’s rating. We in this research, theoretically, aim to investigate students’ personal perceptions of their employability in the future job market; thus, we invited them to rate their perceived employability. Empirically, our examinations also precluded the possibility of CMB. However, others’ rating would provide more valid results on students’ employability. For example, following Roessler, Brolin, & Johnson (1990) [ 89 ], researchers in the future could invited employers to assess students’ employability in the real workplace, which may reveal the extent to which students are employable.

The final limitation regards to the research design. Specifically, although we conducted two independent studies in the current research by employing the experimental design (i.e., Study 1) and the time-lagged design (i.e., Study 2), it limits our ability to determine the direction of causality among the variables to the most extent. For instance, the findings may be influenced by opposite or bidirectional relationships due to the potential for undergraduates who have explored their career to enhance their development of future work self. This is because individuals are able to thoroughly examine their internal attributes, which facilitates the formation of a clear self-image in relation to work [ 39 , 90 ]. As research indicated the reciprocal relationship between future work self and career exploration [ 48 ], whether Chinese students’ future work self and their perceived employability are reciprocally related over time. Thus, we suggest that scholars in the future should conduct a more rigorous research design (e.g., the time-lagged research design) to further validate our research findings in terms of reciprocal relationship.

Availability of data and materials

The data resulting from this study is stored and protected according to the Data Management rules of the School of Business and Economics of the Vrije Universiteit Amsterdam, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the School of Business and Economics of the Vrije Universiteit Amsterdam.

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Yaju wrote the original manuscript and provided substantial contributions to the conceptualization of the paper. Lingyan performed the statistical analysis and critically revised important elements and revised this manuscript. Xiaopei revised the manuscript. Wenjing conceived the study and wrote the final version of the paper. Lin gave careful thought and detailed responses to the reviewers’ comments. Finally, all authors read and approved the final version of the manuscript and are responsible for the content of the work.

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High future work self condition, Low future work self condition

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Ma, Y., Hou, L., Cai, W. et al. Linking undergraduates’ future work self and employability: a moderated mediation model. BMC Psychol 12 , 160 (2024). https://doi.org/10.1186/s40359-024-01530-1

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DOI : https://doi.org/10.1186/s40359-024-01530-1

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BMC Psychology

ISSN: 2050-7283

experimental research psychology paper

The Bronfenbrenner Center for Translational Research

Researchers Develop a Test for Extreme Fatigue

A blood test for sleep deprivation could help prevent accidents..

Posted March 21, 2024 | Reviewed by Gary Drevitch

  • Why Is Sleep Important?
  • Find a sleep therapist near me
  • Sleep deprivation has serious health consequences.
  • A research team is working on a blood test to detect sleep deprivation.
  • The idea is to use the test similarly to the ways blood-alcohol testing is administered during traffic stops.

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Over centuries, the human body has developed a natural rhythm that uses biological and environmental factors to determine when to sleep and eat. But continued advances in technology—starting with the light bulb, all the way to on-demand entertainment—allow people to ignore these natural rhythms. Circumstances such as caring for an infant, working a night shift, or staying out late at a party can also disrupt regular sleep patterns. Researchers refer to these disruptions as “social jet lag .”

The evidence clearly demonstrates that deviating from a regular sleep pattern is bad for our health, leading to problems such as poor sleep quality, reduced cognitive performance, unhealthy eating patterns, depression, and anxiety. Data also finds that “social jet lag” can lead to heart disease . And there is clear evidence that sleep deprivation is an acute danger, as well: About 20 percent of traffic accidents worldwide are linked to sleep deprivation. Studies have found that after 24 hours awake, cognitive and motor performance is reduced to levels similar to having twice the legal limit for blood alcohol .

A team of Australian sleep researchers is working to address this problem. They have recently developed a blood test that can accurately detect severe fatigue due to sleep deprivation.

The idea is to use the test similarly to how blood-alcohol testing is used during traffic stops and after car accidents. Being able to identify when someone is sleep-deprived opens the door for lawmakers to create rules and laws which could ultimately discourage people from driving when overly fatigued.

For their recent paper , the Australian researchers tracked the sleeping patterns of 40 young, healthy adults. After maintaining a normal sleep schedule for two weeks, the participants spent several nights in a sleep lab. The experimental group were kept awake for 40 hours straight, with snacks and activity monitoring every hour. The control group maintained a schedule of sleeping for 8 hours and waking for 16 hours.

Researchers collected blood samples at regular intervals throughout the study for all participants, immediately extracted the plasma from these samples, and used machine learning to analyze the data collected from plasma samples to help identify a biomarker. For participants who were sleep deprived in the laboratory, researchers took blood samples from when they were well-rested and then when they were sleep-deprived.

They were able to find a valuable biomarker for fatigue. That biomarker predicted sleep deprivation with a 99.2% accuracy when comparing plasma samples from when participants were well-rested and then when they were sleep-deprived. Without being able to compare a test sample from when someone is well rested — which would be the circumstances during a traffic stop — the biomarker was able to predict sleep deprivation with 89.1% accuracy.

In addition to discovering a screening tool for fatigue, this research provides new information about the biomechanics of sleep deprivation – information that can help researchers better understand what’s happening in the body during fatigue.

The researchers point out a major limitation to their study: There were only a small number of participants, and all were young, healthy adults. More research is needed to explore whether the biomarker is effective in more diverse populations and in less-controlled environments.

The take-home message: Sleep deprivation has serious health consequences; among them, it can affect cognitive and motor skills that can lead to accidents. Researchers are working on developing a blood test to identify sleep deprivation, a step that could be a deterrent for risky behaviors while fatigued.

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