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Critical Thinking, Problem Solving, and Decision Making

93% of employers say that “a demonstrated capacity to think critically, communicate clearly, and solve complex problems is more important than a candidate’s undergraduate major.”
Even more (95 %) say they prioritize hiring college graduates with skills that will help them contribute to innovation in the workplace.
About 95% of those surveyed also say it is important that those they hire demonstrate ethical judgment and integrity; intercultural skills; and capacity for continued new learning.
More than 75% of those surveyed say they want more emphasis on five key areas including: critical thinking, complex problem solving, written and oral communication, and applied knowledge in real-world settings.
80% of employers agree that, regardless of their major, every college student should acquire broad knowledge in the liberal arts and sciences.

We invite you to further consider how we might reimagine and redesign schools and education through the development of skills involving Critical Thinking, Problem Solving, and Decision Making below.

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10 Intellectual Skills and How to Develop Them

Updated December 25, 2023
Published August 21, 2023

Are you looking to learn more about Intellectual skills? In this article, we discuss Intellectual skills in more detail and give you tips about how you can develop and improve them.

What are Intellectual Skills?

Intellectual skills, often called cognitive skills or thinking skills, are a set of mental abilities and capacities that enable individuals to process, analyze, understand, and manipulate information effectively. These skills are crucial for learning, problem-solving, decision-making, and adapting to new situations.

Intellectual skills are significant in academic achievement, professional success, and personal development. They can be broadly categorized into several key areas:

Critical Thinking
Problem-Solving

Analytical Skills

Logical reasoning, information processing, decision-making, attention to detail, metacognition, top 10 intellectual skills.

Below, we discuss the top 10 Intellectual skills. Each skill is discussed in more detail, and we will also give you tips on improving them.

Critical thinking is a crucial intellectual skill in a professional setting that involves analyzing, evaluating, and synthesizing information and ideas to make informed decisions and solve complex problems. This skill encompasses a range of abilities, including logical reasoning, evidence-based decision-making, and the capacity to identify and challenge assumptions and biases.

In a professional context, critical thinking enables individuals to approach challenges and opportunities with a systematic and analytical mindset, ultimately leading to more effective problem-solving and decision-making.

How to Improve Critical Thinking

One way to improve critical thinking professionally is through active engagement in strategic planning and decision-making processes.

For example, a marketing manager tasked with developing a new advertising campaign can enhance their critical thinking skills by thoroughly researching market trends, analyzing consumer behavior data, and critically evaluating the effectiveness of different advertising strategies. By scrutinizing assumptions, gathering data, and considering multiple perspectives, the manager can make more informed decisions and create a more successful campaign.

Furthermore, collaboration with colleagues from diverse backgrounds and expertise can also foster critical thinking. In a project management context, working in cross-functional teams often requires individuals to think critically about how different pieces of a project fit together and how to mitigate potential risks.

By engaging in discussions, challenging each other’s ideas, and collectively examining potential pitfalls, team members can collectively enhance their critical thinking skills and arrive at more robust project plans. This collaborative approach not only improves individual critical thinking abilities but also contributes to the overall success of the project and the organization.

Related : Critical Thinking Interview Questions & Answers

Problem-solving is a fundamental intellectual skill in the professional realm, characterized by the capacity to identify, analyze, and resolve complex issues and challenges. This skill involves a combination of critical thinking, creativity, and practical decision-making.

Effective problem solvers not only address immediate problems but also seek to uncover the root causes, preventing future occurrences. In a professional setting, problem-solving skills are invaluable, as they empower individuals and teams to navigate obstacles and capitalize on opportunities, ultimately contributing to organizational success.

How to Improve Problem-Solving

To enhance problem-solving skills in a professional context, individuals can start by developing a systematic approach to problem analysis. For instance, in IT support, technicians can use a structured problem-solving methodology such as the “5 Whys” technique when faced with a recurring technical issue.

By repeatedly asking “why” to trace the problem’s origin, they can uncover underlying causes and develop more effective solutions rather than merely addressing the surface symptoms.

Collaboration is another key to improving problem-solving skills. In project management, teams often encounter unforeseen challenges that demand creative solutions. By encouraging open communication and diverse perspectives within the team, project managers can harness collective intelligence to brainstorm innovative solutions.

This collaborative problem-solving approach can lead to more robust strategies and foster a culture of continuous improvement within the organization, as team members learn from one another’s problem-solving processes and successes.

Creativity is a vital intellectual skill in the professional arena, encompassing the ability to generate novel and valuable ideas, concepts, and solutions. It involves thinking outside the box, connecting seemingly unrelated concepts, and approaching challenges with a fresh perspective.

In a professional context, creativity goes beyond artistic endeavors and plays a pivotal role in problem-solving, innovation, and the development of new strategies. Professionals who cultivate and apply creative thinking can adapt to rapidly changing environments and envision innovative approaches that drive growth and competitiveness.

How to Improve Creativity

Enhancing creativity in a professional setting can be achieved through various strategies. One approach is to encourage a diverse and inclusive work environment that values different viewpoints and experiences.

In a marketing firm, for instance, a diverse team with individuals from various cultural backgrounds and fields of expertise can bring unique perspectives to campaign ideation. By engaging in brainstorming sessions where everyone is encouraged to contribute freely, a broader range of creative ideas can emerge, leading to more imaginative and impactful marketing strategies.

Additionally, allocating dedicated time for creative exploration can nurture innovation. For example, Google’s “20% time” policy allows employees to spend a portion of their work hours on self-directed projects. This policy has led to the development of successful products like Gmail and Google Maps.

In a technology company, giving engineers the freedom to work on passion projects boosts morale and fosters a culture of innovation where unconventional ideas are given the space to flourish. Organizations can tap into a wealth of untapped potential and drive continuous innovation by creating opportunities for employees to pursue their creative interests.

Related : Creative Thinking Interview Questions + Answers

Analytical skills are a critical intellectual ability in a professional setting, encompassing the capacity to systematically examine and evaluate information, data, and complex problems. Professionals with strong analytical skills can break down intricate issues into manageable components, identify patterns, draw meaningful conclusions, and make well-informed decisions.

These skills often involve numerical and statistical analysis and a keen attention to detail. Analytical skills are indispensable for problem-solving, strategic planning, and data-driven decision-making in various industries and job roles.

How to Improve Analytical Skills

To enhance analytical skills in a professional context, individuals can start by honing their data analysis proficiency. For instance, financial analysts can improve their analytical skills in the financial sector by mastering tools like Microsoft Excel, which facilitates the manipulation and interpretation of large datasets. By utilizing Excel’s functions and pivot tables to analyze financial statements and market trends, analysts can provide more accurate insights to guide investment decisions.

Furthermore, cultivating critical thinking abilities is integral to improving analytical skills. In a healthcare setting, a medical researcher can enhance their analytical skills by critically assessing research studies and clinical trials.

By scrutinizing the methodologies, data collection processes, and statistical analyses used in published studies, the researcher can better evaluate the validity of findings and make informed decisions about the applicability of research outcomes to clinical practice. This practice not only sharpens analytical skills but also contributes to the advancement of evidence-based healthcare practices.

Logical reasoning is a fundamental intellectual skill in a professional setting, involving the ability to think coherently, draw valid conclusions, and make sound judgments based on structured and organized thinking. Professionals with strong logical reasoning skills can effectively analyze complex situations, identify cause-and-effect relationships, and develop well-structured arguments.

This skill is particularly valuable in fields that require critical decision-making, problem-solving, and effective communication. Logical reasoning provides a foundation for strategic planning and ensures that actions are aligned with well-founded principles.

How to Improve Logical Reasoning

Improving logical reasoning skills in a professional context can be achieved through practice in different scenarios. In the legal industry, for example, lawyers continually apply logical reasoning when building cases. They assess evidence, craft arguments, and anticipate counterarguments, all based on a logical framework.

By participating in moot court exercises or engaging in mock trial simulations, aspiring lawyers can refine their logical reasoning skills, learning to build persuasive cases and make compelling legal arguments.

Additionally, engaging in puzzles, brainteasers, and structured problem-solving exercises can sharpen logical reasoning abilities. In the field of software development, programmers regularly apply logical reasoning to code debugging and optimization.

By participating in coding competitions or working on algorithmic challenges, developers can enhance their ability to break down complex problems into logical steps and devise efficient solutions. This improves their coding skills and boosts their overall analytical thinking and problem-solving capabilities.

Related : 10 Deductive Reasoning Skills and How to Develop Them

Information processing is a crucial intellectual skill in a professional setting, encompassing the ability to gather, organize, analyze, and interpret large volumes of data and information efficiently and effectively. In the modern era of data abundance, professionals who excel in information processing can make informed decisions, identify trends, and extract valuable insights that drive business strategies.

This skill involves proficiency in data analysis tools, research methodologies, and the capacity to synthesize complex information into coherent narratives.

How to Improve Information Processing

Improving information processing skills in a professional context can be achieved through various means. For instance, in marketing research, professionals can enhance their skills by mastering data visualization tools. By using platforms like Tableau or Power BI, marketers can transform raw data into visually engaging dashboards and reports, making it easier to communicate insights and trends to stakeholders. This not only streamlines the process of information dissemination but also demonstrates a higher level of analytical expertise.

Furthermore, adopting efficient information management practices is essential. In project management, for instance, professionals can benefit from using project management software that centralizes information, communication, and task tracking. Project managers can use tools like Trello or Asana to ensure that relevant information is readily accessible, deadlines are met, and team members stay aligned on goals.

Effective information processing in this context involves structuring information flow, enabling real-time updates, and enhancing collaboration, ultimately leading to more streamlined project execution and successful outcomes.

Decision-making is a critical intellectual skill in a professional setting that involves selecting the best course of action among various options to achieve desired outcomes. Effective decision-makers possess the ability to assess available information, weigh pros and cons, anticipate potential consequences, and align choices with organizational goals.

This skill requires a blend of analytical thinking, critical judgment, and the capacity to manage uncertainty. In a professional context, sound decision-making influences strategies, shapes project trajectories, and ultimately contributes to the success of an organization.

How to Improve Decision-Making

Improving decision-making skills in a professional context can be approached through several strategies. One key approach is fostering a culture of data-driven decision-making.

In a retail context, for instance, store managers can enhance their decision-making abilities by analyzing sales data to identify peak shopping times and popular products. Armed with these insights, they can make informed staffing decisions, ensuring that the right number of employees is present during high-demand periods. By integrating data analysis into decision-making processes, managers can enhance their ability to make choices that optimize resource allocation and improve customer satisfaction.

Additionally, practicing scenario analysis can aid in refining decision-making skills. In the field of finance, investment professionals often employ scenario analysis to assess the potential impact of different market conditions on investment portfolios. By modeling various scenarios, such as economic downturns or market upswings, investors can evaluate how their decisions might play out under different circumstances.

This practice hones decision-making skills and encourages a proactive approach to risk management, where professionals anticipate challenges and plan accordingly, leading to more resilient and successful outcomes.

As an intellectual skill in a professional setting, memory involves retaining and recalling information, experiences, and knowledge relevant to one’s work. Professionals with strong memory skills can efficiently store and retrieve critical information, which supports in making informed decisions, solving problems, and effectively interacting with colleagues and clients. This skill encompasses both short-term memory for immediate tasks and long-term memory for retaining valuable insights and learning experiences over time.

How to Improve Memory

Improving memory skills in a professional context can be achieved through various techniques. One effective method is active note-taking during meetings and presentations. In the field of education, teachers and educators can enhance their memory skills by jotting down key points and summaries while attending workshops or seminars.

This practice helps in processing and retaining the information being shared and provides a reference point for later recall. Furthermore, organizing these notes in a structured manner, such as using digital note-taking tools or physical notebooks, enhances the accessibility of information when it’s needed in the future.

Another approach to improving memory is the use of mnemonic devices. In the medical field, for example, healthcare professionals can employ memory aids to remember complex medical terminology or drug interactions.

By creating memorable acronyms or associations, doctors and nurses can recall essential information more easily, leading to safer patient care and more effective communication within healthcare teams. This method demonstrates how applying creative memory-enhancing techniques can streamline processes and mitigate the risk of errors in critical professional settings.

Attention to detail is a crucial intellectual skill in a professional setting that involves thoroughly and meticulously observing and reviewing information, tasks, and projects. Professionals with strong attention to detail can identify inconsistencies, errors, and nuances that others might overlook.

This skill ensures accuracy, quality, and precision in work output, which is particularly vital in industries such as healthcare, finance, and design, where even minor mistakes can have significant consequences.

How to Improve Attention to Detail

Improving attention to detail skills can be achieved through specific strategies. One approach is to develop a systematic and organized work process. In the field of graphic design, for instance, designers can enhance their attention to detail by adopting a step-by-step approach to proofreading and editing.

By breaking down their work into distinct phases, such as layout design, typography, and image placement, designers can thoroughly review each element separately, ensuring that no mistakes or inconsistencies go unnoticed. This methodical approach improves the final product’s quality and demonstrates a commitment to delivering error-free work.

Utilizing checklists and templates is another effective method to enhance attention to detail. Professionals can create project checklists in project management that outline key tasks, milestones, and quality control checkpoints. By systematically reviewing each item on the checklist before moving to the project’s next phase, project managers can ensure that all aspects are considered and addressed.

This practice is particularly valuable in complex projects where overlooking details can lead to delays or suboptimal outcomes. Templates also provide a standardized framework that minimizes the risk of oversight, ensuring consistent attention to detail across various projects.

Metacognition is an essential intellectual skill in a professional setting that involves the ability to think about one’s own thinking processes, monitor one’s cognitive activities, and make informed decisions about how to approach tasks and challenges.

It encompasses self-awareness, self-regulation, and the capacity to adapt learning and problem-solving strategies based on reflection and assessment. In a professional context, metacognition empowers individuals to optimize their cognitive processes, continuously improve their skills, and make more effective decisions.

How to Improve Metacognition

Improving metacognitive skills can be approached through several strategies. One method is setting clear goals and objectives for tasks and projects. In the realm of project management, professionals can enhance their metacognitive abilities by establishing specific objectives for each phase of a project and regularly reviewing progress against these goals. By doing so, project managers can assess whether their current strategies are effective or need adjustments, enabling them to adapt and refine their approaches for better outcomes.

Engaging in regular self-assessment and reflection is another way to enhance metacognition. In the field of sales, professionals can improve their metacognitive skills by analyzing their sales strategies and outcomes.

By keeping detailed records of interactions with clients, tracking conversion rates, and reflecting on successful versus unsuccessful sales approaches, salespeople can identify patterns and gain insights into their own strengths and areas for improvement. This self-awareness empowers them to adjust their tactics and adopt more effective selling techniques, leading to increased success in their role.

Intellectual Skills Conclusion

In conclusion, nurturing and enhancing intellectual skills is pivotal for anyone aspiring to excel in their professional journey. The modern landscape demands a versatile set of cognitive abilities that extend beyond mere knowledge acquisition. By actively developing critical thinking, creativity, problem-solving, and communication skills, individuals can position themselves as valuable assets in today’s dynamic workplace.

The significance of these skills lies in their power to transcend specific roles or industries. They serve as the foundation upon which innovation thrives, enabling professionals to navigate complex challenges and uncover novel solutions. As industries evolve and paradigms shift, the capacity to adapt and innovate becomes increasingly vital. Intellectual skills empower individuals to anticipate change, embrace ambiguity, and approach problems strategically and informally.

The tips provided in this article offer a pragmatic roadmap for fostering intellectual growth. You can actively cultivate their cognitive abilities by incorporating continuous learning, deliberate practice, interdisciplinary exploration, and open-minded collaboration.

Furthermore, seeking out diverse perspectives, engaging in reflective practices, and seeking feedback will further refine these skills, enabling individuals to refine their strengths and address their weaknesses.

In a professional setting, applying these tips translates to a marked improvement in your performance and impact. The ability to analyze situations critically and propose innovative solutions enhances decision-making and strategic planning.

Effective communication and collaboration foster productive teamwork and exchange of ideas, resulting in a more vibrant and creative workplace environment. Moreover, a commitment to ongoing learning and growth ensures that professionals remain adaptable and relevant in a rapidly changing landscape.

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The Psychology of Decision-Making Strategies

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

Carly Snyder, MD is a reproductive and perinatal psychiatrist who combines traditional psychiatry with integrative medicine-based treatments.

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You have to make decisions both large and small throughout every single day of your life. What do you want to have for breakfast? What time should you meet a friend for dinner? What college should you go to? How many children do you want to have?

When faced with some decisions, you might be tempted to just flip a coin and let chance determine your fate. In most cases, we follow a certain strategy or series of strategies in order to arrive at a decision.

For many of the relatively minor decisions that we make each and every day, flipping a coin wouldn't be such a terrible approach. For some of the complex and important decisions, we are more likely to invest a lot of time, research, effort, and mental energy into coming to the right conclusion.

So how exactly does this process work? The following are some of the major decision-making strategies that you might use.

The Single-Feature Model

This approach involves hinging your decision solely on a single feature. For example, imagine that you are buying soap. Faced with a wide variety of options at your local superstore, you decide to base your decision on price and buy the cheapest type of soap available. In this case, you ignored other variables (such as scent, brand, reputation, and effectiveness) and focused on just a single feature.

The single-feature approach can be effective in situations where the decision is relatively simple and you are pressed for time. However, it is generally not the best strategy when dealing with more complex decisions.

The Additive Feature Model

This method involves taking into account all the important features of the possible choices and then systematically evaluating each option. This approach tends to be a better method when making more complex decisions.

For example, imagine that you are interested in buying a new camera. You create a list of important features that you want the camera to have, then you rate each possible option on a scale of -5 to +5.

Cameras that have important advantages might get a +5 rating for that factor, while those that have major drawbacks might get a -5 rating for that factor. Once you have looked at each option, you can then tally up the results to determine which option has the highest rating.

The additive feature model can be a great way to determine the best option for a variety of choices. As you can imagine, however, it can be quite time-consuming and is probably not the best decision-making strategy to use if you are pressed for time.

The Elimination by Aspects Model

The elimination by aspects model was first proposed by psychologist Amos Tversky in 1972. In this approach, you evaluate each option one characteristic at a time beginning with whatever feature you believe is the most important. When an item fails to meet the criteria you have established, you cross the item off your list of options. Your list of possible choices gets smaller and smaller as you cross items off the list until you eventually arrive at just one alternative.

Decision Making

The previous three processes are often used in cases where decisions are pretty straightforward, but what happens when there is a certain amount of risk, ambiguity, or uncertainty involved? For example, imagine that you are running late for your psychology class.

Should you drive above the speed limit in order to get there on time, but risk getting a speeding ticket? Or should you drive the speed limit, risk being late, and possibly get docked points for missing a scheduled pop quiz? In this case, you have to weigh the possibility that you might be late for your appointment against the probability that you will get a speeding ticket.

When making a decision in such a situation, people tend to employ two different decision-making strategies: the availability heuristic and the representativeness heuristic. Remember, a heuristic is a rule-of-thumb mental short-cut that allows people to make decisions and judgments quickly.

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The Availability Heuristic

When we are trying to determine how likely something is, we often base such estimates on how easily we can remember similar events happening in the past. For example, if you are trying to determine if you should drive over the speed limit and risk getting a ticket, you might think of how many times you have seen people getting pulled over by a police officer on a particular stretch of highway.

If you cannot immediately think of any examples, you might decide to go ahead and take a chance, since the availability heuristic has led to you judge that few people get pulled over for speeding on your particular route. If you can think of numerous examples of people getting pulled over, you might decide to just play it safe and drive the suggested speed limit.

The Representativeness Heuristic

This mental shortcut involves comparing our current situation to our prototype of a particular event or behavior. For example, when trying to determine whether you should speed to get to your class on time, you might compare yourself to your image a person who is most likely to get a speeding ticket.

If your prototype is that of a careless teen that drives a hot-rod car and you are a young businesswoman who drives a sedan, you might estimate that the probability of getting a speeding ticket is quite low.

Keep in Mind

The decision-making process can be both simple (such as randomly picking out of our available options) or complex (such as systematically rating different aspects of the existing choices). The strategy we use depends on various factors, including how much time we have to make the decision, the overall complexity of the decision, and the amount of ambiguity that is involved.

Hockenbury, D. H. & Hockenbury, S. E. (2006). Psychology. New York: Worth Publishers.
Tversky, A., & Kahneman, D. (1982). Judgment under uncertainty: Heuristics and biases. In Daniel Kahneman, Paul Slovic, & Amos Tversky (Eds.). Judgment under uncertainty: Heuristics and biases. New York: Cambridge University Press.
Tversky, A. (1972). Elimination by aspects: A theory of choice. Psychological Review, 80, 281-299.

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

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3.4: Problem Solving and Decision Making

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Learning Objectives

Learn to understand the problem.
Learn to combine creative thinking and critical thinking to solve problems.
Practice problem solving in a group.

Much of your college and professional life will be spent solving problems; some will be complex, such as deciding on a career, and require time and effort to come up with a solution. Others will be small, such as deciding what to eat for lunch, and will allow you to make a quick decision based entirely on your own experience. But, in either case, when coming up with the solution and deciding what to do, follow the same basic steps.

Define the problem. Use your analytical skills. What is the real issue? Why is it a problem? What are the root causes? What kinds of outcomes or actions do you expect to generate to solve the problem? What are some of the key characteristics that will make a good choice: Timing? Resources? Availability of tools and materials? For more complex problems, it helps to actually write out the problem and the answers to these questions. Can you clarify your understanding of the problem by using metaphors to illustrate the issue?
Narrow the problem. Many problems are made up of a series of smaller problems, each requiring its own solution. Can you break the problem into different facets? What aspects of the current issue are “noise” that should not be considered in the problem solution? (Use critical thinking to separate facts from opinion in this step.)
Generate possible solutions. List all your options. Use your creative thinking skills in this phase. Did you come up with the second “right” answer, and the third or the fourth? Can any of these answers be combined into a stronger solution? What past or existing solutions can be adapted or combined to solve this problem?

Group Think: Effective Brainstorming

Brainstorming is a process of generating ideas for solutions in a group. This method is very effective because ideas from one person will trigger additional ideas from another. The following guidelines make for an effective brainstorming session:

Decide who should moderate the session. That person may participate, but his main role is to keep the discussion flowing.
Define the problem to be discussed and the time you will allow to consider it.
Write all ideas down on a board or flip chart for all participants to see.
Encourage everyone to speak.
Do not allow criticism of ideas. All ideas are good during a brainstorm. Suspend disbelief until after the session. Remember a wildly impossible idea may trigger a creative and feasible solution to a problem.
Choose the best solution. Use your critical thinking skills to select the most likely choices. List the pros and cons for each of your selections. How do these lists compare with the requirements you identified when you defined the problem? If you still can’t decide between options, you may want to seek further input from your brainstorming team.

Decisions, Decisions

You will be called on to make many decisions in your life. Some will be personal, like what to major in, or whether or not to get married. Other times you will be making decisions on behalf of others at work or for a volunteer organization. Occasionally you will be asked for your opinion or experience for decisions others are making. To be effective in all of these circumstances, it is helpful to understand some principles about decision making.

First, define who is responsible for solving the problem or making the decision. In an organization, this may be someone above or below you on the organization chart but is usually the person who will be responsible for implementing the solution. Deciding on an academic major should be your decision, because you will have to follow the course of study. Deciding on the boundaries of a sales territory would most likely be the sales manager who supervises the territories, because he or she will be responsible for producing the results with the combined territories. Once you define who is responsible for making the decision, everyone else will fall into one of two roles: giving input, or in rare cases, approving the decision.

Understanding the role of input is very important for good decisions. Input is sought or given due to experience or expertise, but it is up to the decision maker to weigh the input and decide whether and how to use it. Input should be fact based, or if offering an opinion, it should be clearly stated as such. Finally, once input is given, the person giving the input must support the other’s decision, whether or not the input is actually used.

Consider a team working on a project for a science course. The team assigns you the responsibility of analyzing and presenting a large set of complex data. Others on the team will set up the experiment to demonstrate the hypothesis, prepare the class presentation, and write the paper summarizing the results. As you face the data, you go to the team to seek input about the level of detail on the data you should consider for your analysis. The person doing the experiment setup thinks you should be very detailed, because then it will be easy to compare experiment results with the data. However, the person preparing the class presentation wants only high-level data to be considered because that will make for a clearer presentation. If there is not a clear understanding of the decision-making process, each of you may think the decision is yours to make because it influences the output of your work; there will be conflict and frustration on the team. If the decision maker is clearly defined upfront, however, and the input is thoughtfully given and considered, a good decision can be made (perhaps a creative compromise?) and the team can get behind the decision and work together to complete the project.

Finally, there is the approval role in decisions. This is very common in business decisions but often occurs in college work as well (the professor needs to approve the theme of the team project, for example). Approval decisions are usually based on availability of resources, legality, history, or policy.

Key Takeaways

Effective problem solving involves critical and creative thinking.

The four steps to effective problem solving are the following:

Define the problem
Narrow the problem
Generate solutions
Choose the solution
Brainstorming is a good method for generating creative solutions.
Understanding the difference between the roles of deciding and providing input makes for better decisions.

Checkpoint Exercises

Gather a group of three or four friends and conduct three short brainstorming sessions (ten minutes each) to generate ideas for alternate uses for peanut butter, paper clips, and pen caps. Compare the results of the group with your own ideas. Be sure to follow the brainstorming guidelines. Did you generate more ideas in the group? Did the quality of the ideas improve? Were the group ideas more innovative? Which was more fun? Write your conclusions here.

__________________________________________________________________

Using the steps outlined earlier for problem solving, write a plan for the following problem: You are in your second year of studies in computer animation at Jefferson Community College. You and your wife both work, and you would like to start a family in the next year or two. You want to become a video game designer and can benefit from more advanced work in programming. Should you go on to complete a four-year degree?

Define the problem: What is the core issue? What are the related issues? Are there any requirements to a successful solution? Can you come up with a metaphor to describe the issue?

Narrow the problem: Can you break down the problem into smaller manageable pieces? What would they be?

Generate solutions: What are at least two “right” answers to each of the problem pieces?

Choose the right approach: What do you already know about each solution? What do you still need to know? How can you get the information you need? Make a list of pros and cons for each solution.

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The Oxford Handbook of Cognitive Psychology

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48 Problem Solving

Department of Psychological and Brain Sciences, University of California, Santa Barbara

Published: 03 June 2013
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Problem solving refers to cognitive processing directed at achieving a goal when the problem solver does not initially know a solution method. A problem exists when someone has a goal but does not know how to achieve it. Problems can be classified as routine or nonroutine, and as well defined or ill defined. The major cognitive processes in problem solving are representing, planning, executing, and monitoring. The major kinds of knowledge required for problem solving are facts, concepts, procedures, strategies, and beliefs. Classic theoretical approaches to the study of problem solving are associationism, Gestalt, and information processing. Current issues and suggested future issues include decision making, intelligence and creativity, teaching of thinking skills, expert problem solving, analogical reasoning, mathematical and scientific thinking, everyday thinking, and the cognitive neuroscience of problem solving. Common themes concern the domain specificity of problem solving and a focus on problem solving in authentic contexts.

The study of problem solving begins with defining problem solving, problem, and problem types. This introduction to problem solving is rounded out with an examination of cognitive processes in problem solving, the role of knowledge in problem solving, and historical approaches to the study of problem solving.

Definition of Problem Solving

Problem solving refers to cognitive processing directed at achieving a goal for which the problem solver does not initially know a solution method. This definition consists of four major elements (Mayer, 1992 ; Mayer & Wittrock, 2006 ):

Cognitive —Problem solving occurs within the problem solver’s cognitive system and can only be inferred indirectly from the problem solver’s behavior (including biological changes, introspections, and actions during problem solving). Process —Problem solving involves mental computations in which some operation is applied to a mental representation, sometimes resulting in the creation of a new mental representation. Directed —Problem solving is aimed at achieving a goal. Personal —Problem solving depends on the existing knowledge of the problem solver so that what is a problem for one problem solver may not be a problem for someone who already knows a solution method.

The definition is broad enough to include a wide array of cognitive activities such as deciding which apartment to rent, figuring out how to use a cell phone interface, playing a game of chess, making a medical diagnosis, finding the answer to an arithmetic word problem, or writing a chapter for a handbook. Problem solving is pervasive in human life and is crucial for human survival. Although this chapter focuses on problem solving in humans, problem solving also occurs in nonhuman animals and in intelligent machines.

How is problem solving related to other forms of high-level cognition processing, such as thinking and reasoning? Thinking refers to cognitive processing in individuals but includes both directed thinking (which corresponds to the definition of problem solving) and undirected thinking such as daydreaming (which does not correspond to the definition of problem solving). Thus, problem solving is a type of thinking (i.e., directed thinking).

Reasoning refers to problem solving within specific classes of problems, such as deductive reasoning or inductive reasoning. In deductive reasoning, the reasoner is given premises and must derive a conclusion by applying the rules of logic. For example, given that “A is greater than B” and “B is greater than C,” a reasoner can conclude that “A is greater than C.” In inductive reasoning, the reasoner is given (or has experienced) a collection of examples or instances and must infer a rule. For example, given that X, C, and V are in the “yes” group and x, c, and v are in the “no” group, the reasoning may conclude that B is in “yes” group because it is in uppercase format. Thus, reasoning is a type of problem solving.

Definition of Problem

A problem occurs when someone has a goal but does not know to achieve it. This definition is consistent with how the Gestalt psychologist Karl Duncker ( 1945 , p. 1) defined a problem in his classic monograph, On Problem Solving : “A problem arises when a living creature has a goal but does not know how this goal is to be reached.” However, today researchers recognize that the definition should be extended to include problem solving by intelligent machines. This definition can be clarified using an information processing approach by noting that a problem occurs when a situation is in the given state, the problem solver wants the situation to be in the goal state, and there is no obvious way to move from the given state to the goal state (Newell & Simon, 1972 ). Accordingly, the three main elements in describing a problem are the given state (i.e., the current state of the situation), the goal state (i.e., the desired state of the situation), and the set of allowable operators (i.e., the actions the problem solver is allowed to take). The definition of “problem” is broad enough to include the situation confronting a physician who wishes to make a diagnosis on the basis of preliminary tests and a patient examination, as well as a beginning physics student trying to solve a complex physics problem.

Types of Problems

It is customary in the problem-solving literature to make a distinction between routine and nonroutine problems. Routine problems are problems that are so familiar to the problem solver that the problem solver knows a solution method. For example, for most adults, “What is 365 divided by 12?” is a routine problem because they already know the procedure for long division. Nonroutine problems are so unfamiliar to the problem solver that the problem solver does not know a solution method. For example, figuring out the best way to set up a funding campaign for a nonprofit charity is a nonroutine problem for most volunteers. Technically, routine problems do not meet the definition of problem because the problem solver has a goal but knows how to achieve it. Much research on problem solving has focused on routine problems, although most interesting problems in life are nonroutine.

Another customary distinction is between well-defined and ill-defined problems. Well-defined problems have a clearly specified given state, goal state, and legal operators. Examples include arithmetic computation problems or games such as checkers or tic-tac-toe. Ill-defined problems have a poorly specified given state, goal state, or legal operators, or a combination of poorly defined features. Examples include solving the problem of global warming or finding a life partner. Although, ill-defined problems are more challenging, much research in problem solving has focused on well-defined problems.

Cognitive Processes in Problem Solving

The process of problem solving can be broken down into two main phases: problem representation , in which the problem solver builds a mental representation of the problem situation, and problem solution , in which the problem solver works to produce a solution. The major subprocess in problem representation is representing , which involves building a situation model —that is, a mental representation of the situation described in the problem. The major subprocesses in problem solution are planning , which involves devising a plan for how to solve the problem; executing , which involves carrying out the plan; and monitoring , which involves evaluating and adjusting one’s problem solving.

For example, given an arithmetic word problem such as “Alice has three marbles. Sarah has two more marbles than Alice. How many marbles does Sarah have?” the process of representing involves building a situation model in which Alice has a set of marbles, there is set of marbles for the difference between the two girls, and Sarah has a set of marbles that consists of Alice’s marbles and the difference set. In the planning process, the problem solver sets a goal of adding 3 and 2. In the executing process, the problem solver carries out the computation, yielding an answer of 5. In the monitoring process, the problem solver looks over what was done and concludes that 5 is a reasonable answer. In most complex problem-solving episodes, the four cognitive processes may not occur in linear order, but rather may interact with one another. Although some research focuses mainly on the execution process, problem solvers may tend to have more difficulty with the processes of representing, planning, and monitoring.

Knowledge for Problem Solving

An important theme in problem-solving research is that problem-solving proficiency on any task depends on the learner’s knowledge (Anderson et al., 2001 ; Mayer, 1992 ). Five kinds of knowledge are as follows:

Facts —factual knowledge about the characteristics of elements in the world, such as “Sacramento is the capital of California” Concepts —conceptual knowledge, including categories, schemas, or models, such as knowing the difference between plants and animals or knowing how a battery works Procedures —procedural knowledge of step-by-step processes, such as how to carry out long-division computations Strategies —strategic knowledge of general methods such as breaking a problem into parts or thinking of a related problem Beliefs —attitudinal knowledge about how one’s cognitive processing works such as thinking, “I’m good at this”

Although some research focuses mainly on the role of facts and procedures in problem solving, complex problem solving also depends on the problem solver’s concepts, strategies, and beliefs (Mayer, 1992 ).

Historical Approaches to Problem Solving

Psychological research on problem solving began in the early 1900s, as an outgrowth of mental philosophy (Humphrey, 1963 ; Mandler & Mandler, 1964 ). Throughout the 20th century four theoretical approaches developed: early conceptions, associationism, Gestalt psychology, and information processing.

Early Conceptions

The start of psychology as a science can be set at 1879—the year Wilhelm Wundt opened the first world’s psychology laboratory in Leipzig, Germany, and sought to train the world’s first cohort of experimental psychologists. Instead of relying solely on philosophical speculations about how the human mind works, Wundt sought to apply the methods of experimental science to issues addressed in mental philosophy. His theoretical approach became structuralism —the analysis of consciousness into its basic elements.

Wundt’s main contribution to the study of problem solving, however, was to call for its banishment. According to Wundt, complex cognitive processing was too complicated to be studied by experimental methods, so “nothing can be discovered in such experiments” (Wundt, 1911/1973 ). Despite his admonishments, however, a group of his former students began studying thinking mainly in Wurzburg, Germany. Using the method of introspection, subjects were asked to describe their thought process as they solved word association problems, such as finding the superordinate of “newspaper” (e.g., an answer is “publication”). Although the Wurzburg group—as they came to be called—did not produce a new theoretical approach, they found empirical evidence that challenged some of the key assumptions of mental philosophy. For example, Aristotle had proclaimed that all thinking involves mental imagery, but the Wurzburg group was able to find empirical evidence for imageless thought .

Associationism

The first major theoretical approach to take hold in the scientific study of problem solving was associationism —the idea that the cognitive representations in the mind consist of ideas and links between them and that cognitive processing in the mind involves following a chain of associations from one idea to the next (Mandler & Mandler, 1964 ; Mayer, 1992 ). For example, in a classic study, E. L. Thorndike ( 1911 ) placed a hungry cat in what he called a puzzle box—a wooden crate in which pulling a loop of string that hung from overhead would open a trap door to allow the cat to escape to a bowl of food outside the crate. Thorndike placed the cat in the puzzle box once a day for several weeks. On the first day, the cat engaged in many extraneous behaviors such as pouncing against the wall, pushing its paws through the slats, and meowing, but on successive days the number of extraneous behaviors tended to decrease. Overall, the time required to get out of the puzzle box decreased over the course of the experiment, indicating the cat was learning how to escape.

Thorndike’s explanation for how the cat learned to solve the puzzle box problem is based on an associationist view: The cat begins with a habit family hierarchy —a set of potential responses (e.g., pouncing, thrusting, meowing, etc.) all associated with the same stimulus (i.e., being hungry and confined) and ordered in terms of strength of association. When placed in the puzzle box, the cat executes its strongest response (e.g., perhaps pouncing against the wall), but when it fails, the strength of the association is weakened, and so on for each unsuccessful action. Eventually, the cat gets down to what was initially a weak response—waving its paw in the air—but when that response leads to accidentally pulling the string and getting out, it is strengthened. Over the course of many trials, the ineffective responses become weak and the successful response becomes strong. Thorndike refers to this process as the law of effect : Responses that lead to dissatisfaction become less associated with the situation and responses that lead to satisfaction become more associated with the situation. According to Thorndike’s associationist view, solving a problem is simply a matter of trial and error and accidental success. A major challenge to assocationist theory concerns the nature of transfer—that is, where does a problem solver find a creative solution that has never been performed before? Associationist conceptions of cognition can be seen in current research, including neural networks, connectionist models, and parallel distributed processing models (Rogers & McClelland, 2004 ).

Gestalt Psychology

The Gestalt approach to problem solving developed in the 1930s and 1940s as a counterbalance to the associationist approach. According to the Gestalt approach, cognitive representations consist of coherent structures (rather than individual associations) and the cognitive process of problem solving involves building a coherent structure (rather than strengthening and weakening of associations). For example, in a classic study, Kohler ( 1925 ) placed a hungry ape in a play yard that contained several empty shipping crates and a banana attached overhead but out of reach. Based on observing the ape in this situation, Kohler noted that the ape did not randomly try responses until one worked—as suggested by Thorndike’s associationist view. Instead, the ape stood under the banana, looked up at it, looked at the crates, and then in a flash of insight stacked the crates under the bananas as a ladder, and walked up the steps in order to reach the banana.

According to Kohler, the ape experienced a sudden visual reorganization in which the elements in the situation fit together in a way to solve the problem; that is, the crates could become a ladder that reduces the distance to the banana. Kohler referred to the underlying mechanism as insight —literally seeing into the structure of the situation. A major challenge of Gestalt theory is its lack of precision; for example, naming a process (i.e., insight) is not the same as explaining how it works. Gestalt conceptions can be seen in modern research on mental models and schemas (Gentner & Stevens, 1983 ).

Information Processing

The information processing approach to problem solving developed in the 1960s and 1970s and was based on the influence of the computer metaphor—the idea that humans are processors of information (Mayer, 2009 ). According to the information processing approach, problem solving involves a series of mental computations—each of which consists of applying a process to a mental representation (such as comparing two elements to determine whether they differ).

In their classic book, Human Problem Solving , Newell and Simon ( 1972 ) proposed that problem solving involved a problem space and search heuristics . A problem space is a mental representation of the initial state of the problem, the goal state of the problem, and all possible intervening states (based on applying allowable operators). Search heuristics are strategies for moving through the problem space from the given to the goal state. Newell and Simon focused on means-ends analysis , in which the problem solver continually sets goals and finds moves to accomplish goals.

Newell and Simon used computer simulation as a research method to test their conception of human problem solving. First, they asked human problem solvers to think aloud as they solved various problems such as logic problems, chess, and cryptarithmetic problems. Then, based on an information processing analysis, Newell and Simon created computer programs that solved these problems. In comparing the solution behavior of humans and computers, they found high similarity, suggesting that the computer programs were solving problems using the same thought processes as humans.

An important advantage of the information processing approach is that problem solving can be described with great clarity—as a computer program. An important limitation of the information processing approach is that it is most useful for describing problem solving for well-defined problems rather than ill-defined problems. The information processing conception of cognition lives on as a keystone of today’s cognitive science (Mayer, 2009 ).

Classic Issues in Problem Solving

Three classic issues in research on problem solving concern the nature of transfer (suggested by the associationist approach), the nature of insight (suggested by the Gestalt approach), and the role of problem-solving heuristics (suggested by the information processing approach).

Transfer refers to the effects of prior learning on new learning (or new problem solving). Positive transfer occurs when learning A helps someone learn B. Negative transfer occurs when learning A hinders someone from learning B. Neutral transfer occurs when learning A has no effect on learning B. Positive transfer is a central goal of education, but research shows that people often do not transfer what they learned to solving problems in new contexts (Mayer, 1992 ; Singley & Anderson, 1989 ).

Three conceptions of the mechanisms underlying transfer are specific transfer , general transfer , and specific transfer of general principles . Specific transfer refers to the idea that learning A will help someone learn B only if A and B have specific elements in common. For example, learning Spanish may help someone learn Latin because some of the vocabulary words are similar and the verb conjugation rules are similar. General transfer refers to the idea that learning A can help someone learn B even they have nothing specifically in common but A helps improve the learner’s mind in general. For example, learning Latin may help people learn “proper habits of mind” so they are better able to learn completely unrelated subjects as well. Specific transfer of general principles is the idea that learning A will help someone learn B if the same general principle or solution method is required for both even if the specific elements are different.

In a classic study, Thorndike and Woodworth ( 1901 ) found that students who learned Latin did not subsequently learn bookkeeping any better than students who had not learned Latin. They interpreted this finding as evidence for specific transfer—learning A did not transfer to learning B because A and B did not have specific elements in common. Modern research on problem-solving transfer continues to show that people often do not demonstrate general transfer (Mayer, 1992 ). However, it is possible to teach people a general strategy for solving a problem, so that when they see a new problem in a different context they are able to apply the strategy to the new problem (Judd, 1908 ; Mayer, 2008 )—so there is also research support for the idea of specific transfer of general principles.

Insight refers to a change in a problem solver’s mind from not knowing how to solve a problem to knowing how to solve it (Mayer, 1995 ; Metcalfe & Wiebe, 1987 ). In short, where does the idea for a creative solution come from? A central goal of problem-solving research is to determine the mechanisms underlying insight.

The search for insight has led to five major (but not mutually exclusive) explanatory mechanisms—insight as completing a schema, insight as suddenly reorganizing visual information, insight as reformulation of a problem, insight as removing mental blocks, and insight as finding a problem analog (Mayer, 1995 ). Completing a schema is exemplified in a study by Selz (Fridja & de Groot, 1982 ), in which people were asked to think aloud as they solved word association problems such as “What is the superordinate for newspaper?” To solve the problem, people sometimes thought of a coordinate, such as “magazine,” and then searched for a superordinate category that subsumed both terms, such as “publication.” According to Selz, finding a solution involved building a schema that consisted of a superordinate and two subordinate categories.

Reorganizing visual information is reflected in Kohler’s ( 1925 ) study described in a previous section in which a hungry ape figured out how to stack boxes as a ladder to reach a banana hanging above. According to Kohler, the ape looked around the yard and found the solution in a flash of insight by mentally seeing how the parts could be rearranged to accomplish the goal.

Reformulating a problem is reflected in a classic study by Duncker ( 1945 ) in which people are asked to think aloud as they solve the tumor problem—how can you destroy a tumor in a patient without destroying surrounding healthy tissue by using rays that at sufficient intensity will destroy any tissue in their path? In analyzing the thinking-aloud protocols—that is, transcripts of what the problem solvers said—Duncker concluded that people reformulated the goal in various ways (e.g., avoid contact with healthy tissue, immunize healthy tissue, have ray be weak in healthy tissue) until they hit upon a productive formulation that led to the solution (i.e., concentrating many weak rays on the tumor).

Removing mental blocks is reflected in classic studies by Duncker ( 1945 ) in which solving a problem involved thinking of a novel use for an object, and by Luchins ( 1942 ) in which solving a problem involved not using a procedure that had worked well on previous problems. Finding a problem analog is reflected in classic research by Wertheimer ( 1959 ) in which learning to find the area of a parallelogram is supported by the insight that one could cut off the triangle on one side and place it on the other side to form a rectangle—so a parallelogram is really a rectangle in disguise. The search for insight along each of these five lines continues in current problem-solving research.

Heuristics are problem-solving strategies, that is, general approaches to how to solve problems. Newell and Simon ( 1972 ) suggested three general problem-solving heuristics for moving from a given state to a goal state: random trial and error , hill climbing , and means-ends analysis . Random trial and error involves randomly selecting a legal move and applying it to create a new problem state, and repeating that process until the goal state is reached. Random trial and error may work for simple problems but is not efficient for complex ones. Hill climbing involves selecting the legal move that moves the problem solver closer to the goal state. Hill climbing will not work for problems in which the problem solver must take a move that temporarily moves away from the goal as is required in many problems.

Means-ends analysis involves creating goals and seeking moves that can accomplish the goal. If a goal cannot be directly accomplished, a subgoal is created to remove one or more obstacles. Newell and Simon ( 1972 ) successfully used means-ends analysis as the search heuristic in a computer program aimed at general problem solving, that is, solving a diverse collection of problems. However, people may also use specific heuristics that are designed to work for specific problem-solving situations (Gigerenzer, Todd, & ABC Research Group, 1999 ; Kahneman & Tversky, 1984 ).

Current and Future Issues in Problem Solving

Eight current issues in problem solving involve decision making, intelligence and creativity, teaching of thinking skills, expert problem solving, analogical reasoning, mathematical and scientific problem solving, everyday thinking, and the cognitive neuroscience of problem solving.

Decision Making

Decision making refers to the cognitive processing involved in choosing between two or more alternatives (Baron, 2000 ; Markman & Medin, 2002 ). For example, a decision-making task may involve choosing between getting $240 for sure or having a 25% change of getting $1000. According to economic theories such as expected value theory, people should chose the second option, which is worth $250 (i.e., .25 x $1000) rather than the first option, which is worth $240 (1.00 x $240), but psychological research shows that most people prefer the first option (Kahneman & Tversky, 1984 ).

Research on decision making has generated three classes of theories (Markman & Medin, 2002 ): descriptive theories, such as prospect theory (Kahneman & Tversky), which are based on the ideas that people prefer to overweight the cost of a loss and tend to overestimate small probabilities; heuristic theories, which are based on the idea that people use a collection of short-cut strategies such as the availability heuristic (Gigerenzer et al., 1999 ; Kahneman & Tversky, 2000 ); and constructive theories, such as mental accounting (Kahneman & Tversky, 2000 ), in which people build a narrative to justify their choices to themselves. Future research is needed to examine decision making in more realistic settings.

Intelligence and Creativity

Although researchers do not have complete consensus on the definition of intelligence (Sternberg, 1990 ), it is reasonable to view intelligence as the ability to learn or adapt to new situations. Fluid intelligence refers to the potential to solve problems without any relevant knowledge, whereas crystallized intelligence refers to the potential to solve problems based on relevant prior knowledge (Sternberg & Gregorenko, 2003 ). As people gain more experience in a field, their problem-solving performance depends more on crystallized intelligence (i.e., domain knowledge) than on fluid intelligence (i.e., general ability) (Sternberg & Gregorenko, 2003 ). The ability to monitor and manage one’s cognitive processing during problem solving—which can be called metacognition —is an important aspect of intelligence (Sternberg, 1990 ). Research is needed to pinpoint the knowledge that is needed to support intelligent performance on problem-solving tasks.

Creativity refers to the ability to generate ideas that are original (i.e., other people do not think of the same idea) and functional (i.e., the idea works; Sternberg, 1999 ). Creativity is often measured using tests of divergent thinking —that is, generating as many solutions as possible for a problem (Guilford, 1967 ). For example, the uses test asks people to list as many uses as they can think of for a brick. Creativity is different from intelligence, and it is at the heart of creative problem solving—generating a novel solution to a problem that the problem solver has never seen before. An important research question concerns whether creative problem solving depends on specific knowledge or creativity ability in general.

Teaching of Thinking Skills

How can people learn to be better problem solvers? Mayer ( 2008 ) proposes four questions concerning teaching of thinking skills:

What to teach —Successful programs attempt to teach small component skills (such as how to generate and evaluate hypotheses) rather than improve the mind as a single monolithic skill (Covington, Crutchfield, Davies, & Olton, 1974 ). How to teach —Successful programs focus on modeling the process of problem solving rather than solely reinforcing the product of problem solving (Bloom & Broder, 1950 ). Where to teach —Successful programs teach problem-solving skills within the specific context they will be used rather than within a general course on how to solve problems (Nickerson, 1999 ). When to teach —Successful programs teaching higher order skills early rather than waiting until lower order skills are completely mastered (Tharp & Gallimore, 1988 ).

Overall, research on teaching of thinking skills points to the domain specificity of problem solving; that is, successful problem solving depends on the problem solver having domain knowledge that is relevant to the problem-solving task.

Expert Problem Solving

Research on expertise is concerned with differences between how experts and novices solve problems (Ericsson, Feltovich, & Hoffman, 2006 ). Expertise can be defined in terms of time (e.g., 10 years of concentrated experience in a field), performance (e.g., earning a perfect score on an assessment), or recognition (e.g., receiving a Nobel Prize or becoming Grand Master in chess). For example, in classic research conducted in the 1940s, de Groot ( 1965 ) found that chess experts did not have better general memory than chess novices, but they did have better domain-specific memory for the arrangement of chess pieces on the board. Chase and Simon ( 1973 ) replicated this result in a better controlled experiment. An explanation is that experts have developed schemas that allow them to chunk collections of pieces into a single configuration.

In another landmark study, Larkin et al. ( 1980 ) compared how experts (e.g., physics professors) and novices (e.g., first-year physics students) solved textbook physics problems about motion. Experts tended to work forward from the given information to the goal, whereas novices tended to work backward from the goal to the givens using a means-ends analysis strategy. Experts tended to store their knowledge in an integrated way, whereas novices tended to store their knowledge in isolated fragments. In another study, Chi, Feltovich, and Glaser ( 1981 ) found that experts tended to focus on the underlying physics concepts (such as conservation of energy), whereas novices tended to focus on the surface features of the problem (such as inclined planes or springs). Overall, research on expertise is useful in pinpointing what experts know that is different from what novices know. An important theme is that experts rely on domain-specific knowledge rather than solely general cognitive ability.

Analogical Reasoning

Analogical reasoning occurs when people solve one problem by using their knowledge about another problem (Holyoak, 2005 ). For example, suppose a problem solver learns how to solve a problem in one context using one solution method and then is given a problem in another context that requires the same solution method. In this case, the problem solver must recognize that the new problem has structural similarity to the old problem (i.e., it may be solved by the same method), even though they do not have surface similarity (i.e., the cover stories are different). Three steps in analogical reasoning are recognizing —seeing that a new problem is similar to a previously solved problem; abstracting —finding the general method used to solve the old problem; and mapping —using that general method to solve the new problem.

Research on analogical reasoning shows that people often do not recognize that a new problem can be solved by the same method as a previously solved problem (Holyoak, 2005 ). However, research also shows that successful analogical transfer to a new problem is more likely when the problem solver has experience with two old problems that have the same underlying structural features (i.e., they are solved by the same principle) but different surface features (i.e., they have different cover stories) (Holyoak, 2005 ). This finding is consistent with the idea of specific transfer of general principles as described in the section on “Transfer.”

Mathematical and Scientific Problem Solving

Research on mathematical problem solving suggests that five kinds of knowledge are needed to solve arithmetic word problems (Mayer, 2008 ):

Factual knowledge —knowledge about the characteristics of problem elements, such as knowing that there are 100 cents in a dollar Schematic knowledge —knowledge of problem types, such as being able to recognize time-rate-distance problems Strategic knowledge —knowledge of general methods, such as how to break a problem into parts Procedural knowledge —knowledge of processes, such as how to carry our arithmetic operations Attitudinal knowledge —beliefs about one’s mathematical problem-solving ability, such as thinking, “I am good at this”

People generally possess adequate procedural knowledge but may have difficulty in solving mathematics problems because they lack factual, schematic, strategic, or attitudinal knowledge (Mayer, 2008 ). Research is needed to pinpoint the role of domain knowledge in mathematical problem solving.

Research on scientific problem solving shows that people harbor misconceptions, such as believing that a force is needed to keep an object in motion (McCloskey, 1983 ). Learning to solve science problems involves conceptual change, in which the problem solver comes to recognize that previous conceptions are wrong (Mayer, 2008 ). Students can be taught to engage in scientific reasoning such as hypothesis testing through direct instruction in how to control for variables (Chen & Klahr, 1999 ). A central theme of research on scientific problem solving concerns the role of domain knowledge.

Everyday Thinking

Everyday thinking refers to problem solving in the context of one’s life outside of school. For example, children who are street vendors tend to use different procedures for solving arithmetic problems when they are working on the streets than when they are in school (Nunes, Schlieman, & Carraher, 1993 ). This line of research highlights the role of situated cognition —the idea that thinking always is shaped by the physical and social context in which it occurs (Robbins & Aydede, 2009 ). Research is needed to determine how people solve problems in authentic contexts.

Cognitive Neuroscience of Problem Solving

The cognitive neuroscience of problem solving is concerned with the brain activity that occurs during problem solving. For example, using fMRI brain imaging methodology, Goel ( 2005 ) found that people used the language areas of the brain to solve logical reasoning problems presented in sentences (e.g., “All dogs are pets…”) and used the spatial areas of the brain to solve logical reasoning problems presented in abstract letters (e.g., “All D are P…”). Cognitive neuroscience holds the potential to make unique contributions to the study of problem solving.

Problem solving has always been a topic at the fringe of cognitive psychology—too complicated to study intensively but too important to completely ignore. Problem solving—especially in realistic environments—is messy in comparison to studying elementary processes in cognition. The field remains fragmented in the sense that topics such as decision making, reasoning, intelligence, expertise, mathematical problem solving, everyday thinking, and the like are considered to be separate topics, each with its own separate literature. Yet some recurring themes are the role of domain-specific knowledge in problem solving and the advantages of studying problem solving in authentic contexts.

Future Directions

Some important issues for future research include the three classic issues examined in this chapter—the nature of problem-solving transfer (i.e., How are people able to use what they know about previous problem solving to help them in new problem solving?), the nature of insight (e.g., What is the mechanism by which a creative solution is constructed?), and heuristics (e.g., What are some teachable strategies for problem solving?). In addition, future research in problem solving should continue to pinpoint the role of domain-specific knowledge in problem solving, the nature of cognitive ability in problem solving, how to help people develop proficiency in solving problems, and how to provide aids for problem solving.

Anderson L. W. , Krathwohl D. R. , Airasian P. W. , Cruikshank K. A. , Mayer R. E. , Pintrich P. R. , Raths, J., & Wittrock M. C. ( 2001 ). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York : Longman.

Baron J. ( 2000 ). Thinking and deciding (3rd ed.). New York : Cambridge University Press.

Google Scholar

Google Preview

Bloom B. S. , & Broder B. J. ( 1950 ). Problem-solving processes of college students: An exploratory investigation. Chicago : University of Chicago Press.

Chase W. G. , & Simon H. A. ( 1973 ). Perception in chess. Cognitive Psychology, 4, 55–81.

Chen Z. , & Klahr D. ( 1999 ). All other things being equal: Acquisition and transfer of the control of variable strategy . Child Development, 70, 1098–1120.

Chi M. T. H. , Feltovich P. J. , & Glaser R. ( 1981 ). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.

Covington M. V. , Crutchfield R. S. , Davies L. B. , & Olton R. M. ( 1974 ). The productive thinking program. Columbus, OH : Merrill.

de Groot A. D. ( 1965 ). Thought and choice in chess. The Hague, The Netherlands : Mouton.

Duncker K. ( 1945 ). On problem solving. Psychological Monographs, 58 (3) (Whole No. 270).

Ericsson K. A. , Feltovich P. J. , & Hoffman R. R. (Eds.). ( 2006 ). The Cambridge handbook of expertise and expert performance. New York : Cambridge University Press.

Fridja N. H. , & de Groot A. D. ( 1982 ). Otto Selz: His contribution to psychology. The Hague, The Netherlands : Mouton.

Gentner D. , & Stevens A. L. (Eds.). ( 1983 ). Mental models. Hillsdale, NJ : Erlbaum.

Gigerenzer G. , Todd P. M. , & ABC Research Group (Eds.). ( 1999 ). Simple heuristics that make us smart. Oxford, England : Oxford University Press.

Goel V. ( 2005 ). Cognitive neuroscience of deductive reasoning. In K. J. Holyoak & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 475–492). New York : Cambridge University Press.

Guilford J. P. ( 1967 ). The nature of human intelligence. New York : McGraw-Hill.

Holyoak K. J. ( 2005 ). Analogy. In K. J. Holyoak & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 117–142). New York : Cambridge University Press.

Humphrey G. ( 1963 ). Thinking: An introduction to experimental psychology. New York : Wiley.

Judd C. H. ( 1908 ). The relation of special training and general intelligence. Educational Review, 36, 28–42.

Kahneman D. , & Tversky A. ( 1984 ). Choices, values, and frames. American Psychologist, 39, 341–350.

Kahneman D. , & Tversky A. (Eds.). ( 2000 ). Choices, values, and frames. New York : Cambridge University Press.

Kohler W. ( 1925 ). The mentality of apes. New York : Liveright.

Larkin J. H. , McDermott J. , Simon D. P. , & Simon H. A. ( 1980 ). Expert and novice performance in solving physics problems. Science, 208, 1335–1342.

Luchins A. ( 1942 ). Mechanization in problem solving. Psychological Monographs, 54 (6) (Whole No. 248).

Mandler J. M. , & Mandler G. ( 1964 ). Thinking from associationism to Gestalt. New York : Wiley.

Markman A. B. , & Medin D. L. ( 2002 ). Decision making. In D. Medin (Ed.), Stevens’ handbook of experimental psychology, Vol. 2. Memory and cognitive processes (2nd ed., pp. 413–466). New York : Wiley.

Mayer R. E. ( 1992 ). Thinking, problem solving, cognition (2nd ed). New York : Freeman.

Mayer R. E. ( 1995 ). The search for insight: Grappling with Gestalt psychology’s unanswered questions. In R. J. Sternberg & J. E. Davidson (Eds.), The nature of insight (pp. 3–32). Cambridge, MA : MIT Press.

Mayer R. E. ( 2008 ). Learning and instruction. Upper Saddle River, NJ : Merrill Prentice Hall.

Mayer R. E. ( 2009 ). Information processing. In T. L. Good (Ed.), 21st century education: A reference handbook (pp. 168–174). Thousand Oaks, CA : Sage.

Mayer R. E. , & Wittrock M. C. ( 2006 ). Problem solving. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 287–304). Mahwah, NJ : Erlbaum.

McCloskey M. ( 1983 ). Intuitive physics. Scientific American, 248 (4), 122–130.

Metcalfe J. , & Wiebe D. ( 1987 ). Intuition in insight and non-insight problem solving. Memory and Cognition, 15, 238–246.

Newell A. , & Simon H. A. ( 1972 ). Human problem solving. Englewood Cliffs, NJ : Prentice-Hall.

Nickerson R. S. ( 1999 ). Enhancing creativity. In R. J. Sternberg (Ed.), Handbook of creativity (pp. 392–430). New York : Cambridge University Press.

Nunes T. , Schliemann A. D. , & Carraher D. W , ( 1993 ). Street mathematics and school mathematics. Cambridge, England : Cambridge University Press.

Robbins P. , & Aydede M. (Eds.). ( 2009 ). The Cambridge handbook of situated cognition. New York : Cambridge University Press.

Rogers T. T. , & McClelland J. L. ( 2004 ). Semantic cognition: A parallel distributed processing approach. Cambridge, MA : MIT Press.

Singley M. K. , & Anderson J. R. ( 1989 ). The transfer of cognitive skill. Cambridge, MA : Harvard University Press.

Sternberg R. J. ( 1990 ). Metaphors of mind: Conceptions of the nature of intelligence. New York : Cambridge University Press.

Sternberg R. J. ( 1999 ). Handbook of creativity. New York : Cambridge University Press.

Sternberg R. J. , & Gregorenko E. L. (Eds.). ( 2003 ). The psychology of abilities, competencies, and expertise. New York : Cambridge University Press.

Tharp R. G. , & Gallimore R. ( 1988 ). Rousing minds to life: Teaching, learning, and schooling in social context. New York : Cambridge University Press.

Thorndike E. L. ( 1911 ). Animal intelligence. New York: Hafner.

Thorndike E. L. , & Woodworth R. S. ( 1901 ). The influence of improvement in one mental function upon the efficiency of other functions. Psychological Review, 8, 247–261.

Wertheimer M. ( 1959 ). Productive thinking. New York : Harper and Collins.

Wundt W. ( 1973 ). An introduction to experimental psychology. New York : Arno Press. (Original work published in 1911).

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Leading through Problem Solving and Decision Making

7 videos | 46m 51s
Includes Assessment
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Playable 1. Leading through Problem Solving and Decision Making 56s Successful leaders recognize that while they’re fully accountable for the results, it’s essential to work with their teams to get the input they need to solve problems and make decisions. In this video, you'll discover how to use a three-stage approach to engage your team in problem-solving and decision making. You'll also learn how to use your critical thinking and interpersonal skills to define problems, generate feasible solutions, and make the best decisions for your team and organization. FREE ACCESS
Playable 2. The Problem Solving and Decision Making Process 8m 40s In this video, you will learn how to deal with a problem effectively. You will also discover the common reactions people have when suddenly presented with a problem. FREE ACCESS
Locked 3. Defining the Problem 8m 31s In this video, you will learn how to define the problem. You will also discover how to develop a problem statement that addresses a single problem and describes it objectively in specific and measurable terms. FREE ACCESS
Locked 4. Generating Feasible Solutions 10m 1s In this video, find out how to recognize the best way to generate feasible solutions. FREE ACCESS
Locked 5. Choosing a Viable Solution and Making Decisions 11m 2s During this video you will learn about the key concepts for making decisions. You will also discover how to evaluate a proposed business decision. FREE ACCESS
Locked 6. Using Problem Solving and Decision Making Skills 6m 53s When you're in the middle of tackling a big problem, one of the things the leader has to do is de-conflict some of the political forces and priorities at work. In this video, you will learn how to syndicate risk up and out of the way. You will also discover how to reconcile those priorities so you can define the root of the problem and get to the essential truth. FREE ACCESS
Locked 7. Let's Review 48s In this video, you'll review the key concepts covered in this course, including how to lead teams using problem solving skills to make decisions. FREE ACCESS

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How to Enhance Your Decision-Making Skills as a Leader

Leader making decision with team watching

14 Mar 2024

As a leader, you make countless decisions—from whom to hire and which projects to prioritize to where to make budget cuts.

If you’re a new leader, acclimating to being a decision-maker can be challenging. Luckily, like other vital business skills, you can learn how to make better decisions through education and practice.

Here’s a primer on why decision-making skills are crucial to leadership and six ways to enhance yours.

Access your free e-book today.

Why Are Decision-Making Skills Important?

While decision-making is built into most leaders’ job descriptions, it’s a common pain point. According to a 2023 Oracle study , 85 percent of business leaders report suffering from “decision distress”—regretting, feeling guilty about, or questioning a decision they made in the past year.

When distressed by difficult decisions, it can be easy to succumb to common pitfalls , such as:

Defaulting to consensus
Not offering alternatives to your proposed solution
Mistaking opinions for facts
Losing sight of purpose
Truncating debate

By defaulting to the “easy answer” or avoiding working through a decision, you can end up with outcomes that are stagnant at best and disastrous at worst.

Yet, decision-making is a skill you can sharpen in your leadership toolkit. Here are six ways to do so.

6 Ways to Enhance Your Leadership Decision-Making Skills

1. involve your team.

One common pitfall of leadership is thinking you must make every decision yourself. While you may have the final judgment call, enlisting others to work through challenging decisions can be helpful.

Asking for peers’ input can open your mind to new perspectives. For instance, if you ask your direct reports to brainstorm ways to improve your production process’s efficiency, chances are that they’ll have some ideas you didn’t think of.

If a decision is more private—such as whether to promote one employee over another—consider consulting fellow organizational leaders to approach it from multiple angles.

Another reason to involve your team in the decision-making process is to achieve buy-in. Your decision will likely impact each member, whether it’s about a new or reprioritized strategic initiative. By helping decide how to solve the challenge, your employees are more likely to feel a sense of ownership and empowerment during the execution phase.

Related: How to Get Employee Buy-In to Execute Your Strategic Initiatives

2. Understand Your Responsibilities to Stakeholders

When facing a decision, remember your responsibilities to stakeholders. In the online course Leadership, Ethics, and Corporate Accountability —offered as a Credential of Leadership, Impact, and Management in Business (CLIMB) program elective or individually—Harvard Business School Professor Nien-hê Hsieh outlines your three types of responsibilities as a leader: legal, economic, and ethical .

Hsieh also identifies four stakeholder groups—customers, employees, investors, and society—that you must balance your obligations to when making decisions.

For example, you have the following responsibilities to customers and employees:

Well-being: What’s ultimately good for the person
Rights: Entitlement to receive certain treatment
Duties: A moral obligation to behave in a specific way
Best practices: Aspirational standards not required by law or cultural norms

“Many of the decisions you face will not have a single right answer,” Hsieh says in the course. “Sometimes, the most viable answer may come with negative effects. In such cases, the decision is not black and white. As a result, many call them ‘gray-area decisions.’”

As a starting point for tackling gray-area decisions, identify your stakeholders and your responsibilities to each.

Related: How to Choose Your CLIMB Electives

3. Consider Value-Based Strategy

If you make decisions that impact your organization’s strategy, consider how to create value. Often, the best decision provides the most value to the most stakeholders.

The online course Business Strategy —one of seven courses comprising CLIMB's New Leaders learning path—presents the value stick as a visual representation of a value-based strategy's components.

By toggling each, you can envision how strategic decisions impact the value you provide to different shareholders.

For instance, if you choose to lower price, customer delight increases. If you lower the cost of goods, you increase value for your firm but decrease it for suppliers.

This kind of framework enables you to consider strategic decisions’ impact and pursue the most favorable outcome.

4. Familiarize Yourself with Financial Statements

Any organizational leadership decision you make is bound to have financial implications. Building your decision-making skills to become familiar and comfortable with your firm’s finances is crucial.

The three financial statements you should know are:

The balance sheet , which provides a snapshot of your company’s financial health for a given period
The income statement , which gives an overview of income and expenses during a set period and is useful for comparing metrics over time
The cash flow statement , which details cash inflows and outflows for a specific period and demonstrates your business’s ability to operate in the short and long term

In addition to gauging your organization’s financial health, learn how to create and adhere to your team or department’s budget to ensure decisions align with resource availability and help your team stay on track toward goals.

By sharpening your finance skills , you can gain confidence and back your decisions with financial information.

5. Leverage Data

Beyond financial information, consider other types of data when making decisions. That data can come in the form of progress toward goals or marketing key performance indicators (KPIs) , such as time spent on your website or number of repeat purchases. Whatever the decision, find metrics that provide insight into it.

For instance, if you need to prioritize your team’s initiatives, you can use existing data about projects’ outcomes and timelines to estimate return on investment .

By leveraging available data, you can support your decisions with facts and forecast their impact.

Related: The Advantages of Data-Driven Decision-Making

6. Learn from Other Leaders

Finally, don’t underestimate the power of learning from other leaders. You can do so by networking within your field or industry and creating a group of peers to bounce ideas off of.

One way to build that group is by taking an online course. Some programs, including CLIMB , have peer learning teams built into them. Each term, you’re sorted into a new team based on your time zone, availability, and gender. Throughout your educational experience, you collaborate with your peers to synthesize learnings and work toward a capstone project—helping you gain new perspectives on how to approach problem-solving and decision-making.

In addition to learning from peers during your program, you can network before and after it. The HBS Online Community is open to all business professionals and a resource where you can give and receive support, connect over topics you care about, and collaborate toward a greater cause.

When searching for courses, prioritize those featuring real-world examples . For instance, HBS Online’s courses feature business leaders explaining situations they’ve encountered in their careers. After learning the details of their dilemmas, you’re prompted to consider how you’d handle them. Afterward, the leaders explain what they did and the insights they gained.

By listening to, connecting with, and learning from other leaders, you can discover new ways to approach your decisions.

Elevate Your Career. Transform Your Organization | Download Brochure

Gaining Confidence as a Leader

Taking an online leadership course can help you gain confidence in your decision-making skills. In a 2022 City Square Associates survey , 84 percent of HBS Online learners said they have more confidence making business decisions, and 90 percent report feeling more self-assured at work.

If you want to improve your skills, consider a comprehensive business program like CLIMB .

It features three courses on foundational topics:

Finance and accounting

And three courses on cutting-edge leadership skills:

Dynamic Teaming
Personal Branding
Leading in the Digital World

Additionally, you select an open elective of your choice from HBS Online’s course catalog .

Through education and practice, you can build your skills and boost your confidence in making winning decisions for your organization.

Are you ready to level up your leadership skills? Explore our yearlong Credential of Leadership, Impact, and Management in Business (CLIMB) program , which comprises seven courses for leading in the modern business world. Download the CLIMB brochure to learn about its curriculum, admissions requirements, and benefits.

About the Author

Cognitive Thinking Skills

Dr jane yeomans.

March 28, 2023

What are cognitive thinking skills, and what is their significance for promoting learning outcomes?

Main, P (2023, March 28). Cognitive Thinking Skills. Retrieved from https://www.structural-learning.com/post/cognitive-thinking-skills

What are Cognitive Thinking Skills?

Cognitive thinking skills are the mental processes that allow us to perceive, understand, and analyze information. These skills are essential for problem-solving, decision-making, and critical thinking. Fortunately, cognitive thinking skills can be learned and developed with practice and training.

In this article, we'll explore what cognitive thinking skills are, why they are important, and how we can promote them in the classroom. We'll also provide some practical tips and exercises to help you enhance your students' cognitive thinking skills to become more effective problem-solvers and decision-makers.

Using the house of cognition model, this article will look at each part of the house and then go on to suggest how this model can help us to understand failure to learn . The article will outline why it is important to focus on cognitive skills and cognitive processes in order to support learning.

Learning is a complex process that involves various cognitive thinking skills such as attention, memory, problem-solving, and decision-making. These skills are essential for acquiring new knowledge, making connections between different concepts, and applying what has been learned in different situations.

By understanding the different components of cognitive thinking skills and how they relate to learning, we can develop strategies to support learners and help them overcome obstacles to learning. It is important to recognize that cognitive skills are not fixed, but can be developed and improved with practice and targeted interventions.

Using the House of Cognition

The foundations of the house are the underpinning theories about cognition. These theories would need several separate articles to explore them in detail, but the ‘headlines’ of these theories are:

Social and cultural factors are important. Our culture affects the way we perceive things. Cognition and learning develop in a social context. Lev Vygotsky is probably the most well know proponent of these ideas. Social interactions promote cognitive development, particularly where those interactions are with a ‘more knowledgeable other’ (MKO). This MKO type of interaction assists the learner to perform at a higher level ;
Cognition and cognitive abilities aren’t fixed. Our thinking, reasoning and problem-solving skills can be affected by many factors. For example, dealing with trauma or the lasting effects of ACEs (adverse childhood experiences). Professor Reuven Feuerstein’s experiences in the 1950s working with young people who were Holocaust survivors led him to suggest that intelligence wasn’t fixed, because the young people he worked with had to put all their energies into coping with their trauma , resulting in a reduction in their capacity for reasoning and problem-solving.

Supporting cognitive thinking skills

How can we support and promote cognitive thinking skills? The idea of the house of cognition is one way of providing a structure for this support. It also helps us to understand some of the factors involved in successful – and unsuccessful – learning.

The bricks that make up the house of cognition are the cognitive thinking skills that all individuals develop and use. Professor Reuven Feuerstein's work, mentioned above, led him to draw up a list of what he called cognitive functions.

These cognitive functions are what we might also call thinking skills, learning-to-learn skills , cognitive processes, cognitive ability or cognitive skills. Feuerstein’s original list was of deficient cognitive functions but later this list was turned round to describe what we should, rather than what we shouldn’t see. He organised his list of cognitive thinking skills into three areas.

Input: the cognitive thinking skills that the learner needs to gather all the information that they need to complete a task or solve a problem.
Elaboration: the cognitive thinking skills that the learner needs to complete a task or solve a problem
Output: the cognitive thinking skills that the learner needs to show what they have learned

One important part of Feuerstein’s work was that he recognised the importance of the affective elements of learning. What do we mean by the affective element? Basically, this involves emotions, feelings and attitudes. It relates to how we deal with things emotionally. Feuerstein called these non intellective factors .

The cognitive thinking skills and non-intellective factors are shown in the table below. Information in this table is drawn from Adey and Shayer, Feuerstein and Lidz.

Organising cognitive thinking skills

Focussed perception, paying attention

Using all the senses to gather information

Systematic search/exploration, planning

Gathering information using a system or plan so that nothing is missed

Conservation

Knowing what stays the same and what changes.

Using labels

Giving the things we gather through our senses and our experience names so that we can remember them more clearly and talk about them

Use of temporal and spatial concepts

Using knowledge about space and time: describing things and events in terms of where and when they occur

Precision and accuracy

Being precise and accurate when it matters; recognising the need to be precise and accurate when gathering information

Considering more than one source of information

Gathering information from several sources; organising the information we gather by considering more than one thing at a time (working memory is used to hold information in our head whilst gathering other information)

ELABORATION

Defining the problem

Knowing what to do with the information gathered: what do we need to do or figure out

Using only the part of the information we have gathered that is relevant, that is, that applies to the problem, and ignoring the rest

Planning and sequencing

Making a plan that will include the steps we need to take to reach our goal, knowing what to do first, second, and so on, knowing what ‘finished’ looks like.

Being able to recognise what is the same and what is different

Categorising

Finding the class or set that new objects or experiences belong to

Projecting relationships

Seeing how things go together; looking for the relationship by which separate objects, events, and experiences can be used together

Hypothetical thinking

If………..then thinking; thinking about different possibilities and figuring out what would happen if you were to choose one or another

Working with several sources of information: memory

Working memory : holding information in your head whilst working with it.

Short term memory: recalling recent learning.

Long term memory : recalling previous learning or approaches to solving problems

Logical justification

Being able to defend your opinion or choice using logical evidence

Interiorisation

Having a good picture in our mind of what we are looking for, or what we must do

Precision and accuracy in communication

Communicating clearly using precise and accurate language so your answer is clear

Communicating outcomes

Having the necessary vocabulary/expressive skills to communicate findings

Reducing egocentric communication

Being able to put yourself in the shoes of the listener

Restraining impulsivity

Thinking before responding; reducing a trial and error approach to learning; count to 10 (at least) so that you do not say or do something you will be sorry for later

Overcoming blocking

Not being able to respond because the learner feels that s/he can’t do it. If you cannot answer a question for some reason even though you ‘know’ the answer, do not fret or panic. Leave the question for a little while and then, when you return to it, use a strategy to help you find the answer

Visual transport

Mentally lifting something up and placing it elsewhere; Carrying an exact picture of an object in your mind to another place for comparison without losing or changing some details

NON INTELLECTIVE FACTORS

Wanting to learn and take part, engagement in the task/activity/lesson. How interested is the learner?

Willingness to find out

Frustration tolerance

Coping when tasks are challenging: what does the learner do when things become tough?

Self regulation , including restraining impulsivity

Taking time to think or act

Response to challenge

Wanting to tackle a more difficult task or step of a task

Response to mediation/intervention of adults

How the learner responds to the adult’s mediation, intervention or support

Persistence

Keeping going, seeing a task through to the end

Flexibility

The learner tries out alternative solutions or self corrects, or the learner perseveres in using a strategy even when it does not work

Promoting Thinking Skills

The mortar that holds together the bricks of the house of cognition represents the process of mediation. Mediation is a specific way of supporting and promoting cognitive thinking skills . We saw that there are five layers to the cognitive map which help us to understand task demands.

Using the house of cognition analogy, if we simply pile up bricks the house won’t be stable. We have to stick the bricks together. So, if we teach cognitive thinking skills without sticking them together, they are not likely to be useful.

Mediation is a way of helping pupils to make links between the curriculum they are following and the cognitive thinking skills they are using. Feuerstein suggested that for an interaction to be called mediation, the following three essential characteristics have to be present:

Intentionality and reciprocity: all this means is that mediation is an intentional act. It doesn’t happen by accident. Reciprocity means that you will adjust your mediation according to how the pupil responds.
Meaning: this is where you communicate the importance of the task or activity. With most pupils we can tell them that ‘this is important because’….’we are doing this because….’. Where pupils are very young and still acquiring language , or have some language delay, we might communicate meaning by showing enthusiasm through your body language or tone of voice
Transcendence: this is also called bridging, because it is about building bridges between the current task, previous learning and future learning.

These essential characteristics are important because interaction can only be called mediation when these three characteristics are used. Kathy Greenberg gives a useful overview of how mediation is more than good teaching. Greenberg suggests what 'Teacher-Mediators' do in contrast to good teachers. Here are some examples:

Teacher-mediators collaborate as another learner with students
Teacher-mediators connect concepts to students' real world experiences
Teacher-mediators provide opportunities for students to explore ideas
Teacher-mediators provide extra time and assistance so every student can reflect on the process of reaching the right answer
Teacher-mediators clarify and expand students' understanding beyond the immediate needs of the context and content

Cognition in the Curriculum

The idea of building a house also illustrates the importance of cognitive thinking skills in relation to the taught curriculum. The roof of a house is the last part to be added to the building. It isn't possible to build the roof and then add the other parts of the building.

In our house of cognition, the roof represents the curriculum or the products of learning. Successful learning is being able to put the roof on the house.....and that won’t work without the foundations, bricks and mortar being in place first. Cognitive skills are important foundations for learning.

We could argue that the current National Curriculum and EYFS Early Learning Goals are the roof of the house. They set out the content of what should be learned; the products of learning. Therefore, schools and early years settings mostly work on the roof of the house. There is little or no emphasis on the other parts of the house of cognition, so there is nothing to hold up the roof.

Promoting Cognition Skills in Special Education

Promoting cognitive skills in children with special educational needs can be a challenging but rewarding task. Here are nine practical strategies that teachers can use:

Scaffold Learning : Break down complex tasks into smaller, manageable parts. This helps students understand each component before moving on to the next. This approach is particularly useful for children with dyslexia who may struggle with information overload.
Use Visual Aids : Visual aids can help children with dyspraxia and other cognitive impairments understand abstract concepts. Diagrams, charts, and other visual tools can make learning more engaging and accessible.
Promote Active Learning : Encourage students to participate actively in their learning process. This could involve hands-on activities, group work, or problem-solving tasks. Active learning promotes critical thinking and reasoning skills .
Teach Metacognitive Strategies : Metacognition, or thinking about thinking, can help students understand their own learning processes. Teaching strategies like self-questioning and reflection can improve students' ability to monitor their own understanding and adjust their learning strategies as needed.
Incorporate Technology : Assistive technology can be a powerful tool for supporting students with special educational needs. For example, speech-to-text software can help students with dyslexia improve their writing skills.
Differentiate Instruction : Tailor your teaching methods to meet the individual needs of each student. Differentiated instruction can involve adjusting the content, process, product, or learning environment to support each student's learning style and ability level.
Encourage Cooperative Learning : Group activities can promote social skills and cooperative learning . Working in a team can help students develop their communication and interpersonal skills, which are crucial for their cognitive development.
Use Real-World Examples : Applying learning to real-world situations can make abstract concepts more concrete. This can help students understand the relevance of what they're learning and improve their long-term memory.
Provide Regular Feedback : Regular, constructive feedback can help students understand their strengths and areas for improvement. This can motivate them to work on their weaknesses and enhance their cognitive skills.

Remember, every child is unique, and what works for one might not work for another. It's important to be patient, flexible, and creative in your approach.

Key Insights:

Breaking down complex tasks into smaller parts can make learning more manageable for students with cognitive impairments.
Visual aids, active learning, and real-world examples can make abstract concepts more concrete and understandable.
Regular, constructive feedback can motivate students to improve their cognitive skills.

According to a study by the American Society for Engineering Education, using real-world examples in teaching can significantly improve students' understanding and retention of information. As the famous educational psychologist Jean Piaget once said, "The goal of education is not to increase the amount of knowledge but to create the possibilities for a child to invent and discover, to create men who are capable of doing new things."

Focusing on the process of learning

So how do we put the roof on the house? This can be a challenge when there is so much emphasis on delivering the products, rather than the processes of learning. However, a few simple adjustments can be made in order to include the bricks and mortar in everyday classroom practice:

In curriculum planning , think about the cognitive functions or cognitive skills that are needed for particular tasks
During teacher-led sessions that involve explaining and/or demonstrating tasks, make explicit reference to the cognitive functions that learners will use. Here’s an example that you might use for a written expression task:

‘when you do this piece of writing you will make a plan. A plan means thinking about something before you do it and deciding the steps you need to do to finish the task. You decide what to do first and next and so on. So when we use a plan for writing we have three big chunks, the beginning, the middle and the end. Then for each chunk we will list in order what will happen in our story. When we have this plan we can begin to write in more detail’

When pupils are completing the task, use open-ended questions or prompts that focus on the learning process or cognitive skill (that is, the thinking skills). For example:

‘what does finished look like?’

‘What will you do first? And next?’

‘Let’s make a plan so you don’t miss anything out’

‘What can you do to help you to remember?’

Focusing on the processes of learning through supporting cognitive thinking skills will help children and young people to be successful and independent learners. If an individual knows how to learn they will develop skills and behaviours that are transferable to all kinds of contexts beyond the classroom and the taught curriculum .

Adey, P. and Shayer, M. (1994). Really Raising Standards: Cognitive intervention and academic achievement. London: Routledge

Greenberg, K.H. (2005). The Cognitive Enrichment Advantage Handbook . Knoxville, USA: KCD Harris and Associate Press

Feuerstein, R (nd). Developed Cognitive Functions. Source: Feuerstein Institute, www.icelp.info

Lidz, C. (2007). Application of Cognitive Functions Behaviour Rating Scale. In: Haywood and Lidz, 2007, Dynamic Assessment in Practice: Clinical and educational applications . Cambridge University Press

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Critical Thinking

Developing the right mindset and skills.

By the Mind Tools Content Team

We make hundreds of decisions every day and, whether we realize it or not, we're all critical thinkers.

We use critical thinking each time we weigh up our options, prioritize our responsibilities, or think about the likely effects of our actions. It's a crucial skill that helps us to cut out misinformation and make wise decisions. The trouble is, we're not always very good at it!

In this article, we'll explore the key skills that you need to develop your critical thinking skills, and how to adopt a critical thinking mindset, so that you can make well-informed decisions.

What Is Critical Thinking?

Critical thinking is the discipline of rigorously and skillfully using information, experience, observation, and reasoning to guide your decisions, actions, and beliefs. You'll need to actively question every step of your thinking process to do it well.

Collecting, analyzing and evaluating information is an important skill in life, and a highly valued asset in the workplace. People who score highly in critical thinking assessments are also rated by their managers as having good problem-solving skills, creativity, strong decision-making skills, and good overall performance. [1]

Key Critical Thinking Skills

Critical thinkers possess a set of key characteristics which help them to question information and their own thinking. Focus on the following areas to develop your critical thinking skills:

Being willing and able to explore alternative approaches and experimental ideas is crucial. Can you think through "what if" scenarios, create plausible options, and test out your theories? If not, you'll tend to write off ideas and options too soon, so you may miss the best answer to your situation.

To nurture your curiosity, stay up to date with facts and trends. You'll overlook important information if you allow yourself to become "blinkered," so always be open to new information.

But don't stop there! Look for opposing views or evidence to challenge your information, and seek clarification when things are unclear. This will help you to reassess your beliefs and make a well-informed decision later. Read our article, Opening Closed Minds , for more ways to stay receptive.

Logical Thinking

You must be skilled at reasoning and extending logic to come up with plausible options or outcomes.

It's also important to emphasize logic over emotion. Emotion can be motivating but it can also lead you to take hasty and unwise action, so control your emotions and be cautious in your judgments. Know when a conclusion is "fact" and when it is not. "Could-be-true" conclusions are based on assumptions and must be tested further. Read our article, Logical Fallacies , for help with this.

Use creative problem solving to balance cold logic. By thinking outside of the box you can identify new possible outcomes by using pieces of information that you already have.

Self-Awareness

Many of the decisions we make in life are subtly informed by our values and beliefs. These influences are called cognitive biases and it can be difficult to identify them in ourselves because they're often subconscious.

Practicing self-awareness will allow you to reflect on the beliefs you have and the choices you make. You'll then be better equipped to challenge your own thinking and make improved, unbiased decisions.

One particularly useful tool for critical thinking is the Ladder of Inference . It allows you to test and validate your thinking process, rather than jumping to poorly supported conclusions.

Developing a Critical Thinking Mindset

Combine the above skills with the right mindset so that you can make better decisions and adopt more effective courses of action. You can develop your critical thinking mindset by following this process:

Gather Information

First, collect data, opinions and facts on the issue that you need to solve. Draw on what you already know, and turn to new sources of information to help inform your understanding. Consider what gaps there are in your knowledge and seek to fill them. And look for information that challenges your assumptions and beliefs.

Be sure to verify the authority and authenticity of your sources. Not everything you read is true! Use this checklist to ensure that your information is valid:

Are your information sources trustworthy ? (For example, well-respected authors, trusted colleagues or peers, recognized industry publications, websites, blogs, etc.)
Is the information you have gathered up to date ?
Has the information received any direct criticism ?
Does the information have any errors or inaccuracies ?
Is there any evidence to support or corroborate the information you have gathered?
Is the information you have gathered subjective or biased in any way? (For example, is it based on opinion, rather than fact? Is any of the information you have gathered designed to promote a particular service or organization?)

If any information appears to be irrelevant or invalid, don't include it in your decision making. But don't omit information just because you disagree with it, or your final decision will be flawed and bias.

Now observe the information you have gathered, and interpret it. What are the key findings and main takeaways? What does the evidence point to? Start to build one or two possible arguments based on what you have found.

You'll need to look for the details within the mass of information, so use your powers of observation to identify any patterns or similarities. You can then analyze and extend these trends to make sensible predictions about the future.

To help you to sift through the multiple ideas and theories, it can be useful to group and order items according to their characteristics. From here, you can compare and contrast the different items. And once you've determined how similar or different things are from one another, Paired Comparison Analysis can help you to analyze them.

The final step involves challenging the information and rationalizing its arguments.

Apply the laws of reason (induction, deduction, analogy) to judge an argument and determine its merits. To do this, it's essential that you can determine the significance and validity of an argument to put it in the correct perspective. Take a look at our article, Rational Thinking , for more information about how to do this.

Once you have considered all of the arguments and options rationally, you can finally make an informed decision.

Afterward, take time to reflect on what you have learned and what you found challenging. Step back from the detail of your decision or problem, and look at the bigger picture. Record what you've learned from your observations and experience.

Critical thinking involves rigorously and skilfully using information, experience, observation, and reasoning to guide your decisions, actions and beliefs. It's a useful skill in the workplace and in life.

You'll need to be curious and creative to explore alternative possibilities, but rational to apply logic, and self-aware to identify when your beliefs could affect your decisions or actions.

You can demonstrate a high level of critical thinking by validating your information, analyzing its meaning, and finally evaluating the argument.

Critical Thinking Infographic

See Critical Thinking represented in our infographic: An Elementary Guide to Critical Thinking .

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Critical Thinking and Decision-Making - What is Critical Thinking?

Critical thinking and decision-making -, what is critical thinking, critical thinking and decision-making what is critical thinking.

Critical Thinking and Decision-Making: What is Critical Thinking?

Lesson 1: what is critical thinking, what is critical thinking.

Critical thinking is a term that gets thrown around a lot. You've probably heard it used often throughout the years whether it was in school, at work, or in everyday conversation. But when you stop to think about it, what exactly is critical thinking and how do you do it ?

Watch the video below to learn more about critical thinking.

Simply put, critical thinking is the act of deliberately analyzing information so that you can make better judgements and decisions . It involves using things like logic, reasoning, and creativity, to draw conclusions and generally understand things better.

illustration of the terms logic, reasoning, and creativity

This may sound like a pretty broad definition, and that's because critical thinking is a broad skill that can be applied to so many different situations. You can use it to prepare for a job interview, manage your time better, make decisions about purchasing things, and so much more.

The process

illustration of "thoughts" inside a human brain, with several being connected and "analyzed"

As humans, we are constantly thinking . It's something we can't turn off. But not all of it is critical thinking. No one thinks critically 100% of the time... that would be pretty exhausting! Instead, it's an intentional process , something that we consciously use when we're presented with difficult problems or important decisions.

Improving your critical thinking

illustration of the questions "What do I currently know?" and "How do I know this?"

In order to become a better critical thinker, it's important to ask questions when you're presented with a problem or decision, before jumping to any conclusions. You can start with simple ones like What do I currently know? and How do I know this? These can help to give you a better idea of what you're working with and, in some cases, simplify more complex issues.

Real-world applications

illustration of a hand holding a smartphone displaying an article that reads, "Study: Cats are better than dogs"

Let's take a look at how we can use critical thinking to evaluate online information . Say a friend of yours posts a news article on social media and you're drawn to its headline. If you were to use your everyday automatic thinking, you might accept it as fact and move on. But if you were thinking critically, you would first analyze the available information and ask some questions :

What's the source of this article?
Is the headline potentially misleading?
What are my friend's general beliefs?
Do their beliefs inform why they might have shared this?

illustration of "Super Cat Blog" and "According to survery of cat owners" being highlighted from an article on a smartphone

After analyzing all of this information, you can draw a conclusion about whether or not you think the article is trustworthy.

Critical thinking has a wide range of real-world applications . It can help you to make better decisions, become more hireable, and generally better understand the world around you.

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Decision Making: a Theoretical Review

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Published: 15 November 2021
Volume 56 , pages 609–629, ( 2022 )

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Matteo Morelli 1 ,
Maria Casagrande ORCID: orcid.org/0000-0002-4430-3367 2 &
Giuseppe Forte 1 , 3

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Decision-making is a crucial skill that has a central role in everyday life and is necessary for adaptation to the environment and autonomy. It is the ability to choose between two or more options, and it has been studied through several theoretical approaches and by different disciplines. In this overview article, we contend a theoretical review regarding most theorizing and research on decision-making. Specifically, we focused on different levels of analyses, including different theoretical approaches and neuropsychological aspects. Moreover, common methodological measures adopted to study decision-making were reported. This theoretical review emphasizes multiple levels of analysis and aims to summarize evidence regarding this fundamental human process. Although several aspects of the field are reported, more features of decision-making process remain uncertain and need to be clarified. Further experimental studies are necessary for understanding this process better and for integrating and refining the existing theories.

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André, M., Borgquist, L., Foldevi, M., & Mölstad, S. (2002). Asking for ‘rules of thumb’: a way to discover tacit knowledge in general practice. Family Practice, 19 (6), 617–22. https://doi.org/10.1093/fampra/19.6.617

Article PubMed Google Scholar

Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50 (1–3), 7–15. https://doi.org/10.1016/0010-0277(94)90018-3

Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997). Deciding advantageously before knowing the advantageous strategy. Science, 275 (5304), 1293–5. https://doi.org/10.1126/science.275.5304.1293

Bechara, A., Damasio, H., & Damasio, A. R. (2000a). Emotion, decision making and the orbitofrontal cortex. Cerebral cortex, 10 (3), 295–307.

Bechara, A., Tranel, D., & Damasio, H. (2000b). Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions. Brain, 123 (Pt 11), 2189–202. https://doi.org/10.1093/brain/123.11.2189

Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: a neural theory of economic decision. Games and Economic Behavior, 52, 336–372. https://doi.org/10.1016/j.geb.2004.06.010

Article Google Scholar

Blanchard, T. C., Strait, C. E., & Hayden, B. Y. (2015). Ramping ensemble activity in dorsal anterior cingulate neurons during persistent commitment to a decision. Journal of Neurophysiology, 114 (4), 2439–49. https://doi.org/10.1152/jn.00711.2015

Article PubMed PubMed Central Google Scholar

Bohanec, M. (2009). Decision making: A computer-science and information-technology viewpoint. Interdisciplinary Description of Complex Systems, 7 (2), 22–37

Google Scholar

Brand, M., Fujiwara, E., Borsutzky, S., Kalbe, E., Kessler, J., & Markowitsch, H. J. (2005). Decision-Making deficits of korsakoff patients in a new gambling task with explicit rules: associations with executive functions. Neuropsychology, 19 (3), 267–277. https://doi.org/10.1037/0894-4105.19.3.267

Broche-Pérez, Y., Jiménez, H., & Omar-Martínez, E. (2016). Neural substrates of decision-making. Neurologia, 31 (5), 319–25. https://doi.org/10.1016/j.nrl.2015.03.001

Byrnes, J. P. (2013). The nature and development of decision-making: A self-regulation model . Psychology Press

Clark, L., & Manes, F. (2004). Social and emotional decision-making following frontal lobe injury. Neurocase, 10 (5), 398–403. https://doi.org/10.1080/13554790490882799

Cummings, J. L. (1995). Anatomic and behavioral aspects of frontal-subcortical circuits a. Annals of the New York Academy of Sciences, 769 (1), 1–14

Dale, S. (2015). Heuristics and biases: The science of decision-making. Business Information Review, 32 (2), 93–99. https://doi.org/10.1177/0266382115592536

Damasio, A. R. (1996). The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 351 (1346), 1413–20. https://doi.org/10.1098/rstb.1996.0125

Dewberry, C., Juanchich, M., & Narendran, S. (2013). Decision-making competence in everyday life: The roles of general cognitive styles, decision-making styles and personality. Personality and Individual Differences, 55 (7), 783–788. https://doi.org/10.1016/j.paid.2013.06.012

Doya, K. (2008). Modulators of decision making. Nature Neuroscience, 11 (4), 410–6. https://doi.org/10.1038/nn2077

Dunn, B. D., Dalgleish, T., & Lawrence, A. D. (2006). The somatic marker hypothesis: a critical evaluation. Neuroscience & Biobehavioral Reviews, 30 (2), 239–71. https://doi.org/10.1016/j.neubiorev.2005.07.001

Elliott, R., Rees, G., & Dolan, R. J. (1999). Ventromedial prefrontal cortex mediates guessing. Neuropsychologia, 37 (4), 403–411

Ernst, M., Bolla, K., Mouratidis, M., Contoreggi, C., Matochik, J. A., Kurian, V., et al. (2002). Decision-making in a risk-taking task: a PET study. Neuropsychopharmacology, 26 (5), 682–91. https://doi.org/10.1016/S0893-133X(01)00414-6

Ernst, M., & Paulus, M. P. (2005). Neurobiology of decision making: a selective review from a neurocognitive and clinical perspective. Biological Psychiatry, 58 (8), 597–604. https://doi.org/10.1016/j.biopsych.2005.06.004

Evans, J. S. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59, 255–78. https://doi.org/10.1146/annurev.psych.59.103006.093629

Fellows, L. K. (2004). The cognitive neuroscience of human decision making: A review and conceptual framework. Behavioral & Cognitive Neuroscience Reviews, 3 (3), 159–72. https://doi.org/10.1177/1534582304273251

Fellows, L. K., & Farah, M. J. (2007). The role of ventromedial prefrontal cortex in decision making: judgment under uncertainty or judgment per se? Cerebral Cortex, 17 (11), 2669–74. https://doi.org/10.1093/cercor/bhl176

Fehr, E., & Camerer, C. F. (2007). Social neuroeconomics: the neural circuitry of social preferences. Trends in Cognitive Sciences, 11 (10), 419–27. https://doi.org/10.1016/j.tics.2007.09.002

Finucane, M. L., Alhakami, A., Slovic, P., & Johnson, S. M. (2000). The affect heuristic in judgments of risks and benefits. Journal of Behavioral Decision Making, 13, 1–17. https://doi.org/10.1002/(SICI)1099-0771(200001/03)13:1<1::AID-BDM333>3.0.CO;2-S

Fischhoff, B. (2010). Judgment and decision making. Wiley Interdisciplinary Reviews: Cognitive Science, 1 (5), 724–735. https://doi.org/10.1002/wcs.65

Forte, G., Favieri, F., & Casagrande, M. (2019). Heart rate variability and cognitive function: a systematic review. Frontiers in Neuroscience, 13, 710

Forte, G., Morelli, M., & Casagrande, M. (2021). Heart rate variability and decision-making: autonomic responses in making decisions. Brain Sciences, 11 (2), 243

Forte, G., Favieri, F., Oliha, E. O., Marotta, A., & Casagrande, M. (2021). Anxiety and attentional processes: the role of resting heart rate variability. Brain Sciences, 11 (4), 480

Frith, C. D., & Singer, T. (2008). The role of social cognition in decision making. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 363 (1511), 3875–86. https://doi.org/10.1098/rstb.2008.0156

Galotti, K. M. (2002). Making decisions that matter: How people face important life choices . Lawrence Erlbaum Associates Publishers

Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451–82. https://doi.org/10.1146/annurev-psych-120709-145346

Gigerenzer, G., & Selten, R. (Eds.). (2001). Bounded Rationality: The Adaptive Toolbox . MIT Press

Goel, V., Gold, B., Kapur, S., & Houle, S. (1998). Neuroanatomical correlates of human reasoning. Journal of Cognitive Neuroscience, 10 (3), 293–302

Gold, J. I., & Shadlen, M. N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30, 535–74. https://doi.org/10.1146/annurev.neuro.29.051605.113038

Gottlieb, J. (2007). From thought to action: the parietal cortex as a bridge between perception, action, and cognition. Neuron, 53 (1), 9–16. https://doi.org/10.1016/j.neuron.2006.12.009

Gozli, D. G. (2017). Behaviour versus performance: The veiled commitment of experimental psychology. Theory & Psychology, 27, 741–758

Gozli, D. (2019). Free Choice. Experimental Psychology and Human Agency . Springer. https://doi.org/10.1007/978-3-030-20422-8_6

Group, T. M. A. D., Fawcett, T. W., Fallenstein, B., Higginson, A. D., Houston, A. I., Mallpress, D. E., & McNamara, J. M., …. (2014). The evolution of decision rules in complex environments. Trends in Cognitive Sciences , 18 (3), 153–161

Guess, C. (2004). Decision making in individualistic and collectivistic cultures. Online Readings in Psychology and Culture , 4 (1). https://doi.org/10.9707/2307-0919.1032

Gupta, R., Koscik, T. R., Bechara, A., & Tranel, D. (2011). The amygdala and decision-making. Neuropsychologia, 49 (4), 760–6. https://doi.org/10.1016/j.neuropsychologia.2010.09.029

Heilbronner, S. R., & Hayden, B. Y. (2016). Dorsal anterior cingulate cortex: a bottom-up view. Annual Review of Neuroscience, 39, 149–70. https://doi.org/10.1146/annurev-neuro-070815-013952

Hickson, L., & Khemka, I. (2014). The psychology of decision making. International review of research in developmental disabilities (Vol 47, pp. 185–229). Academic

Johnson, J. G., & Busemeyer, J. R. (2010). Decision making under risk and uncertainty. Wiley Interdisciplinary Reviews: Cognitive Science, 1 (5), 736–749. https://doi.org/10.1002/wcs.76

Kable, J. W., & Glimcher, P. W. (2009). The neurobiology of decision: consensus and controversy. Neuron , 63 (6),733–45. https://doi.org/10.1016/j.neuron.2009.09.003

Kahneman, D. (2003). A perspective on judgment and choice. Mapping bounded rationality. American Psychologist, 58 (9), 697–720. https://doi.org/10.1037/0003-066X.58.9.697

Kahneman, D. (2011). P ensieri lenti e veloci . Trad.it. a cura di Serra, L., Arnoldo Mondadori Editore

Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47 (2), 263–292

Kheramin, S., Body, S., Mobini, S., Ho, M. Y., Velázquez-Martinez, D. N., Bradshaw, C. M., et al. (2002). Effects of quinolinic acid-induced lesions of the orbital prefrontal cortex on inter-temporal choice: a quantitative analysis. Psychopharmacology (Berl), 165 (1), 9–17. https://doi.org/10.1007/s00213-002-1228-6

Lee, V. K., & Harris, L. T. (2013). How social cognition can inform social decision making. Frontiers in Neuroscience, 7, 259. https://doi.org/10.3389/fnins.2013.00259

Lerner, J. S., Li, Y., Valdesolo, P., & Kassam, K. S. (2015). Emotion and decision making. Annual Review of Psychology, 66 , 799–823

Loewenstein, G., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 127 (2), 267–286. https://doi.org/10.1037/0033-2909.127.2.267

Mather, M. (2006). A review of decision-making processes: weighing the risks and benefits of aging. In Carstensen, L. L., & Hartel, C. R. (Eds.), & Committee on Aging Frontiers in Social Psychology, Personality, and Adult Developmental Psychology, Board on Behavioral, Cognitive, and Sensory Sciences, When I’m 64 (pp. 145–173). National Academies Press

Mazzucchi, L. (2012). La riabilitazione neuropsicologica: Premesse teoriche e applicazioni cliniche (3rd ed.). EDRA

Mishra, S. (2014). Decision-making under risk: integrating perspectives from biology, economics, and psychology. Personality and Social Psychology Review, 18 (3), 280–307. https://doi.org/10.1177/1088868314530517

Moreira, C. (2018). Unifying decision-making: a review on evolutionary theories on rationality and cognitive biases. arXiv preprint arXiv:1811.12455

Naqvi, N., Shiv, B., & Bechara, A. (2006). The role of emotion in decision making: a cognitive neuroscience perspective. Current Directions in Psychological Science, 15 (5), 260–264. https://doi.org/10.1111/j.1467-8721.2006.00448.x

O’Doherty, J. P., Buchanan, T. W., Seymour, B., & Dolan, R. J. (2006). Predictive neural coding of reward preference involves dissociable responses in human ventral midbrain and ventral striatum. Neuron, 49 (1), 157–66. https://doi.org/10.1016/j.neuron.2005.11.014

Padoa-Schioppa, C., & Assad, J. A. (2008). The representation of economic value in the orbitofrontal cortex is invariant for changes of menu. Nature Neuroscience, 11 (1), 95–102. https://doi.org/10.1038/nn2020

Palombo, D. J., Keane, M. M., & Verfaellie, M. (2015). How does the hippocampus shape decisions? Neurobiology of Learning and Memory, 125, 93–7. https://doi.org/10.1016/j.nlm.2015.08.005

Pardo-Vazquez, J. L., Padron, I., Fernandez-Rey, J., & Acuña, C. (2011). Decision-making in the ventral premotor cortex harbinger of action. Frontiers in Integrative Neuroscience, 5, 54. https://doi.org/10.3389/fnint.2011.00054

Paulus, M. P., & Yu, A. J. (2012). Emotion and decision-making: affect-driven belief systems in anxiety and depression. Trends in Cognitive Science, 16, 476–483. https://doi.org/10.1016/j.tics.2012.07.009

Payne, J. W. (1973). Alternative approaches to decision making under risk: Moments versus risk dimensions. Psychological Bulletin, 80 (6), 439–453. https://doi.org/10.1037/h0035260

Payne, J. W., Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker . Cambridge University Press

Phelps, E. A., Lempert, K. M., & Sokol-Hessner, P. (2014). Emotion and decision making: multiple modulatory neural circuits. Annual Review of Neuroscience, 37, 263–287

Pronin, E. (2007). Perception and misperception of bias in human judgment. Trends in Cognitive Sciences, 11 (1), 37–43

Rangel, A., Camerer, C., & Read Montague, P. (2008). Neuroeconomics: The neurobiology of value-based decision-making. Nature Reviews Neuroscience, 9 (7), 545–556. https://doi.org/10.1038/nrn2357

Reyna, V. F., & Lloyd, F. J. (2006). Physician decision making and cardiac risk: Effects of knowledge, risk perception, risk tolerance, and fuzzy processing. Journal of Experimental Psychology: Applied, 12 (3), 179–195. https://doi.org/10.1037/1076-898X.12.3.179

Rilling, J. K., & Sanfey, A. G. (2011). The neuroscience of social decision-making. Annual Review of Psychology, 62, 23–48. https://doi.org/10.1146/annurev.psych.121208.131647

Robinson, D. N. (2016). Explanation and the “brain sciences". Theory & Psychology, 26 (3), 324–332

Robbins, T. W., James, M., Owen, A. M., Sahakian, B. J., McInnes, L., & Rabbitt, P. (1994). Cambridge Neuropsychological Test Automated Battery (CANTAB): a factor analytic study of a large sample of normal elderly volunteers. Dementia, 5 (5), 266–81. https://doi.org/10.1159/000106735

Rogers, R. D., Owen, A. M., Middleton, H. C., Williams, E. J., Pickard, J. D., Sahakian, B. J., et al. (1999). Choosing between small, likely rewards and large, unlikely rewards activates inferior and orbital prefrontal cortex. The Journal of Neuroscience, 19 (20), 9029–9038. https://doi.org/10.1523/JNEUROSCI.19-20-09029.1999

Rolls, E. T., & Baylis, L. L. (1994). Gustatory, olfactory, and visual convergence within the primate orbitofrontal cortex. The Journal of Neuroscience, 14 (9), 5437–52. https://doi.org/10.1523/JNEUROSCI.14-09-05437.1994

Rolls, E. T., Critchley, H. D., Browning, A. S., Hernadi, I., & Lenard, L. (1999). Responses to the sensory properties of fat of neurons in the primate orbitofrontal cortex. The Journal of Neuroscience, 19 (4), 1532–40. https://doi.org/10.1523/JNEUROSCI.19-04-01532.1999

Rosenbloom, M. H., Schmahmann, J. D., & Price, B. H. (2012). The functional neuroanatomy of decision-making. The Journal of Neuropsychiatry and Clinical Neurosciences, 24 (3), 266–77. https://doi.org/10.1176/appi.neuropsych.11060139

Rushworth, M. F., & Behrens, T. E. (2008). Choice, uncertainty and value in prefrontal and cingulate cortex. Nature Neuroscience, 11 (4), 389–97. https://doi.org/10.1038/nn2066

Sanfey, A. G. (2007). Social decision-making: insights from game theory and neuroscience. Science, 318 (5850), 598–602. https://doi.org/10.1126/science.1142996

Serra, L., Bruschini, M., Ottaviani, C., Di Domenico, C., Fadda, L., Caltagirone, C., et al. (2019). Thalamocortical disconnection affects the somatic marker and social cognition: a case report. Neurocase, 25 (1–2), 1–9. https://doi.org/10.1080/13554794.2019.1599025

Shahsavarani, A. M., & Abadi, E. A. M. (2015). The bases, principles, and methods of decision-making: A review of literature. International Journal of Medical Reviews, 2 (1), 214–225

Slovic, P., Finucane, M. L., Peters, E., & MacGregor, D. G. (2002). Rational actors or rational fools: Implications of the affect heuristic for behavioral economics. Journal of Socio-Economics, 31 (4), 329–342. https://doi.org/10.1016/S1053-5357(02)00174-9

Slovic, P., Finucane, M. L., Peters, E., & MacGregor, D. G. (2004). Risk as analysis and risk as feelings: some thoughts about affect, reason, risk, and rationality. Risk Analysis, 24, 311–322. https://doi.org/10.1111/j.0272-4332.2004.00433.x

Staerklé, C. (2015). Political Psychology. International Encyclopedia of the Social & Behavioral Sciences , 427–433. https://doi.org/10.1016/B978-0-08-097086-8.24079-8

Tremblay, S., Sharika, K. M., & Platt, M. L. (2017). Social decision-making and the brain: a comparative perspective. Trends in Cognitive Sciences, 21 (4), 265–276. https://doi.org/10.1016/j.tics.2017.01.007

Trepel, C., Fox, C. R., & Poldrack, R. A. (2005). Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk. Brain Research. Cognitive Brain Research, 23 (1), 34–50. https://doi.org/10.1016/j.cogbrainres.2005.01.016

Van Der Pligt, J. (2015). Decision making, psychology of. International Encyclopedia of the Social & Behavioral Sciences, 2 (5), 917–922. https://doi.org/10.1016/B978-0-08-097086-8.24014-2

Von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior . Princeton University Press

Weber, E. U., & Hsee, C. K. (2000). Culture and individual judgment and decision making. Applied Psychology: An International Journal, 49, 32–61. https://doi.org/10.1111/1464-0597.00005

Weller, J. A., Levin, I. P., Shiv, B., & Bechara, A. (2009). The effects of insula damage on decision-making for risky gains and losses. Society for Neuroscience, 4 (4), 347–58. https://doi.org/10.1080/17470910902934400

Williams, D. J., & Noyes, J. M. (2007). How does our perception of risk influence decision-making? Implications for the design of risk information. Theoretical Issues in Ergonomics Science, 8, 1–35. https://doi.org/10.1080/14639220500484419

Yamada, H., Inokawa, H., Matsumoto, N., Ueda, Y., & Kimura, M. (2011). Neuronal basis for evaluating selected action in the primate striatum. European Journal of Neuroscience, 34 (3), 489–506. https://doi.org/10.1111/j.1460-9568.2011.07771.x

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Morelli, M., Casagrande, M. & Forte, G. Decision Making: a Theoretical Review. Integr. psych. behav. 56 , 609–629 (2022). https://doi.org/10.1007/s12124-021-09669-x

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$\r\nMimi Lord*$

Weatherhead School of Management, Case Western Reserve University, Cleveland, OH, USA

Open-mindedness (OPM) is a construct that is considered a key foundational aspect of learning in individuals, groups and organizations. Also known as critical inquiry or reflection, OPM is believed to increase learning through examination of prior beliefs, decisions and mistakes, and also through openness to new ideas. Renowned theorists including Dewey and Argyris have emphasized the relationship between OPM and learning, yet little quantitative research has tested it or examined moderators of the linkage. The setting for the current study is that of endowment investment committees at U.S. universities and colleges who need to make knowledgeable and well-reasoned decisions about the composition of investment portfolios. Findings indicate that OPM has a positive, significant effect on group learning capacity (LCAP) and also that shared vision, which represents the group's collective purpose and direction, moderates that relationship. The literature review and discussion offer insights about how OPM is related to the research on group conflict, and how shared vision (SHV) differs from concepts such as interpersonal cohesiveness and conformity that have been associated with groupthink. A review of relevant research from the fields of organizational learning, group dynamics, and absorptive capacity provides context for the development of the hypotheses and the discussion of findings.

Introduction

From Socrates to modern learning theorists, open-mindedness (OPM) has been considered essential to learning and understanding. Dewey (1933) , Kolb (1984) and Argyris (1976) all have underscored the significance that the ancient Greeks placed on inquiry, openness, dialog, and critical reflection. Yet empirical research examining the relationship between OPM and learning is scant relative to the volumes on theory, and particularly in group decision-making domains. In addition, little empirical research has explored how interpersonal dynamics in groups might moderate the relationship between OPM and learning.

The purpose of this study, situated within the broad fields of organizational learning and behavior, is to provide additional insights about how groups learn and especially about the types of dialogs and group dynamics that foster learning. We will test the relationship between OPM and learning capacity (LCAP) in decision-making groups, as well as the effect of shared vision (SHV) on that relationship. In our literature review and discussion, we will explore how different types of cohesiveness affect group learning and effectiveness, and whether SHV is situated within the cohesiveness spectrum. We will argue that because SHV coexists with OPM in this study, it provides a positive contribution to group learning capacity. In contrast, if SHV co-existed with closed-mindedness, one could expect it (along with closed-mindedness) to detract from group learning capacity. In the latter context, SHV might be related to the strong interpersonal attraction aspect of cohesiveness associated with groupthink, a type of behavior that lacks independent critical thinking and is focused primarily on reaching consensus ( Janis, 1972 , 1983 ), thus restricting group learning capacity.

Given that OPM and group task conflict overlap with regards to the emphasis placed on critical reflection and assessment, we suggest that literature on task conflict has strong relevance to this study. Task conflict occurs when there are “disagreements among group members about the content of the tasks being performed, including differences in viewpoints, ideas, and opinions” ( Jehn, 1995 , p. 258). We will discuss similarities between OPM and task conflict, and especially when expressions of task conflict are mild rather than intense ( Todorova et al., 2014 ). While task conflict has been researched predominantly in terms of its positive influence on decision quality, the current study uses the concept of OPM as a predictor and examines its influence on a different dependent variable, learning capacity.

We adopt the term LCAP as our dependent variable because it signifies the ability of organizations, groups/teams and individuals to engage in learning processes leading to positive outcomes such as performance, competitive advantage, innovation, adaptability, and knowledge transfer ( Volberda et al., 2010 ; Van Wijk et al., 2011 ). The term has been used as a synonym for “absorptive capacity” to describe a group's ability to acquire relevant external information, integrate it with existing knowledge, and exploit it to commercial benefit ( Cohen and Levinthal, 1990 ). LCAP has been characterized as a process of gaining knowledge ( Lane et al., 2006 ), which is considered a key resource of an organization and a primary source of competitive advantage ( Barney, 1991 ; Grant, 1996 ; Kogut and Zander, 1996 ).

We define OPM as a group's critical assessment of its assumptions, beliefs and prior actions, as well as its openness to new ideas ( Sinkula et al., 1997 ; Calantone et al., 2002 ). This critical assessment concept resembles Dewey's description of reflection: “assessing the grounds (justification) of one's beliefs” ( Dewey, 1933 , p. 9). Reflection is often used as a synonym for higher-order processes ( Mezirow, 1990 ) or double-loop learning ( Argyris and Schön, 1978 ), which results in transcending current ways of thinking and acting. The OPM construct in this study also bears strong resemblance to the term “authentic inquiry” ( Mazutis and Slawinski, 2008 ), which encourages critical reflection and open dialog. Without open dialog, individuals may engage in defensive routines that inhibit their learning ( Argyris and Schön, 1978 ). They may not be willing to examine and learn from past mistakes and thus may withhold information that they perceive as detrimental to others' perceptions of themselves. When authentic dialog is encouraged, members are more likely to confront conflict through inquiry and to seek understanding without engaging in power struggles ( Mazutis and Slawinski, 2008 ).

The OPM construct has been employed widely in marketing literature as a first-order factor within a second-order reflective factor called learning orientation, which is described as a set of organizational values that influence individuals' and groups' propensity to seek and use knowledge ( Sinkula et al., 1997 ). Organizations with a learning orientation have a sense of direction for their learning as well as a critical-assessment approach that encourages open debates and questioning of assumptions ( Slater and Narver, 1995 ). Learning orientation studies typically include learning commitment and SHV as other first-order factors. To the best of our knowledge, the current study is unique in focusing on OPM and SHV as stand-alone factors that influence learning capacity. We employ OPM as having the main effect on LCAP due to its prominence in theoretical literature, and SHV as a moderator due to its motivational and purpose-oriented characteristics that would enhance the primary relationship. Learning commitment is not included in this study since we believe that its characteristics are largely subsumed in SHV and OPM.

Unlike other studies on LCAP and its antecedents in the domains of manufacturing, marketing and information technology, this study's domain is that of decision-making committees in non-profit institutions, and specifically the investment committees of college and university endowments. These committees, composed largely of alumni volunteers, typically are charged with making important decisions affecting the composition and performance of endowment portfolios. Understanding factors that affect portfolio decisions and performance is critical for college and university leaders since the endowment earnings can have a significant impact on the financial health of the institution ( Brown et al., 2010 ). As with many other decision-making groups whose environments are constantly changing, investment committees need to be able to acquire relevant information from the external world (i.e., the financial markets and external experts) on a continual basis, to assimilate it with their existing knowledge, and to implement it successfully. In a quantitative study about endowment management ( Lord, 2014b ), committees who understood how to implement their investment-related knowledge had more diversified portfolios and higher risk-adjusted returns.

In the following section, we will formalize our hypotheses by examining research on learning capacity, OPM and shared vision. Our empirical study is based on a survey of “key informants” who are involved with investment committees at 168 U.S. university endowments.

Background and Hypotheses

Our study is situated in the field of organizational learning, which has been defined as a process of improving organizational actions through better knowledge and understanding ( Fiol and Lyles, 1985 ; Garvin, 1993 ). Organizational learning researchers have addressed cognitive types of learning ( Kolb, 1984 ; Argyris, 1999 ) as well as learning processes ( Levitt and March, 1988 ; Huber, 1991 ; Tippins and Sohi, 2003 ). Certain researchers ( Huber, 1991 ; Tippins and Sohi, 2003 ) refer to four processes in organizational learning: (a) information acquisition; (b) information sharing; (c) information interpretation; and (d) information storage. Other organizational learning researchers refer to only two processes: (a) explore and exploit ( March, 1991 ); (b) organizational search and trial/error ( Levitt and March, 1988 ); and (c) reflection and action ( Edmondson, 2002 ). Learning theorists differ as to whether taking action (or exploiting) is a requirement of organizational learning. Huber (1991) and Tippins and Sohi (2003) clearly do not have that requirement. In fact, Huber states that organizational learning has occurred if, through the group's processing of information, the range of its potential behaviors has changed. In contrast, Edmondson (2002) , March (1991) and Levitt and March (1988) clearly require that action must be taken in order for learning to have occurred.

Another stream of research related to the field of organizational learning is called “knowledge management” ( Bassi, 1999 ), which focuses largely on managing what is learned, including storing and retrieving knowledge. Also related is the dynamic capabilities framework, developed by Teece et al. (1997) , which refers to the ability to renew and adapt competencies in order to be in sync with rapidly changing business environments.

While incorporating aspects of these related constructs, LCAP is distinguished by its emphasis on acquiring relevant “external” information and by its imperative of implementing or “exploiting” the knowledge successfully. LCAP has been theorized and employed in research studies as having one, two, three or four dimensions (or processes). In early conceptualizations of the learning (or absorptive) capacity construct, Cohen and Levinthal (1989 , 1990) referred to its three dimensions of identifying relevant information, assimilating it, and applying new knowledge successfully, yet they did not provide a measurement tool other than research and development expenditures. Szulanski (1996) used a unidimensional measure and found that the lack of recipient absorptive capacity is a major barrier to knowledge transfer between different functions in an organization. Zahra and George (2002) re-conceptualized the construct into two primary dimensions with each having two sub-dimensions: potential absorptive capacity consisting of acquisition and assimilation of new external knowledge; and realized absorptive capacity consisting of knowledge transformation and exploitation. Jansen et al. (2005) operationalized the construct with all four sub-dimensions and tested for antecedents of coordination, systems, and socialization capabilities. Lichtenthaler (2009) followed Cohen and Levinthal's guidance of three dimensions, employing exploratory, transformative, and exploitative learning processes with measurement items borrowed from previous studies. In sum, the LCAP construct has been operationalized in multiple ways with varying dimensions and scales ( Lane et al., 2006 ). In this study we are focused on the holistic meaning of LCAP and not on the distinct dimensions or processes of it. Therefore, we employ a unidimensional factor for LCAP that we believe captures Cohen and Levinthal's (1989 , 1990) conceptualization.

Our hypotheses in this study are in alignment with: (a) the learning disciplines of Senge (1990) that emphasize the need for SHV and open dialogs that are oriented to finding truth; (b) a set of learning-oriented activities called “teaming” which encourage group members to collaborate and to engage in honest and reflective conversations ( Edmondson, 2012 ); and (c) a learning environment called “ba” which supports learning creation and an ongoing re-evaluation of existing premises ( Nonaka et al., 2000 ).

Open-Mindedness and Learning Capacity

Dewey (1933) stated that OPM (which he called “reflection”) refers to assessing the grounds or justification of one's beliefs. Similarly, more recent researchers argue that OPM is critical for examining individuals' mental models, which are deeply held beliefs or conceptions that may confine them to familiar patterns of thinking and acting ( Senge, 1990 ; Day and Nedungadi, 1994 ; Sinkula et al., 1997 ). If these deeply held beliefs and assumptions are not questioned and altered, groups' effectiveness will be diminished ( Day, 1994 ; Sinkula, 1994 ). When group members have differences in their interpretation of task-related issues, they experience greater learning and gain a more accurate assessment of situations ( Fiol, 1994 ). Argyris and Schön (1978) maintain that a key aspect of OPM is its attention to detecting and correcting errors, which they consider essential to organizational learning.

Examination of deeply held convictions and consideration of alternative perspectives often involve a relatively high level of disagreement ( Janis, 1972 ; Jehn, 1995 ; Slater and Narver, 1995 ). Disagreement that remains task-oriented is referred to as both “cognitive conflict” and “task conflict” and has been found to result in higher-quality decisions ( Amason and Schweiger, 1994 ; Amason, 1996 ). In their research on corporate board decision-making, Forbes and Milliken (1999) argued that cognitive conflict fosters an environment that is characterized by a task-oriented focus and a tolerance of multiple viewpoints and opinions; thus, it promotes critical discussions and helps to prevent groupthink. Because cognitive conflict remains task-oriented, it is not to be confused with affective (or relationship) conflict, which can become personal and damage the group's commitment and ability to work together ( Amason, 1996 ). Researchers have suggested techniques and tools to help leaders and group members foster and maintain OPM so that conflicts remain task-oriented and not personal. Among those are: (a) developing and expressing one's own view; (b) questioning and understanding other views; (c) integrating and creating solutions; and (d) agreeing to and implementing solutions ( Tjosvold et al., 2014 ). Another suggestion is to assign a member (or members) to serve as a devil's advocate, questioning group members' underlying assumptions and opinions ( Amason, 1996 ).

Cognitive (or task) conflict has typically been studied as an antecedent to higher quality decisions rather than to learning capacity. One empirical study found that “openness” led to organizational learning ( Hult et al., 2000 ) but the openness construct had two dimensions, participativeness and reflectiveness, whereas only the latter resembles the OPM construct employed in this study. As noted previously, OPM has been used in empirical studies more as a first-order factor of learning orientation than as a stand-alone construct. In a study that did examine it as a stand-alone factor, OPM was found to have a significant and positive effect on product innovation ( Calisir et al., 2013 ); the study did not employ a learning construct. Although a significant body of literature has discussed the linkage between OPM and learning, we have been unable to find a study that empirically tests that relationship in group decision-making settings.

Hypothesis 1. Open-mindedness will have a positive effect on learning capacity .

Shared Vision as a Moderator

SHV has been described as the embodiment of a group's collective goals and aspirations ( Tsai and Ghoshal, 1998 ) as well as its shared sense of purpose and operating values ( Senge, 1990 ). SHV is considered essential for proactive learning because it fosters commitment, energy and purpose among group members ( Tobin, 1993 ; Day, 1994 ). Similarly, Senge (1990) states that learning cannot occur without SHV since it provides the “pull” toward goals that helps to overcome forces of inertia.

SHV helps to motivate teams ( Van den Bossche et al., 2006 ); to promote sharing of perspectives and knowledge ( Bunderson and Reagans, 2010 ); to promote positive feelings and commitment among members ( Boyatzis, 2008 ); to foster greater organizational engagement ( Mahon et al., 2014 ); and to legitimize the acquisition and assessment of new knowledge ( Lyles and Salk, 1996 ). When team members share common or cooperative goals they are open to problem-solving approaches that help them learn from mistakes ( Tjosvold et al., 2004 ); in contrast, competitive goals have been found to correlate negatively with collective problem-solving approaches and to undermine group learning. Tsai and Ghoshal (1998) state that SHV and collective goals are reflections of the cognitive dimension of social capital.

Strong interpersonal cohesiveness of group members, on the other hand, has been associated with groupthink ( Mullen et al., 1994 ), which has been described as a dysfunctional mode of decision making that can occur when there is a lack of independent critical thinking and when there is a strong desire to have unanimity among members ( Janis, 1972 , 1983 ). However, while cohesiveness may be a determinant of groupthink, it is not sufficient ( Janis, 1972 ). Cohesiveness must be accompanied by directive leadership and a lack of cognitive conflict to foster groupthink; when cognitive conflict is present it fosters an environment with a task-oriented focus and a tolerance of multiple viewpoints and opinions ( Janis, 1983 ; Bernthal and Insko, 1993 ). Thus, a distinction has been made between a type of cohesiveness that is task-oriented and a type that is focused on interpersonal attraction, with only the latter being linked to groupthink ( Hogg, 1993 ). This view was supported in a quantitative study by Mullen et al. (1994) : interpersonal attraction contributed to groupthink and poor decision quality, whereas commitment to task tended to ward it off. Researchers also have studied the possible relationship between conformity and groupthink, and particularly when there is a strong “compliance” aspect to conformity. Compliance refers to situations where group members are in agreement publicly but are not in agreement privately; this can occur when members suppress their private doubts about the group decision for reasons such as fear of recrimination if they were to dissent ( McCauley, 1989 ).

Our argument in the current study is that SHV is about collective purpose, goals and tasks that increase the effect of OPM on learning capacity. In this study, SHV is not driven by a desire to be unanimous due to either strong interpersonal attraction or compliance motives that have been associated with groupthink. Thus, it seems logical that SHV would provide the beneficial effect of keeping open-minded dialogs on a collective learning track that supports the group's goals.

Hypothesis 2. Shared vision will strengthen the positive effect of open-mindedness on learning capacity .

Figure 1 shows the hypothesized model, with SHV moderating the effect of OPM on learning capacity.

Figure 1. Hypothesized model .

Data Collection, Screening and Sample

Empirical data to test the hypothesized relationships were obtained by an electronic survey. Emails soliciting participation were sent to 650 colleges and universities, all of which had participated in the 2009 endowment survey by the National Association of College and University Business Officers (NACUBO) and Commonfund (2009–2010) , or in previous annual surveys sponsored solely by NACUBO. Non-members of NACUBO may purchase a version of the 2009 study at http://www.nacubo.org/Products/Online_Research_Products/2009_NACUBO_Commonfund_Study_of_Endowments.html . Emails were addressed to financial officers requesting survey participation by a “key informant”: someone who had regularly attended investment committee meetings for at least several years and was very familiar with the committee's composition, responsibilities, nature of discussions, and decision-making practices. The solicitation email suggested that either the university financial officer most involved with the endowment or the investment committee chair would be an ideal respondent. The Institutional Review Board at the author's institution approved the Informed Consent and ethical conduct of the study, and all protocols governing the use of human subjects were followed. After the initial email of solicitation in September 2010, three reminders were e-mailed over the subsequent 3–4 weeks. Since the questions in the survey related to a period that ended more than a year earlier (June 30, 2009), we were not concerned with slight differences in survey response dates.

A total of 191 colleges/universities responded to the survey; the usable number was reduced to 168, or 25.8%, after eliminating nine cases due to incomplete surveys, three outliers, and 11 institutions for which certain objective data were not available from NACUBO studies. The three outliers had Cook's Distance values greater than 1.0, the threshold suggested as being problematic by Tabachnick and Fidell (2007 , p. 75). To determine if the sample was representative of the 650 colleges with five-year performance data in the 2009 NACUBO–Commonfund survey, we conducted an independent samples t -test of the means of the five-year annualized performance returns. No significant difference was observed between the means ( t = −0.656; df = 815; p = 0.512). The mean return from the NACUBO–Commonfund study was 2.70%, s = 2.55%, while the mean of this sample was 2.56%; s = 2.10%.

All but four respondents were finance, foundation, or investment officers at their colleges or universities; two were outsourced chief investment officers and two were investment committee members. On average, respondents had served 11 years in an endowment-related role with the college/university. Respondents were from both public (39%) and private (61%) institutions and the size of endowments spanned all six categories in the annual NACUBO–Commonfund study, from less than $25 million to greater than $1 billion. The average endowment size of survey participants as of fiscal year-end 2009 was $315 million, compared to $306 million in the 2009 NACUBO–Commonfund study.

The investment committees in our sample play important roles in key decisions concerning the management of the endowment portfolio. Approximately two-thirds of respondents indicated that the committee makes final decisions about hiring/firing managers and consultants, as well as policy asset allocations.

Measurement

The full questionnaire included more than 70 items including those relating to factors for the structural model in this study as well as other data about governance issues, staffing, performance and asset allocation. Certain factors that are not included in this study were used in a previous paper about group factors leading to diversified investment portfolios and superior financial performance ( Lord, 2014b) ; information about some of those other factors is included later in this paper in the section called Other Findings. For our model in the current study, we used the items for the latent factors of OPM, SHV and learning capacity. For control variables we used staff size and committee meeting frequency as they were said to relate to learning and performance in a qualitative study of endowments ( Lord, 2014a ).

Independent and Interaction Variables

The scale items for all latent factors employed a 7-point Likert scale ranging from Very Strongly Disagree to Very Strongly Agree; they are provided in the appendix. Items for the independent variable (OPM) and the interaction moderator (shared vision) were adopted from existing scales ( Sinkula et al., 1997 ; Calantone et al., 2002 ). An example of the items in the OPM construct was, “The committee was not afraid to reflect critically on investment-related assumptions it made,” and a sample item from the SHV construct was, “Our committee was in agreement about the endowment's purpose.”

Dependent Variable

The LCAP scale was developed and adapted from research in the field of absorptive capacity: Jaworski and Kohli (1993) ; Zahra and George (2002) ; Jansen et al. (2005) , and Szulanski (1996) . Items included: “The committee collected in-depth information that was relevant to our investment decisions,” and “The committee knew how to implement new investment knowledge.”

Factor Analysis

Sampling adequacy is excellent with a reading of 0.926 for the Kaiser-Meyer-Olkin Measure of Sampling. Bartlett's test of Sphericity is significant at the 0.000 level, indicating that there are correlations in the data set that are appropriate for factor analysis. Exploratory factor analysis (EFA) was conducted simultaneously with all the items for the latent factors using principal axis factoring with Promax rotation. The purpose of EFA was to determine if the observed variables loaded together as expected, were adequately correlated, and met the criteria of reliability and validity. Three latent factors were clearly observed with sufficient item loadings on each and with minimal cross-loadings. The EFA included the eigenvalues of 11.213 for learning capacity, 2.346 for OPM and 1.348 for shared vision. We assessed scale reliability for each latent factor with Cronbach's alpha, a measure of internal consistency or the closeness of the items for each factor. The Cronbach's alpha is high for all three factors: OPM (0.871), SHV (0.904), and LCAP (0.939), indicating high internal consistency.

EFA was followed by confirmatory factor analysis (CFA) for more rigorous testing and validation of the factor structure. We computed composite reliability (CR) scores for each factor, which were above the minimum threshold of 0.700. CR was 0.860 for OPM, 0.919 for shared vision, and 0.939 for learning capacity. Convergent validity was tested by calculating the average variance extracted (AVE); all factors had an AVE above the recommended threshold of 0.500 ( Kline, 2011 ). Next, we tested discriminant validity by reviewing the maximum shared variance (MSV) and the average shared variance (ASV) for each factor and confirmed that they were less than the AVE for each factor. Discriminant validity was also confirmed in that the square root of the AVE was greater than the inter-factor correlations ( Fornell and Larcker, 1981 ). See Table 1 for details on these measures; square root of the AVE is on the diagonal.

Table 1. Convergent and discriminant validity and reliability .

The goodness of fit statistics for the measurement model are shown in Table 2 along with the “ideal thresholds” outlined by Hu and Bentler (1999) . Model fit is acceptable in that all ideal thresholds are met except for root mean square error of approximation (RMSEA) which is extremely close at 0.061; other research ( Steiger, 2007 ) stipulates an upper RMSEA limit of 0.07 for acceptable fit.

Table 2. Fit statistics for measurement model .

Because items for our study's three latent factors were collected via the same instrument at the same time, it was prudent to conduct a common method bias test. We used the common latent factor (CLF) method advocated by MacKenzie and Podsakoff (2012) when no theoretically driven marker variable is collected. We compared the standardized regression weights before and after adding the CLF and found that the differences were all less than 0.200, thus indicating that the model does not suffer from common method bias.

Hypotheses were tested using covariance-based structural equation modeling (SEM) with IBM's AMOS program. Hypothesis 1 is supported in that the standardized regression weight from OPM to LCAP is positive and significant at the 0.001 level. The model with standardized regression weights is shown in Figure 2 .

Figure 2. Structural model results .

Hypothesis 2 is also supported in that SHV strengthens the positive effect of OPM on learning capacity. This can be shown in Figure 3 . When SHV is high, the slope of the relationship between OPM and LCAP is steeper; and when SHV is low, the line is flatter. The standardized regression weight between the interaction variable (OPM X SHV) and the dependent variable (LCAP) is positive and significant at the 0.001 level. In sum, SHV moderates the effect of OPM on LCAP by strengthening the positive relationship.

Figure 3. Interaction effect .

Model fit is excellent as shown in Table 3 along with the “ideal thresholds” outlined by Hu and Bentler (1999) . All thresholds are met. R-squared is also excellent at 73.4%; this reveals how much of the variance in the dependent variable is explained by the predictors.

Table 3. Fit statistics for structural model .

Other Findings

Our survey also collected data on the degree of diverse investment expertise among committee members; this refers to the breath of investment expertise across various asset classes (such as domestic equities, international equities, fixed income, real estate, hedge funds and private equity). In the previous study ( Lord, 2014b ), diverse investment expertise was found to contribute both to knowledge acquisition and to knowledge implementation. This finding was in alignment with theory by Cohen and Levinthal (1989 , 1990 ) that prior experience is a key determinant of absorptive (or learning) capacity. For this paper we employ one item representing diverse investment expertise and divide the respondents into two roughly equal groups. Group 1 consists of the 88 respondents who answered either “very strongly agree” or “strongly agree” to the following statement: “Our committee over the five-year period always included expertise across a broad variety of asset classes.” Group 2 consists of 80 respondents who answered either very strongly disagree, strongly disagree, somewhat disagree, neutral, or somewhat agree. In Table 4 we can see the differences in the mean scores between the two groups for shared vision, OPM and learning capacity.

Table 4. Differences in groups based on levels of diverse investment expertise .

Using the independent samples t -test, there was a significant difference in the mean scores for the two groups across all three factors in the study; significance was at 0.001 for each factor and degrees of freedom were 166. The following t values were reported for each variable: shared vision, 4.273; OPM, 6.654; and learning capacity, 7.880. In sum, committees that had more diversified investment expertise across asset classes had higher levels of shared vision, OPM and LCAP than committees with less diversified expertise across asset classes. Therefore, committees wanting to increase their levels of shared vision, OPM and LCAP may want to consider diversifying the types of expertise on the committee. In the current research, the expertise that was examined all related to the broad realm of investments but it included variety of expertise within that realm.

In addition, our survey collected data on the degree of portfolio diversification across different asset classes, as discussed in a previous study ( Lord, 2014b ). By dividing our sample into two halves—one with the most diversified portfolios and the other with the least diversified portfolios—we found that the halves differ significantly with regards to the three variables in this study. For OPM and learning capacity, differences in the mean responses between more diversified portfolios and less diversified portfolios were significant at the 0.001 level. And for shared vision, the difference between more and less diversified portfolios was significant at the 0.01 level. In sum, more diversified investment portfolios occurred in committee environments of higher shared vision, OPM and learning capacity. In the previous paper ( Lord, 2014b ), portfolios with greater diversification among asset classes had higher risk-adjusted returns relative to their peers of similar size over a five-year period.

The findings in this study provide strong support to learning theorists' belief that OPM (also referred to as critical assessment, authentic inquiry or reflection) is a key determinant of learning capacity. In addition, the study is novel in finding that SHV has a positive and significant effect on the relationship between OPM and learning capacity. It is important to keep in mind that OPM in this study has a greater impact on LCAP than does shared vision. The co-existence of SHV and OPM in the model's configuration produces a greater effect on LCAP than OPM alone. In our view, that's because SHV not only provides direction and motivation for the group's efforts but also because its moderating effect is on an already-strong learning environment. If, as mentioned previously, closed-mindedness were hypothesized to reduce learning capacity, SHV could be expected to augment that effect. Therefore, consideration must be given to the context or environment in which SHV exists. In extreme situations, SHV could be used in studies with horrific results. Consider a model where “Hatred of Jews” contributed to “Deaths at Auschwitz.” It would seem logical to assume that “shared vision” among Hitler and his cronies would augment the relationship between “Hatred of Jews” and “Deaths at Auschwitz.” Happily, SHV in the current study co-exists with an independent and a dependent variable that are dramatically more positive.

Another important consideration is that SHV should not connote rigidity of the group's beliefs or goals. Especially in an environment with strong OPM and learning capacity, group members could be expected to re-examine their existing beliefs and goals, and to be willing to alter them based on greater understanding of the context in which they operate. OPM would essentially dictate an ongoing assessment of the group's purpose and goals to determine whether they are still justified.

This study also contributes to the literature on group conflict in that previous research focused on the benefits of task conflict to decision quality ( Jehn, 1995 ; Amason, 1996 ) while this study links task conflict (as represented by OPM) to learning capacity. We believe it is quite likely that OPM (due to its similarities to task conflict) could also be found to have a positive effect on decision quality. One could easily argue that there is a strong correlation between those two outcomes. One might hypothesize, for example, that LCAP is an antecedent to decision quality. Our findings also support research positing that task conflict is very different from relationship (or affective) conflict in that the former is focused on the content of the task while the latter is focused on personal factors ( Todorova et al., 2014 ; Weingart et al., 2014 ). Relationship or affective conflict can include interpersonal criticism, individual bragging, blaming, and defensiveness—all behaviors that can inhibit group learning; these types of behavior may occur in competitive environments where the “we” is superseded by the “me.” In contrast, cognitive or task conflict is oriented toward the substance of the work and helps to reveal additional insights and perspectives that contribute to group learning. In our view, group conflict that remains task oriented could be more accurately and positively framed as “productive disagreement” rather than “task conflict.”

In a recent addition to research on task conflict, Todorova et al. (2014) differentiate between mild and intense task conflict expression . Mild task conflict expression occurs when team members debate about differing ideas or opinions, and express different viewpoints about work issues. On the other hand, intense task conflict expression occurs when members criticize each other's viewpoints, clash about objectives/goals, and argue about desired output. While it is possible for both of these expressions of task conflict to remain focused on tasks, only mild task conflict expression had a significant, positive effect on information acquisition in their study. Intense task conflict expression, on the other hand, had a significant negative effect on information acquisition. The authors suggest that frequent, intense task conflict expressions can interfere with potential informational benefits since the intensity of arguments may limit information sharing and processing. We suggest that the OPM construct in our study is very similar to mild task conflict expression, and that it supports the findings of Todorova et al. (2014) that mild task conflict expression contributes significantly to learning. We concur that intense task conflict expression starts to resemble relationship conflict, which tends to detract from learning.

As for concerns about conformity, we contend that a group climate of OPM would be negatively related to compliance behaviors that have been associated with groupthink. In addition, SHV represents group members' genuine belief that they are working collaboratively toward a common purpose whereas conformity often represents situations where group members publicly “act” as though they are in agreement when, instead, they privately disagree. When beliefs are genuine they are internalized, whereas when expressions of belief are not genuine they may indicate compliance ( McCauley, 1989 ).

While we are open to the view that SHV falls within the spectrum of cohesiveness, we would argue that the very strong influence of OPM in this study severely limits the possibility of the type of intense interpersonal cohesiveness that is associated with groupthink. In our view, groupthink is simply not compatible with either OPM or learning capacity. If group members are open-minded they are not consensus seeking for the sake of seeking consensus. In addition, if they are open-minded they want to seek new external information, to assimilate it and to apply it rather than conform to the stated group view without engaging in learning behaviors. There may be some degree of interpersonal cohesiveness built through the collective work of developing shared vision, and it could be argued that the cohesiveness around SHV may become so strong that it veers toward a group desire to be unanimous in thoughts and perspectives. In response, we offer a counterargument from this study's results that the concurrent presence of OPM–with its focus on critical assessment–will ward off that occurrence, just as we argue that SHV provides a curb on dialogs that may start out as open-minded but become so emotionally intense that they destroy the conditions and capacity for learning. In a sense, SHV and OPM may serve to regulate each other in healthy ways.

Limitations and Future Directions

Our survey was conducted of “key informants” of college and university endowments, whereas multiple responses of members from each endowment committee likely would have been more representative. In addition, given that all respondents were associated with university endowments, the study may not be generalizable to other decision-making committees or boards.

While the methodology in this study employs a one-directional causal model, with OPM and the interaction variable (OPM combined with shared vision) leading to learning capacity, we believe it is more appropriate to consider the variables as reciprocal in that relationships can go in both directions. For example, it seems logical to believe that greater LCAP could lead to greater OPM, in that more implementations of learning would provide more instances for critical reflection. In addition, more OPM and the greater understanding associated with it could augment the group's SHV about its purpose and goals. And, as noted previously, we believe that OPM could help the group refine or even adopt a new SHV if it can no longer justify the old one. In short, the variables appear to be contemporaneously intertwined.

Another possible limitation is that we did not test or control for demographic factors such as ethnicity or gender; such inclusion could have enlightened our understanding of generalizability. Also, our construct for LCAP is unidimensional whereas a multi-dimensional construct could have provided more insights regarding how OPM and the interaction variable would influence each of the learning dimensions.

In addition, the study could have provided further insights if it had included a construct for interpersonal cohesiveness; this would have permitted us to contrast the influence of SHV vs. the influence of strong interpersonal cohesiveness on the relationship between OPM and learning capacity. The personality trait called “agreeableness” might be a starting place in considering a measure.

Future research could provide further insights into conditions for greater LCAP by addressing some of the limitations noted above as well as considering factors such as leadership styles and other facets of a learning environment.

We believe this study provides new insights about group dynamics that affect collective learning. By employing SHV as a moderator of the effect of OPM on group learning capacity, the study makes an innovative contribution to other research that encompasses both SHV and OPM. Authors Amason and Sapienza (1997) discuss the need for both openness and mutuality in effective team decision-making. They define mutuality as the degree to which team members share goals and responsibilities, and openness as the team's “propensity to tolerate, encourage, and engage in open, frank expression of views.” Thus, “mutuality” is related to “shared vision,” and “openness” is related to “OPM.” Researchers stress the importance of getting the balance right ( Jehn, 1995 ; Amason and Sapienza, 1997 ). If there is too much mutuality and not enough cognitive conflict (or OPM), group members may become complacent or agree too readily such that LCAP and decision quality suffer. However, if the openness becomes so heated that it resembles intense task conflict expression, the effects can include confusion, personal conflict and even closed-mindedness, all of which would detract from learning.

In conclusion, we would argue that there's some truth to Oscar Wilde's quote: “Everything in moderation, including moderation.” A proper balance between OPM and SHV appears to offer true benefits such as greater learning capacity. On the other hand, there also may be truth to another quote by Wilde: “Moderation is a fatal thing. Nothing succeeds like excess.” With regards to the latter, excess LCAP may contribute to success. No doubt, one must choose his/her excesses carefully.

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.

Amason, A. C. (1996). Distinguishing the effects of functional and dysfunctional conflict on strategic decision making: resolving a paradox for top management teams. Acad. Manage. J . 39, 123–148. doi: 10.2307/256633

CrossRef Full Text | Google Scholar

Amason, A. C., and Sapienza, H. J. (1997). The effects of top management team size and interaction norms on cognitive and affective conflict. J. Manage . 23, 495–516. doi: 10.1016/S0149-2063(97)90045-3

Amason, A. C., and Schweiger, D. M. (1994). Resolving the paradox of conflict, strategic decision making, and organizational performance. Int. J. Confl. Manage . 5, 239–253. doi: 10.1108/eb022745

Argyris, C. (1976). Single-loop and double-loop models in research on decision making. Adm. Sci. Q . 21, 363–375. doi: 10.2307/2391848

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar

Argyris, C. (1999). On Organizational Learning, 2nd Edn . Madden, MA: Blackwell Publishing.

Argyris, C., and Schön, D. (1978). Organizational Learning: A Theory of Action Perspective . Reading, MA: Addison-Wesley.

Google Scholar

Barney, J. (1991). Firm resources and sustained competitive advantage. J. Manage . 17, 99–120. doi: 10.1177/014920639101700108

Bassi, L. (1999). “Harnessing the power of intellectual capital,” in The Knowledge Management Yearbook 1999-2000 , eds J. Cortada and J. Woods (Boston, MA: Butterworth Heinemann), 422–431.

Bernthal, P., and Insko, C. (1993). Cohesiveness without groupthink: the interactive effects of social and task cohesion. Group Organ. Manage . 18, 66–87. doi: 10.1177/1059601193181005

Boyatzis, R. E. (2008). Leadership development from a complexity perspective. Consult. Psychol. J . 60, 298–313. doi: 10.1037/1065-9293.60.4.298

Brown, J. R., Dimmock, S. G., Kang, J.-K., and Weisbenner, S. (2010). Why I Lost My Secretary: The Effect of Endowment Shocks on University Operations . NBER Working Paper No. 15861. doi: 10.14264/uql.2013.10. Available online at: http://www.nber.org/papers/w15861

CrossRef Full Text

Bunderson, J. S., and Reagans, R. E. (2010). Power, status, and learning in organizations. Organ. Sci . 22, 1182–1194. doi: 10.1287/orsc.1100.0590

Calantone, R. J., Cavusgil, S. T., and Zhao, Y. (2002). Learning orientation, firm innovation capability, and firm performance. Ind. Mark. Manage . 31, 515–524. doi: 10.1016/S0019-8501(01)00203-6

Calisir, F., Gumussoy, C. A., and Guzelsoy, E. (2013). Impacts of learning orientation on product innovation performance. Learn. Organ . 20, 176–194. doi: 10.1108/09696471311328442

Cohen, W. M., and Levinthal, D. A. (1989). Innovation and learning: the two faces of R & D. Econ. J . 99, 569–596. doi: 10.2307/2233763

Cohen, W. M., and Levinthal, D. A. (1990). Absorptive capacity: a new perspective on learning and innovation. Adm. Sci. Q . 35, 128–152. doi: 10.2307/2393553

Day, G. S. (1994). The capabilities of market-driven organizations. J. Mark . 58, 37–52. doi: 10.2307/1251915

Day, G. S., and Nedungadi, P. (1994). Managerial representations of competitive advantage. J. Mark . 58, 31–44. doi: 10.2307/1252267

Dewey, J. (1933). How We Think: A Restatement of the Relation of Reflective Thinking to the Educative Process . Lexington, MA: D.C. Heath and Company.

Edmondson, A. C. (2002). The local and variegated nature of learning in organizations: a group-level perspective. Organ. Sci . 13, 128–146. doi: 10.1287/orsc.13.2.128.530

Edmondson, A. C. (2012). Teaming: How Organizations Learn, Innovate , and Compete in the Knowledge Economy . San Francisco, CA: Jossey-Bass.

Fiol, C. M. (1994). Consensus, diversity, and learning in organizations. Organ. Sci . 5, 403–420. doi: 10.1287/orsc.5.3.403

Fiol, C. M., and Lyles, M. A. (1985). Organizational learning. Acad. Manage. Rev . 10, 803–813.

Forbes, D. P., and Milliken, F. J. (1999). Cognition and corporate governance: understanding boards of directors as strategic decision-making groups. Acad. Manage. Rev . 24, 489–505.

Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res . 18, 39–50. doi: 10.2307/3151312

Garvin, D. A. (1993). Building a learning organization. Harv. Bus. Rev . 71, 78–91.

Pubmed Abstract | Pubmed Full Text | Google Scholar

Grant, R. (1996). Prospering in dynamically-competitive environments: organizational capability as knowledge integration. Organ. Sci . 7, 375–387. doi: 10.1287/orsc.7.4.375

Hogg, M. A. (1993). Group cohesiveness: a critical review and some new directions. Eur. Rev. Soc. Psychol . 4, 85–111.

Hu, L. T., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Modeling 6, 1–55. doi: 10.1080/10705519909540118

Huber, G. P. (1991). Organizational learning: the contributing processes and the literatures. Organ. Sci . 2, 88–115. doi: 10.1287/orsc.2.1.88

Hult, G. T. M., Hurley, R. F., Giunipero, L. C., and Nichols, E. L. (2000). Organizational learning in global purchasing: a model and test of internal users and corporate buyers. Decis. Sci . 31, 293–325. doi: 10.1111/j.1540-5915.2000.tb01625.x

Janis, I. L. (1972). Victims of Groupthink . Boston, MA: Houghton-Mifflin.

Janis, I. L. (1983). Groupthink: Psychological Studies of Policy Decisions and Fiascoes . Boston, MA: Houghton-Mifflin.

Jansen, J. P., Van Den Bosch, F. A. J., and Volberda, H. W. (2005). Managing potential and realized absorptive capacity. Acad. Manage. J . 48, 999–1015. doi: 10.5465/AMJ.2005.19573106

Jaworski, B. J., and Kohli, A. K. (1993). Market orientation: antecedents and consequences. J. Mark . 57, 53–70. doi: 10.2307/1251854

Jehn, K. A. (1995). A multimethod examination of the benefits and detriments of intragroup conflict. Adm. Sci. Q . 40, 256–282. doi: 10.2307/2393638

Kline, R. B. (2011). Principles and Practices of Structural Equation Modeling . New York, NY: Guilford Publications.

Kogut, B., and Zander, U. (1996). What firms do? Coordination, identity, and learning. Organ. Sci . 7, 502–518. doi: 10.1287/orsc.7.5.502

Kolb, D. (1984). Experiential Learning . Upper Saddle River, NJ: Prentice Hall, Inc.

Lane, P., Koka, B. R., and Pathak, S. (2006). The reification of absorptive capacity: a critical review and rejuvenation of the construct. Acad. Manage. Rev . 31, 833–863. doi: 10.5465/AMR.2006.22527456

Levitt, B., and March, J. G. (1988). Organizational learning. Annu. Rev. Sociol . 14, 319–340. doi: 10.1146/annurev.so.14.080188.001535

Lichtenthaler, U. (2009). Absorptive capacity, environmental turbulence, and the complementarity of organizational learning processes. Acad. Manage. J . 52, 822–846. doi: 10.5465/AMJ.2009.43670902

Lord, M. (2014a). Smaller university endowments: team characteristics, portfolio composition and performance. Qual. Res. Financ. Mark . 6, 4–32. doi: 10.1108/QRFM-07-2012-0021

Lord, M. (2014b). University endowment committees: how a learning orientation and knowledge factors contribute to portfolio diversification and performance. Eur. J. Finan . doi: 10.1080/1351847X.2013.879536

Lyles, M. A., and Salk, J. E. (1996). Knowledge acquisition from foreign parents in international joint ventures: an empirical examination in the Hungarian context. J. Int. Bus. Stud . 27, 877–903. doi: 10.1057/palgrave.jibs.8490155

MacKenzie, S. B., and Podsakoff, P. M. (2012). Common method bias in marketing: causes, mechanisms, and procedural remedies. J. Retailing 88, 542–555. doi: 10.1016/j.jretai.2012.08.001

Mahon, E. G., Taylor, S. N., and Boyatzis, R. E. (2014). Antecedents of organizational engagement: Exploring vision, mood and perceived organizational support with emotional intelligence as a moderator. Front. Psychol . 5:1322. doi: 10.3389/fpsyg.2014.01322

March, J. G. (1991). Exploration and exploitation in organizational learning. Organ. Sci . 2, 71–87. doi: 10.1287/orsc.2.1.71

Mazutis, D., and Slawinski, N. (2008). Leading organizational learning through authentic dialogue. Manage. Learn . 39, 437–456. doi: 10.1177/1350507608093713

McCauley, C. (1989). The nature of social influence in groupthink: compliance and internalization. J. Pers. Soc. Psychol . 57, 250–260. doi: 10.1037/0022-3514.57.2.250

Mezirow, J. (1990). Fostering Critical Reflection in Adulthood . San Francisco, CA: Jossey Bass.

Mullen, B., Anthony, T., Salas, E., and Driskell, J. E. (1994). Group cohesiveness and quality of decision making, an integration of tests of the groupthink hypothesis. Small Group Res . 25, 189–204. doi: 10.1177/1046496494252003

National Association of College and University Business Officers (NACUBO) and Commonfund. (2009–2010). NACUBO-Commonfund Study of Endowments . Available online at: http://www.nacubo.org/Products/Online_Research_Products/2009_NACUBO_Commonfund_Study_of_Endowments.html

Nonaka, I., Toyama, R., and Konno, N. (2000). SECI, ba and leadership: a unified model of dynamic knowledge creation. Long Range Plann . 33, 5–34. doi: 10.1016/S0024-6301(99)00115-6

Senge, P. M. (1990). The Fifth Discipline: The Art and Practice of the Learning Organization . New York, NY: Doubleday.

Sinkula, J. (1994). Market information processing and organizational learning. J. Mark . 58, 35–45. doi: 10.2307/1252249

Sinkula, J. M., Baker, W. E., and Noordewier, T. (1997). A framework for market-based organizational learning: linking values, knowledge, and behavior. J. Acad. Mark. Sci . 25, 305–318. doi: 10.1177/0092070397254003

Slater, S. F., and Narver, J. C. (1995). Market orientation and the learning organization. J. Mark . 59, 63–74. doi: 10.2307/1252120

Steiger, J. H. (2007). Understanding the limitations of global fit assessment in structural equation modeling. Pers. Individ. Dif . 42, 893–898. doi: 10.1016/j.paid.2006.09.017

Szulanski, G. (1996). Exploring internal stickiness: impediments to the transfer of best practice within the firm. Strateg. Manage. J . 17, 27–43. doi: 10.1002/smj.4250171105

Tabachnick, B. G., and Fidell, L. S. (2007). Using Multivariate Statistics, 5th Edn . Boston, MA: Pearson Education.

Teece, D. J., Pisano, G. P., and Shuen, A. (1997). Dynamic capabilities and strategic management. Strateg. Manage. J . 18, 509–533.

Tippins, M. J., and Sohi, R. S. (2003). IT competency and firm performance: is org learning a missing link? Strateg. Manage. J . 24, 745–761. doi: 10.1002/smj.337

Tjosvold, D., Wong, A. S. H., and Chen, N. Y. F. (2014). Constructively managing conflicts in organizations. Annu. Rev. Organ. Psychol. Organ. Behav . 1, 545–568. doi: 10.1146/annurev-orgpsych-031413-091306

Tjosvold, D., Yu, Z., and Hui, C. (2004). Team learning from mistakes: the contribution of cooperative goals and problem-solving. J. Manage. Stud . 41, 1223–1245. doi: 10.1111/j.1467-6486.2004.00473.x

Tobin, D. R. (1993). Re-Educating the Corporation: Foundations for the Learning Organization . Essex Junction, VT: Oliver Wright.

Todorova, G., Bear, J. B., and Weingart, L. R. (2014). Can conflict be energizing? A study of task conflict, positive emotions, and job satisfaction. J. Appl. Psychol . 99, 451. doi: 10.1037/a0035134

Tsai, W., and Ghoshal, S. (1998). Social capital and value creation: the role of intrafirm networks. Acad. Manage. Rev . 41, 464–476. doi: 10.2307/257085

Van den Bossche, P., Gijselaers, W., Segers, M., and Kirschner, P. A. (2006). Social and cognitive factors driving teamwork in collaborative learning environments. Small Group Res . 37, 490–521 doi: 10.1177/1046496406292938

Van Wijk, R., Van Den Bosch, F. A. J., and Volberda, H. W. (2011). “Absorptive capacity: taking stock of its progress and prospects,” in Handbook of Organizational Learning and Knowledge Management, 2nd Edn ., eds M. Easterby-Smith and M. A. Lyles (Chichester: John Wiley and Sons), 273–304.

Volberda, H. W., Foss, N. J., and Lyles, M. A. (2010). Absorbing the concept of absorptive capacity: how to realize its potential in the organizational field. Organ. Sci . 21, 931–951. doi: 10.1287/orsc.1090.0503

Weingart, L., Behfar, K., Bendersky, C., Todorova, G., and Jehn, K. (2014). The directness and oppositional intensity of conflict expression. Acad. Manage. Rev . doi: 10.5465/amr.2013.0124

Zahra, S., and George, G. (2002). Absorptive capacity: a review, reconceptualization, and extension. Acad. Manage. Rev . 27, 185–203. doi: 10.5465/AMR.2002.6587995

Appendix: Constructs, Definitions and Items

Open-mindedness (opm).

The committee's critical assessment of its assumptions, beliefs and prior actions, as well as its openness to new ideas.

1. Committee members routinely judged the quality of the decisions they made.

2. The committee was not afraid to reflect critically on investment-related assumptions it made.

3. Committee members realized that the way we perceive the markets must be continually questioned.

4. Committee members routinely made critical assessments of the investment approach.

Shared Vision (SHV)

A common mental model for the direction of the organization.

1. Our committee was in agreement about the endowment's purpose.

2. Committee members were committed to the goals for the endowment.

3. There was agreement among committee members about the vision for the endowment.

4. Committee members viewed themselves as partners in our efforts for the endowment.

Learning Capacity (LCAP)

The committee's ability to acquire, assimilate and implement knowledge successfully.

1. The committee collected in-depth information that was relevant to our investment decisions.

2. The committee quickly recognized shifts in the financial markets.

3. The committee quickly analyzed and interpreted changing market conditions.

4. The committee quickly determined the usefulness of new investment-related knowledge to existing knowledge.

5. The committee was capable of assessing potential investment opportunities based on its existing knowledge.

6. The committee knew how to implement new investment knowledge.

7. The committee had routines in place that it believed are essential for superior long-term performance.

8. The committee had policies in place that it believed are essential for superior long-term performance.

9. The committee knew how to capitalize on its investment knowledge.

Keywords: learning capacity, shared vision, open-mindedness, organizational learning, absorptive capacity, cohesiveness, task conflict, groupthink

Citation: Lord M (2015) Group learning capacity: the roles of open-mindedness and shared vision. Front. Psychol . 6 :150. doi: 10.3389/fpsyg.2015.00150

Received: 15 November 2014; Accepted: 28 January 2015; Published online: 27 February 2015.

Reviewed by:

Copyright © 2015 Lord. 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) or licensor 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: Mimi Lord, Doctor of Management Program, Weatherhead School of Management, Case Western Reserve University, 10900 Euclid Ave., Cleveland OH 44106, USA e-mail: [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.

Psychology: Thinking, language, and Intelligence

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Thinking, language, and Intelligence

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Intelligence

Problem Solving

Decision making

Intelligence theories

History of Intelligence testing

Books in Library Catalog

Language Development

Learning Disorders

Dyslexia: A learning disorder characterized by impaired ability to read.

Intellectual disability: A generalized deficit or impairment in intellectual and social skills.

dysgraphia: have a learning disability that results in a struggle to write legibly

Nature & Nurture

IQ scores of identical twins tend to be more similar than those between other siblings, but whether identical twins are raised together or apart also has a bearing on how similar their IQ scores are likely to be.

Evidence indicates that genetic and environmental factors interact in complex ways in shaping intelligence.

heritability: The degree to which heredity accounts for variations on a given trait within a population.

Increasing evidence points to environmental, rather than genetic, factors in explaining racial or ethnic group differences in IQ

Learning Theorists: see language as developing according to laws of learning. refer to concepts of imitation, observation, and reinforcement.

Nativist theory: innate factors which make up children's nature cause children to attend to and acquire language in certain ways

Psycholinguistic theory: the view that language learning involves an interaction between environmental factors and an inborn tendency to acquire language

Language acquisition device (LAD): neural "prewiring" that facilitates the child's learning of grammar

Intellectual functioning appears to be influenced by interaction of genetic factors, health, personality, and sociocultural factors

The process of mentally representing and manipulating information. mental image A mental picture or representation of an object or event.

cognitive psychology: The branch of psychology that focuses on such mental processes as thinking, problem solving, decision making, and use of language.

Creativity: a form of thinking in which we combine information in new ways that provide useful solutions to problems.

George de Mestral created Velcro
Arthur Fry created adhesive used on post it notes

divergent thinking: The ability to conceive of new ways of viewing situations and new uses for familiar objects.

Tests that tap divergent thinking were originated by psychologist J. P. Guilford and his colleagues.
The Alternate Uses Test: instructs subjects to list as many possible uses as they can for a common object. The person’s score is based on the number of acceptable responses the person is able to generate

convergent thinking: The attempt to narrow down a range of alternatives to converge on the one correct answer to a problem.

cognitive processes

An analogy is a comparison between two things based on their similar features or properties
conceptual combinations: Combinations of two or more concepts into one concept, resulting in the creation of a novel idea or application.
conceptual expansion: Expanding familiar concepts by applying them to new uses.

brainstorming: A method of promoting divergent thinking by encouraging people to propose as many solutions to a problem as possible without fear of being judged negatively by others, no matter how farfetched their proposals may be.

Apply skills of problem solving to become a creative problem solver include adopting a questioning attitude, gathering information, avoiding getting stuck in mental sets, generating alternatives, sleeping on it, and test out possible solutions.

mental image: a mental picture or representation of an object or event

Many of Albert Einstein's insights derived from the use of mental imaging in the form of thought experiments

concepts: Mental categories for classifying events, objects, and ideas on the basis of their common features or properties.

helps us respond more quickly to events by reducing the need for new learning each time we encounter a familiar object or event
helps us to make sense of the world and prepares us to anticipate or predict events.

Types of Concepts

logical concepts: Concepts with clearly defined rules for membership.

natural concepts: Concepts with poorly defined or fuzzy rules for membership

Problem solving: a cognitive process in which we employ mental strategies to solve problems.

Problem Solving Strategies

algorithm: A step-by-step set of rules that will always lead to a correct solution to a problem.

Systematic random search: an algorithm for solving problems in which each possible solution is tested according to a particular set of rules

heuristic: A rule of thumb for solving problems or making judgments or decisions.

means–end heuristic: we evaluate our current situation and compare it with the end result we want to achieve. We then develop a plan to reduce the distance between the two, step by step.
backward-working heuristic: we start with a possible solution and then work backward to see if the data support the solution.
creating subgoals: we break a larger problem down into smaller, more manageable problems.

Problem Solving Road Blocks

mental set: The tendency to rely on strategies that worked in similar situations in the past but that may not be appropriate to the present situation.

functional fixedness: The tendency to perceive objects as limited to the customary functions they serve.

decision making: A form of problem solving in which we must select a course of action from among the available alternatives.

Motivated Reasoning: making decisions and judgments on the basis of emotion rather than careful evaluation of all the available evidence
Anchoring and adjustment heuristic: a decision making heuristic in which a presumption or first estimate serves as a cognitive anchor; as we receive additional information, we make adjustments but tend to remain in the proximity of the anchor

confirmation bias: The tendency to maintain allegiance to an initial hypothesis despite strong evidence to the contrary.

representativeness heuristic: A rule of thumb for making a judgment that assumes a given sample is representative of the larger population from which it is drawn. availability heuristic: The tendency to judge events as more likely to occur when information pertaining to them comes readily to mind.

insight: in Gestalt psychology, a sudden perception of relationships among elements of the mentally represented elements of a problem that permits its solution

incubation: in problem solving, a process that may sometimes occur when we stand back from a frustrated problem for a while and the solution "suddenly" appears

A system of communication composed of symbols (words, hand signs, and so on) that are arranged according to a set of rules (grammar) to form meaningful expressions.

Language is a communication system that involves using words and systematic rules to organize those words to transmit information from one individual to another.

Components of Language:

Lexicon: refers to the words of a given language

grammar: The set of rules governing how symbols in a given language are used to form meaningful expressions.

phonemes: The basic units of sound in a language.

morphemes: The smallest units of meaning in a language.

syntax: The rules of grammar that determine how words are ordered within sentences or phrases to form meaningful expressions.

semantics: The set of rules governing the meaning of words.

infinite creativity: the capacity to combine words into original sentences

displacement: the quality of language that permits one to communicate information about objects and events in another time and place

language acquisition device: Noam Chomsky’s concept of an innate, prewired mechanism in the brain that allows children to acquire language naturally.

linguistic relativity hypothesis: The proposition that the language we use determines how we think and how we perceive the world (also called the Whorfian hypothesis).

Created by Benjamin Whorf

Development of Grammar

holophrases: initial utterances of children; a single word used to express complex meaning

Telegraphic speech: end of second year, two word sentences; cut out "unnecessary" words

Overregularization: the application of regular grammatical rules for forming inflections to irregular verbs or nouns

Theories of Intelligence

The capacity to think and reason clearly and to act purposefully and effectively in adapting to the environment and pursuing one’s goals.

Emotional intelligence: the ability to recognize and manage emotions

Measuring Intelligence

Alfred Binet & Theodore Simon

subtracted the child’s mental age from his or her actual age

German psychologist, William Stern

Adopted by Binet & Simon

Henry Goddard

brought the Binet-Simon intelligence test to the United States, also held another important distinction. He briefly served as coach of the USC football team. He remains to this day the only undefeated head coach in USC history.

Stanford University psychologist, Lewis Terman

adapted the Binet-Simon test for American use, adding many items of his own and establishing criteria, or norms, for comparing an individual’s scores with those of the general population. The revised test, known as the Stanford-Binet Intelligence Scale (SBIS), was first published in 1916.

norms: The standards used to compare an individual’s performance on a test with the performance of others.

standardization: The process of establishing norms for a test by administering the test to large numbers of people who constitute a standardization sample.

David Wechsler

Wechsler scales: group test questions into a number of subtests. Each subtest measures different intellectual tasks. Highlights individual weaknesses and strengths.

Characteristics of a Good Intelligence test

reliability: The stability of test scores over time.

validity: The degree to which a test measures what it purports to measure.

predictive validity: the degree to which test scores accurately predict future behavior or performance

culture-fair tests: Tests designed to eliminate cultural biases.

mainstreaming: The practice of placing children with special needs in a regular classroom environment.

Spearman’s “g"

British psychologist Charles Spearman observed that people who scored well on one test of mental ability tended to score well on other tests. He believed that there is an underlying general factor of intelligence that allows people to do well on mental tests. He also believed that intelligence includes specific abilities that, along with “g,” contribute to performance on individual tests

Spearman’s “s"

Spearman's symbol for specific factors, or s factors, which he believed accounted for individual abilities

Psychologist Louis L. Thurstone

did not believe that any one large, dominating factor like “g” could account for intelligence. Rather, his studies pointed to a set of seven primary mental abilities: verbal comprehension, numerical ability, memory, inductive reasoning, perceptual speed, verbal fluency, and spatial relations

Psychologist Howard Gardner

Multiple Intelligence Theory:

rejects the view that there is a single entity called “intelligence.” Rather, he believes there exist different types of intelligence, multiple intelligences, that vary from person to person. Gardner identified eight different intelligences: linguistic, logical-mathematical, musical, spatial, bodily-kinesthetic, interpersonal, intrapersonal, and naturalist
According to Gardner’s model of multiple intelligences, we possess separate intelligences that we rely on to perform different types of tasks.

Psychologist Robert Sternberg

emphasizes how we bring together different aspects of our intelligence to meet the demands we face in our daily lives. Sternberg proposes a triarchic theory of intelligence, which holds that intelligence has three aspects: analytic (academic ability), creative ( Cope with situations and find many solutions to problems), and practical("street smarts").
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HR Ch.2 Flashcards
the level of one's capacity for new learning, problem solving, and decision making. Locus of Control-a continuum representing one's belief as to whether external or internal forces control one's destiny. Perception-refers to a person's interpretation of reality. Perceptual Congruence-
PDF PROBLEM SOLVING AND DECISION MAKING competency
problem solving techniques (e.g., brainstorming, consensus decision making process, etc.). 2 Applied • Analyzes and anticipates unintended consequences when making recommendations or decisions. • Forms informal problem solving teams; regularly forms and leads problem solving groups.
Critical Thinking, Problem Solving, and Decision Making
93% of employers say that "a demonstrated capacity to think critically, communicate clearly, and solve complex problems is more important than a candidate's undergraduate major.". Even more (95 %) say they prioritize hiring college graduates with skills that will help them contribute to innovation in the workplace. About 95% of those ...
PDF Teaching Problem Solving and Decision Making
Teaching Problem Solving and Decision Making A student's capacity to solve problems is critical to his or her success in educa-tion and in life. This capacity has become even more important in the context of educational reform efforts. Peterson (1996) noted that an increased focus on teach-
Intellectual Skills and How to Develop Them
Intellectual skills are significant in academic achievement, professional success, and personal development. They can be broadly categorized into several key areas: Critical Thinking. Problem-Solving. Creativity. Analytical Skills. Logical Reasoning. Information Processing. Decision-Making.
The Psychology of Decision-Making Strategies
The decision-making process can be both simple (such as randomly picking out of our available options) or complex (such as systematically rating different aspects of the existing choices). The strategy we use depends on various factors, including how much time we have to make the decision, the overall complexity of the decision, and the amount ...
3.4: Problem Solving and Decision Making
Key Takeaways. Effective problem solving involves critical and creative thinking. The four steps to effective problem solving are the following: Define the problem. Narrow the problem. Generate solutions. Choose the solution. Brainstorming is a good method for generating creative solutions.
Problem Solving
Cognitive—Problem solving occurs within the problem solver's cognitive system and can only be inferred indirectly from the problem solver's behavior (including biological changes, introspections, and actions during problem solving).. Process—Problem solving involves mental computations in which some operation is applied to a mental representation, sometimes resulting in the creation of ...
Leading through Problem Solving and Decision Making
4.5 (3457) From Channel: Leading In The New Normal Leadercamp. Problem Solving & Decision Making. The hallmark of leadership is being able to work through problems and make tough decisions using the skills and experience of your team. In this course, you'll learn how to use a three stage approach to engage your team in this effort.
How to Enhance Your Decision-Making Skills as a Leader
4. Familiarize Yourself with Financial Statements. Any organizational leadership decision you make is bound to have financial implications. Building your decision-making skills to become familiar and comfortable with your firm's finances is crucial.
Cognitive Thinking Skills
Learning is a complex process that involves various cognitive thinking skills such as attention, memory, problem-solving, and decision-making. These skills are essential for acquiring new knowledge, making connections between different concepts, and applying what has been learned in different situations.
Critical Thinking
Critical thinking is the discipline of rigorously and skillfully using information, experience, observation, and reasoning to guide your decisions, actions, and beliefs. You'll need to actively question every step of your thinking process to do it well. Collecting, analyzing and evaluating information is an important skill in life, and a highly ...
Critical Thinking and Decision-Making
Simply put, critical thinking is the act of deliberately analyzing information so that you can make better judgements and decisions. It involves using things like logic, reasoning, and creativity, to draw conclusions and generally understand things better. This may sound like a pretty broad definition, and that's because critical thinking is a ...
Decision Making: a Theoretical Review
Decision-making is a crucial skill that has a central role in everyday life and is necessary for adaptation to the environment and autonomy. It is the ability to choose between two or more options, and it has been studied through several theoretical approaches and by different disciplines. In this overview article, we contend a theoretical review regarding most theorizing and research on ...
Effective Problem-Solving and Decision-Making
There are 4 modules in this course. Problem-solving and effective decision-making are essential skills in today's fast-paced and ever-changing workplace. Both require a systematic yet creative approach to address today's business concerns. This course will teach an overarching process of how to identify problems to generate potential ...
Decision making and problem solving
The Economy Hasn't Changed Innovation. Decision making and problem solving Digital Article. Scott D. Anthony. Over the last couple of weeks in Singapore I've wandered the halls of the regional ...
Decision-Making and Problem-Solving: What's the Difference?
Decision-making is the process of choosing a solution based on your judgment, situation, facts, knowledge or a combination of available data. The goal is to avoid potential difficulties. Identifying opportunity is an important part of the decision-making process. Making decisions is often a part of problem-solving.
1.1 critical thinking, problem solving, and decision making
Critical thinking. The mental process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and evaluating information to reach an answer or conclusion. Requires a higher level of COGNITIVE PROCESSING that is REFLECTED in problem solving and decision making. Related to evaluation and has a broader scope than decision ...
Frontiers
Cognitive (or task) conflict has typically been studied as an antecedent to higher quality decisions rather than to learning capacity. One empirical study found that "openness" led to organizational learning (Hult et al., 2000) but the openness construct had two dimensions, participativeness and reflectiveness, whereas only the latter ...
LibGuides: Psychology: Thinking, language, and Intelligence
Thinking. The process of mentally representing and manipulating information. mental image A mental picture or representation of an object or event. cognitive psychology: The branch of psychology that focuses on such mental processes as thinking, problem solving, decision making, and use of language. Creativity: a form of thinking in which we ...
HR test 2 ch 2 Flashcards
situations in which people feel anxiety, tension, or pressure. burnout. constant lack of interest and motivation to perform one's job because of stress. controlling stress plan. 1. identify stressors. 2. determine their causes and consequences. 3. plan to eliminate or decrease the stress. intelligence.
Solved Job satisfaction is defined asMultiple Choicethe
Question: Job satisfaction is defined asMultiple Choicethe level of one's capacity for new learning, problem solving, and decision making.a set of attitudes toward work.one's overall attitude about oneself.a set of standards by which an individual lives.

Critical Thinking, Problem Solving, and Decision Making

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10 Intellectual Skills and How to Develop Them

What are Intellectual Skills?

Analytical Skills

How to Improve Critical Thinking

How to Improve Problem-Solving

How to Improve Creativity

How to Improve Analytical Skills

How to Improve Logical Reasoning

How to Improve Information Processing

How to Improve Decision-Making

How to Improve Memory

How to Improve Attention to Detail

How to Improve Metacognition

Intellectual Skills Conclusion

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