cognitive problem solving styles

Different Problem-Solving Styles: What Type of Problem Solver Are You?

• Post author: Valerie Soleil, B.A., LL.B.
• Post published: November 12, 2019
• Reading time: 10 mins read
• Post category: Self-Improvement / Self-Knowledge & Personality Tests / Success Skills

Problems. Problems. Problems. Life is full of little and big problems, and often it turns out that the big ones are in fact series of little ones. We all come across problems in our lives. It’s how we deal with them that’s interesting. Experts say there are different kinds of problem-solving styles .

Problem-solving is human

Problems seem like something to avoid. But in reality, they are unavoidable. Look a little closer and life is just one of those big problems full of little, unavoidable problems.

Most of us even go out of our way to find problems . Some add drama to their romantic lives to keep it spicy. Others buy crossword books or start a small business in the evenings outside of their regular work. Not for love, prizes, or riches – but the challenge.

Problem-solving is a survival tool . Perhaps we evolved it instead of claws or telepathy. Our ancestors figured how to survive the cold and eat practically – and later, healthily. Individuals learn how to use tools, achieving with our minds and environments. All of which we couldn’t achieve with just a dumb body. Communities, governments, the businesses that put food on our table. They all come together to solve problems.

Some even say that problem-solving is the primary design attribute of the human brain. As all this problem-solving got more sophisticated, that’s when we evolved to start creating problems to keep our brains fit. Just think of that crossword puzzle.

Solving problems regularly may even boost our chances of ‘survival’ by helping stave off dementia. Although science is still mixed on this. Certainly, problem-solving as part of a concerted effort towards more mental and physical exercise can extend brain function in old age. Even if can’t be shown to prevent Alzheimer’s.

But how about in our daily lives as professionals, parents, and carers? How can you boost your ability to navigate the obstacles that arise each day? Figuring out what type of problem-solver you are in the first place is a pretty good place to start.

Four Styles of Problem-Solving

Different researchers divide people into different categories of problem-solver depending on their approach. For example, one system divides us into four specific groups :

• Implementors

The Clarifier-type is cautious, methodical, and research-oriented . They ask a lot of questions . It can be a pain to have one in the room with you – but it’s probably safer if you do!

The Ideator is more instinctive . They throw potential solutions around, often without waiting to see where they land. This can be frustrating for colleagues who prefer a methodical approach. Lots of ideas may lack value or may disappear before they can be interrogated. But the ideator often has the spark of genius it requires to break a deadlock situation. To see something that no-one else saw.

The Developer is somewhere between the first two types . They value ideas but they also value the interrogation of those ideas. When they come up with a potential solution, they will quickly move to check it from every angle. Only then will they reject or accept it as the best way ahead.

The Implementor, as the name suggests, finds value a little further along in the process . They may egg the team on during ideation and development because they just want to try things out. They will – to use the common sporting analogy – take the ball and run with it.

Three Styles of Problem-Solving

Another method of looking at types like these reduces them to just three different problem-solvers :

• Inconsistent

Clearly, from the names alone, there is some overlap with the first type system. But this second way of looking at things is perhaps a bit more critical. It offers methods of improvement to each type.

For example, the Clarifier-Ideator-Developer-Implementor styles suggest the ideal configuration for a problem-solving team . However, none are considered a ‘better’ one to be than the others.

Therefore, the Intuitive-Inconsistent-Systematic system is more of a value judgement. A purely intuitive problem-solver, the system suggests, can eventually become a systematic type if they work hard enough at it.

What does that work involve? Well, first you have to figure out which type you are. (Hint: check the infographic at the foot of this article).

Intuitive Type of Problem-Solver

If you depend on your instincts, throw yourself straight into actioning a solution before doing your research or testing. Also, if you have a tendency to try to do it all yourself without consulting others – you’re the intuitive type .

Inconsistent Type of Problem-Solver

Do you take your time over a problem – sometimes too long – and tend to switch-up your approach very quickly when a solution is not forthcoming? If this is the case, you could be the inconsistent type.

This type borrows techniques from both the intuitive and systematic types, but not always effectively. You have some idea of the most effective way to solve a problem . However, you are easily discouraged from pursuing an approach to its conclusion.

Systematic Type of Problem-Solver

The systematic type is calm, methodical , but driven. Every stage of the decision-making process is given equal weight: research, analysis, ideation, deliberation, and execution. Including assessing how it all went and how to prevent similar problems arising in future.

Weaknesses of the Problem-Solving Styles

Once you’ve figured out your type, it is time to work on your weaknesses.

For the intuitive type, that means getting time-aware.

Also applying yourself more purposefully. The simplest way to get time-aware is to set yourself deadlines for coming up with solutions. How long depends on the problem, of course. Picking a deadline stops you from procrastinating too long. Or failing to get engaged with the issue.

But picking a lower-end deadline – a minimum period to spend on a problem – is also useful for the intuitive type. Refuse to decide until at least (for example) two minutes have passed. Then, hopefully, you will prevent yourself from plunging into a bad idea without giving it the required thought.

How should someone with the intuitive problem-solving style use this time? Methodically! Divide the solution-finding process into stages . Then, try to complete each stage by the given ‘sub-deadline.’ Don’t forget to pencil in time to talk with others about the problem, and your potential solution.

Ask yourself: what is the problem? What are the different factors and elements involved? What are the consequences? How do you feel about the problem? Finally, how does it affect other people?

And of course, once your solution is actioned, don’t just move on. Stop, analyze how effective your solution was and why. Then figure out what to do to prevent the problem arising again – and what to do differently if it does.

The inconsistent problem-solver has a different set of strengths and weaknesses.

They are easily distracted or filled with doubt. Doubt is an important feeling, but without a framework to assess the validity of that doubt, it will only undermine you. How can the inconsistent problem-solver type stay on the straight-and-narrow to an effective solution?

One method is to exclude others from part of the process. Too many conflicting voices can paralyze someone with the inconsistent style of problem-solving. It has been shown that the brainstorming process can be more effective if done alone than in a group. So try to do just that.

Use words or visual cues to prompt inspiration. Write or draw as you work in order. This will concretize your thought process, which is all too vulnerable to evaporating when doubt hits. You can run your ideas past the group once you’ve had a chance to think them through unencumbered.

Another method is to quantify the value of your ideas. For example, say you’ve cooked up three potential solutions to a problem. But, you have no idea which one is best. It is classic inconsistent-type behavior to lose time dithering between all three ideas, lost in indecision .

Instead, write them down in a chart. Then, give each one a score out of 5 according to its strength in whatever categories are relevant to the problem. For example, expense, time, elegance, effort. Add up the scores and see what the numbers tell you to do.

If you’re a systematic problem-solver type, congratulations: you’re the black belt of problem-solvers!

But do black belts stop learning new moves? Like heck they do! There are infinite problem-solving systems for systematic solvers to try. Each works best in different circumstances, and the true problem-solving guru knows how and when to combine elements of different styles.

The CATWOE Approach to Problem-Solving

The CATWOE approach, for example, is quite straightforward (apparently) series of questions with which to interrogate a problem. It is particularly useful in business scenarios.

• C stands for Clients – who does the problem affect?
• A stands for Actors – who will action the solution?
• T for Transformation indicates the change that is needed for the problem to dissolve.
• O is the owner – the person(s) responsible for the solution.
• W is the Worldview – the problem in its wider context
• E stands for Environmental constraints – the physical and social limits to which your solution must adhere).

Final Thoughts

As soon as you have graduated from being an intuitive or inconsistent problem-solver to becoming officially ‘systematic,’ you’ll find a ton of methods like this online and on the advice of your colleagues and mentors. But don’t run before you can walk.

Start by using the infographic below to analyze your problem-solver type . Then power-up your problem-solving style to not just survive but flourish along this long old problem-filled trek we call life.

References :

• https://professional.dce.harvard.edu
• kscddms.ksc.nasa.gov
• www.lifehack.org
• The infographic was brought to us by www.cashnetusa.com

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This Post Has One Comment

A very good insight into the problem-solving techniques and the types. Quite helpful.

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Problem-Solving Strategies and Obstacles

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

Sean is a fact-checker and researcher with experience in sociology, field research, and data analytics.

JGI / Jamie Grill / Getty Images

• Application
• Improvement

From deciding what to eat for dinner to considering whether it's the right time to buy a house, problem-solving is a large part of our daily lives. Learn some of the problem-solving strategies that exist and how to use them in real life, along with ways to overcome obstacles that are making it harder to resolve the issues you face.

What Is Problem-Solving?

In cognitive psychology , the term 'problem-solving' refers to the mental process that people go through to discover, analyze, and solve problems.

A problem exists when there is a goal that we want to achieve but the process by which we will achieve it is not obvious to us. Put another way, there is something that we want to occur in our life, yet we are not immediately certain how to make it happen.

Maybe you want a better relationship with your spouse or another family member but you're not sure how to improve it. Or you want to start a business but are unsure what steps to take. Problem-solving helps you figure out how to achieve these desires.

The problem-solving process involves:

• Discovery of the problem
• Deciding to tackle the issue
• Seeking to understand the problem more fully
• Researching available options or solutions
• Taking action to resolve the issue

Before problem-solving can occur, it is important to first understand the exact nature of the problem itself. If your understanding of the issue is faulty, your attempts to resolve it will also be incorrect or flawed.

Problem-Solving Mental Processes

Several mental processes are at work during problem-solving. Among them are:

• Perceptually recognizing the problem
• Representing the problem in memory
• Considering relevant information that applies to the problem
• Identifying different aspects of the problem
• Labeling and describing the problem

Problem-Solving Strategies

There are many ways to go about solving a problem. Some of these strategies might be used on their own, or you may decide to employ multiple approaches when working to figure out and fix a problem.

An algorithm is a step-by-step procedure that, by following certain "rules" produces a solution. Algorithms are commonly used in mathematics to solve division or multiplication problems. But they can be used in other fields as well.

In psychology, algorithms can be used to help identify individuals with a greater risk of mental health issues. For instance, research suggests that certain algorithms might help us recognize children with an elevated risk of suicide or self-harm.

One benefit of algorithms is that they guarantee an accurate answer. However, they aren't always the best approach to problem-solving, in part because detecting patterns can be incredibly time-consuming.

There are also concerns when machine learning is involved—also known as artificial intelligence (AI)—such as whether they can accurately predict human behaviors.

Heuristics are shortcut strategies that people can use to solve a problem at hand. These "rule of thumb" approaches allow you to simplify complex problems, reducing the total number of possible solutions to a more manageable set.

If you find yourself sitting in a traffic jam, for example, you may quickly consider other routes, taking one to get moving once again. When shopping for a new car, you might think back to a prior experience when negotiating got you a lower price, then employ the same tactics.

While heuristics may be helpful when facing smaller issues, major decisions shouldn't necessarily be made using a shortcut approach. Heuristics also don't guarantee an effective solution, such as when trying to drive around a traffic jam only to find yourself on an equally crowded route.

Trial and Error

A trial-and-error approach to problem-solving involves trying a number of potential solutions to a particular issue, then ruling out those that do not work. If you're not sure whether to buy a shirt in blue or green, for instance, you may try on each before deciding which one to purchase.

This can be a good strategy to use if you have a limited number of solutions available. But if there are many different choices available, narrowing down the possible options using another problem-solving technique can be helpful before attempting trial and error.

In some cases, the solution to a problem can appear as a sudden insight. You are facing an issue in a relationship or your career when, out of nowhere, the solution appears in your mind and you know exactly what to do.

Insight can occur when the problem in front of you is similar to an issue that you've dealt with in the past. Although, you may not recognize what is occurring since the underlying mental processes that lead to insight often happen outside of conscious awareness .

Research indicates that insight is most likely to occur during times when you are alone—such as when going on a walk by yourself, when you're in the shower, or when lying in bed after waking up.

How to Apply Problem-Solving Strategies in Real Life

If you're facing a problem, you can implement one or more of these strategies to find a potential solution. Here's how to use them in real life:

• Create a flow chart . If you have time, you can take advantage of the algorithm approach to problem-solving by sitting down and making a flow chart of each potential solution, its consequences, and what happens next.
• Recall your past experiences . When a problem needs to be solved fairly quickly, heuristics may be a better approach. Think back to when you faced a similar issue, then use your knowledge and experience to choose the best option possible.
• Start trying potential solutions . If your options are limited, start trying them one by one to see which solution is best for achieving your desired goal. If a particular solution doesn't work, move on to the next.
• Take some time alone . Since insight is often achieved when you're alone, carve out time to be by yourself for a while. The answer to your problem may come to you, seemingly out of the blue, if you spend some time away from others.

Obstacles to Problem-Solving

Problem-solving is not a flawless process as there are a number of obstacles that can interfere with our ability to solve a problem quickly and efficiently. These obstacles include:

• Assumptions: When dealing with a problem, people can make assumptions about the constraints and obstacles that prevent certain solutions. Thus, they may not even try some potential options.
• Functional fixedness : This term refers to the tendency to view problems only in their customary manner. Functional fixedness prevents people from fully seeing all of the different options that might be available to find a solution.
• Irrelevant or misleading information: When trying to solve a problem, it's important to distinguish between information that is relevant to the issue and irrelevant data that can lead to faulty solutions. The more complex the problem, the easier it is to focus on misleading or irrelevant information.
• Mental set: A mental set is a tendency to only use solutions that have worked in the past rather than looking for alternative ideas. A mental set can work as a heuristic, making it a useful problem-solving tool. However, mental sets can also lead to inflexibility, making it more difficult to find effective solutions.

How to Improve Your Problem-Solving Skills

In the end, if your goal is to become a better problem-solver, it's helpful to remember that this is a process. Thus, if you want to improve your problem-solving skills, following these steps can help lead you to your solution:

• Recognize that a problem exists . If you are facing a problem, there are generally signs. For instance, if you have a mental illness , you may experience excessive fear or sadness, mood changes, and changes in sleeping or eating habits. Recognizing these signs can help you realize that an issue exists.
• Decide to solve the problem . Make a conscious decision to solve the issue at hand. Commit to yourself that you will go through the steps necessary to find a solution.
• Seek to fully understand the issue . Analyze the problem you face, looking at it from all sides. If your problem is relationship-related, for instance, ask yourself how the other person may be interpreting the issue. You might also consider how your actions might be contributing to the situation.
• Research potential options . Using the problem-solving strategies mentioned, research potential solutions. Make a list of options, then consider each one individually. What are some pros and cons of taking the available routes? What would you need to do to make them happen?
• Take action . Select the best solution possible and take action. Action is one of the steps required for change . So, go through the motions needed to resolve the issue.
• Try another option, if needed . If the solution you chose didn't work, don't give up. Either go through the problem-solving process again or simply try another option.

You can find a way to solve your problems as long as you keep working toward this goal—even if the best solution is simply to let go because no other good solution exists.

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

Dunbar K. Problem solving . A Companion to Cognitive Science . 2017. doi:10.1002/9781405164535.ch20

Stewart SL, Celebre A, Hirdes JP, Poss JW. Risk of suicide and self-harm in kids: The development of an algorithm to identify high-risk individuals within the children's mental health system . Child Psychiat Human Develop . 2020;51:913-924. doi:10.1007/s10578-020-00968-9

Rosenbusch H, Soldner F, Evans AM, Zeelenberg M. Supervised machine learning methods in psychology: A practical introduction with annotated R code . Soc Personal Psychol Compass . 2021;15(2):e12579. doi:10.1111/spc3.12579

Mishra S. Decision-making under risk: Integrating perspectives from biology, economics, and psychology . Personal Soc Psychol Rev . 2014;18(3):280-307. doi:10.1177/1088868314530517

Csikszentmihalyi M, Sawyer K. Creative insight: The social dimension of a solitary moment . In: The Systems Model of Creativity . 2015:73-98. doi:10.1007/978-94-017-9085-7_7

Chrysikou EG, Motyka K, Nigro C, Yang SI, Thompson-Schill SL. Functional fixedness in creative thinking tasks depends on stimulus modality .  Psychol Aesthet Creat Arts . 2016;10(4):425‐435. doi:10.1037/aca0000050

Huang F, Tang S, Hu Z. Unconditional perseveration of the short-term mental set in chunk decomposition .  Front Psychol . 2018;9:2568. doi:10.3389/fpsyg.2018.02568

National Alliance on Mental Illness. Warning signs and symptoms .

Mayer RE. Thinking, problem solving, cognition, 2nd ed .

Schooler JW, Ohlsson S, Brooks K. Thoughts beyond words: When language overshadows insight. J Experiment Psychol: General . 1993;122:166-183. doi:10.1037/0096-3445.2.166

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

Cognitive Behavioral Therapy

Solving problems the cognitive-behavioral way, problem solving is another part of behavioral therapy..

Posted February 2, 2022 | Reviewed by Ekua Hagan

• What Is Cognitive Behavioral Therapy?
• Find a therapist who practices CBT
• Problem-solving is one technique used on the behavioral side of cognitive-behavioral therapy.
• The problem-solving technique is an iterative, five-step process that requires one to identify the problem and test different solutions.
• The technique differs from ad-hoc problem-solving in its suspension of judgment and evaluation of each solution.

As I have mentioned in previous posts, cognitive behavioral therapy is more than challenging negative, automatic thoughts. There is a whole behavioral piece of this therapy that focuses on what people do and how to change their actions to support their mental health. In this post, I’ll talk about the problem-solving technique from cognitive behavioral therapy and what makes it unique.

The problem-solving technique

While there are many different variations of this technique, I am going to describe the version I typically use, and which includes the main components of the technique:

The first step is to clearly define the problem. Sometimes, this includes answering a series of questions to make sure the problem is described in detail. Sometimes, the client is able to define the problem pretty clearly on their own. Sometimes, a discussion is needed to clearly outline the problem.

The next step is generating solutions without judgment. The "without judgment" part is crucial: Often when people are solving problems on their own, they will reject each potential solution as soon as they or someone else suggests it. This can lead to feeling helpless and also discarding solutions that would work.

The third step is evaluating the advantages and disadvantages of each solution. This is the step where judgment comes back.

Fourth, the client picks the most feasible solution that is most likely to work and they try it out.

The fifth step is evaluating whether the chosen solution worked, and if not, going back to step two or three to find another option. For step five, enough time has to pass for the solution to have made a difference.

This process is iterative, meaning the client and therapist always go back to the beginning to make sure the problem is resolved and if not, identify what needs to change.

Advantages of the problem-solving technique

The problem-solving technique might differ from ad hoc problem-solving in several ways. The most obvious is the suspension of judgment when coming up with solutions. We sometimes need to withhold judgment and see the solution (or problem) from a different perspective. Deliberately deciding not to judge solutions until later can help trigger that mindset change.

Another difference is the explicit evaluation of whether the solution worked. When people usually try to solve problems, they don’t go back and check whether the solution worked. It’s only if something goes very wrong that they try again. The problem-solving technique specifically includes evaluating the solution.

Lastly, the problem-solving technique starts with a specific definition of the problem instead of just jumping to solutions. To figure out where you are going, you have to know where you are.

One benefit of the cognitive behavioral therapy approach is the behavioral side. The behavioral part of therapy is a wide umbrella that includes problem-solving techniques among other techniques. Accessing multiple techniques means one is more likely to address the client’s main concern.

Salene M. W. Jones, Ph.D., is a clinical psychologist in Washington State.

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About problem solving styles.

Knowledge of style is important in education in a number of ways. It contributes to adults’ ability to work together effectively in teams and in large groups.  It provides information that helps educators understand their own personal strengths and how to put them to work as effectively as possible across many tasks and challenges. It helps educators communicate more effectively with each other, but also with parents, community members, and, of course, with students.  In addition to its importance for adults, style can also be important in designing and differentiating instruction.

The VIEW Model

Our approach to problem solving style (the VIEW model) represents and assesses three dimensions and six specific styles that are unique and important in understanding and guiding the efforts of individuals and groups to manage their creative problem solving and change management as effectively as possible.

Orientation to Change

The first VIEW dimension involves your preferences in two general styles for managing change and solving problems creatively. We identify this as “Orientation to Change;” its two contrasting styles are the “Explorer” and the “Developer.” Explorers thrive on and seek out novelty and original ideas (“thinking out of the box”), and they may find externally imposed procedures and structures confining and limiting to their imagination and energy. Developers are concerned with practical applications and the reality of the task, and they use their creative and critical thinking in ways that are clearly recognized by others as being helpful and valuable. They’re good at finding workable possibilities and guiding them to successful implementation. They are creative in “thinking better inside the box.”

Manner of Processing

The second dimension of VIEW, Manner of Processing describes the person’s preference for working externally (i.e., with other people throughout the process) or internally (i.e., thinking and working alone before sharing ideas with others) when managing change and solving problems.

Ways of Deciding

The third dimension of VIEW describes the major emphasis the person gives to people (i.e., maintaining harmony and interpersonal relationships) or to tasks (i.e., emphasizing logical, rational, and appropriate decisions) when making decisions during problem solving or when managing change.

Through our research and development efforts, our instrument, VIEW: An Assessment of Problem Solving Style, translates the VIEW model of style into measurable dimensions. The VIEW assessment is a practical and useful tool for anyone who wishes to understand his or her own approach to change or problem solving. Contact us for more information about the VIEW instrument.

Practical Applications of VIEW

Understanding problem solving styles can be helpful in many ways to individuals, teams, small groups, and organizations. VIEW: An Assessment of Problem Solving Style is a carefully researched, but simple and easy-to-use tool that can enable people to understand their style preferences and to use that knowledge in many powerful ways. 

These pages illustrate briefly a variety of practical applications of the VIEW assessment that cut across many settings or contexts (including, for example: large, global organizations; smaller business and professional settings; educational institutions, hospitals, religious organizations, arts organizations, or other non-profits). 

VIEW can be a valuable tool for individuals who are concerned with understanding their personal style preferences and improving their problem solving effectiveness, for teams or groups who need to work together successfully, and to organizations in their efforts to build a constructive work climate, to recognize and value diversity, and to manage change for long-term success. 

Click on any of the following ten applications of VIEW to see examples for that area. (You may also click here to Download a PDF file with all the applications examples in one file.)

• Improving Problem Solving
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• Providing and Receiving Feedback
• Facilitating Groups
• Managing Change
• Developing Leadership
• Designing Instruction
• Building Teams
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Free Resources

Click here to find a number of free resources that will explain CPS, to obtain articles that deal with both research and practice, and to obtain an extensive bibliography to give you direction for future reading and study.

Online Resources:

Click here for advanced online resources in PDF format that deal with applications of the VIEW Problem Solving Style model. These resources are available at a reasonable cost for immediate download. The cost of each one includes permission to duplicate the file for up to three other individuals at no additional charge.

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Click here for information about our extensive (newly revised and updated) distance learning modules on CPS.

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We also have print publications about problem-solving style that you can purchase.  Click here to view those publications.

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Our CPS programs and services are custom-tailored to meet your needs and interests. We will confer with you, create a complete proposal to meet your unique needs, and work closely with you to carry out our collaborative plan. Click here for more.

We believe that all people have strengths and talents that are important to recognize, develop, and use throughout life.  Read more.

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What are the 5 thinking styles? Understanding different types of thinkers

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• There are five different types of thinkers with their own thinking styles: synthesists, idealists, pragmatists, analysts, and realists.
• Synthesists stand out with their creativity and curiosity; they like to consider different ideas, views, and possibilities.
• Idealists are always setting and working toward big goals—they set the bar high and expect others to do the same.
• Pragmatists take a logical approach to problem-solving; they focus on immediate results, as opposed to long-term effects.
• Analysts are interested in the facts and data points—they have a clear procedure for doing all things.
• Realists are the perfect problem-solvers; tackle problems head-on and don’t feel challenged by your everyday conundrum.

We employ different ways of thinking. Some of us take a creative approach, while others are more analytic; some are focused on the short-term, while others think about the long-term. While we all have unique minds, our tendencies have been summed up into five recognized thinking styles: synthesists, or the creative thinkers; idealists, or the goal-setters; pragmatists, or the logical thinkers; analysts, or the rational intellectuals; and finally, realists, or the perfect problem-solvers. Which type of thinker are you?

Synthesists: The Creative Type of Thinker

Synthesists are largely defined by their creative and curious nature. Instead of leading with logic, they love to explore more abstract ideas. They ask, “What if?” and consider a range of views and possibilities. Some perceive synthesists as being argumentative, as they’re quick to bring attention to opposing views—but these creative thinkers can prevent this perception by first acknowledging others’ ideas before presenting alternatives.

Idealists: The Goal-setting Type of Thinker

Idealists set high standards and are always working toward larger-than-life goals . While others might perceive them as perfectionists, in their minds, they’re simply putting their best foot forward. These individuals are future-oriented, they value teamwork, and they expect everyone to work hard. However, it’s important for idealists to realize that others have their own standards and expectations—which might not match up with the idealist’s standards and expectations.

Pragmatists: The Logical Type of Thinker

Pragmatists don’t waste any time—they take action. They tackle problems logically, step-by step. They’re focused on getting things done, but they aren’t interested in understanding the big picture like idealists are. Rather than considering what’s best in the long-term, they think short-term. While pragmatists get things done, they can benefit from taking a step back and reflecting on big ideas.

Analysts: The Rational/Intellectual Type of Thinker

Analysts work methodically. They gather all of the facts and data, measuring and categorizing along the way. Their personality is rooted in being thorough, accurate, and rational; analysts are always looking for a formula or outlined procedure for solving problems. These individuals tend to discount other ideas, but should open their minds, as other ideas offer unique value.

Realists: The Perfect Problem-solving Type of Thinker

Realists are quick on their feet, and they do whatever it takes to solve the problem at hand. That said, realists bore easily—they don’t feel challenged by everyday problems or stressors as most do. Yet, they want to be challenged. Realists can benefit, like pragmatists, from taking a step back and looking at a problem from different angles. They should take a little more time to gather all of the information that is available to them and find the best solution (which isn’t always the first solution) before acting.

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Exploring the Complexities of Cognitive Styles

In the world of education and psychology, the concept of cognitive styles refers to the unique ways individuals perceive, process, and organize information. These styles can vary greatly from person to person, shaping how we learn, think, and solve problems. In a world of interpersonal communication, understanding the complexity of individual differences may help us navigate relationships more effectively.

The concept of cognitive styles drew significant psychological attention during the 1900’s. However, the psychological community could not agreed upon a finite definition of cognitive styles in general or of which categories should and should not belong to the growing proposed list of cognitive styles. Perhaps, this is because the extreme wide reaching range of the topic.

Key Definition:

Cognitive styles is a psychological concept referring to individuals preferred ways of processing information. Cognitive styles is a blend of cognitive abilities and personality.

Consider the variety of proposed personality types. One of the more compact lists of personality types is the Big Five , which separates personality traits based on five continuums. Just having five continuums to evaluate overall personality creates nearly countless combinations. Now take these countless personality styles and integrate them with cognitive abilities. The list grows exponentially large beyond workable and desirable analysis.

The scientific community choose to deal with this unworkable variance of cognitive styles by evaluating cognitive styles within different realms. For example, we may find inventory tests to evaluate different cognitive styles that lead to depression. A significant portion of cognitive style research is framed within the learning/teaching spectrum.

History of Cognitive Styles

Carl Jung provided much of the early philosophy on different cognitive styles. From his early observations of psychological types (1923), sprung the personality domain of psychology. Jung presented that personality types were characterized by “terms of two attitudes (extraversion and introversion ), two perceptual functions (intuition and sensing), and two judgement functions (thinking and feeling)” ( Sternberg & Grigorenko, 1997 ).

“But the complicated external conditions under which we live, as well as the presumably even more complex conditions of our individual psychic disposition, seldom permit a completely undisturbed flow of our psychic activity. Outer circumstances and inner disposition frequently favour the one mechanism, and restrict or hinder the other ; whereby a predominance of one mechanism naturally arises. If this condition becomes in any way chronic a type is produced, namely an habitual attitude, in which the one mechanism permanently dominates ; not, of course, that the other can ever be completely suppressed, inasmuch as it also is an integral factor in psychic activity” (1923).

Basically, we have inherent tendencies or sensitivities. In the course of interacting with our environment, because of our tendencies, we establish habitual patterns or styles of interaction. We refer to these patterns as a cognitive style. Or in Jung’s words, ‘psychological type.’ Others have referred to these individual differences as cognitive controls, cognitive attitudes, cognitive strategies, and as thinking styles.

A List of Cognitive Styles

An exhaustive list of cognitive styles is beyond the purpose of this article. I will list some of the more popular early styles proposed and then present some specialized styles.

Field Independence versus Field Independence

This style has notable research. Field dependence-independence was first introduced by Herman Witkin (1916-1979). He discovered a difference in subject’s perceptions of visual space. Basically, some individuals could perceive objects outside of the context while others could not. A person high in field independence seeks differentiated information while some one low in field independence fuses information with the surrounding context.

Research suggests that this traits extends beyond the visual sphere ( Guilford, 1980 ). Individuals low in field independence have difficulty restructuring problems or using resources in unconventional ways.

Reflection versus Impulsivity

Some people are more reflective and others more impulsive. A reflective individual pauses before action, considering alternative possibilities, and potential hazardous. While on the other end of the spectrum, impulsive individuals may quickly jump into action without careful reflection. Both reflection and impulsivity have benefits and costs ( Sternberg & Grigorenko, 1997 ).

I see great similarities to this polarity style with Jeffrey Gray’s behavioral activation and behavioral inhibition systems . Basically, he proposes brain activity for reward and punishment differs between people through two differentiated and independent systems. A person with high sensitivity to punishment is likely to be more reflective, evaluating possibilities before action. While a person high in sensitivity to reward is more likely to leap at perceived opportunities without considering possible risks.

Other Types

• Complexity vs. simplicity
• Tolerance vs. intolerance
• Constricted vs. flexible
• Leveling vs. sharpening
• Focused vs. scanning
• Proactive vs. protective

Many of these categories overlap. For instance, proactive vs. protective is similar to reflective vs. impulsive. The research on cognitive styles is not universal. Any researcher can identify a continuum of individual differences, define it, and conduct a study. However, this doesn’t invalidate the research. Accordingly, one must carefully evaluate each study for its own merits and not compare studies for validity if they utilize different criteria to define core terms.

Learning Tendencies

One prominent theory of cognitive styles is the VARK model, developed by Neil Fleming. According to this model, there are four main learning modalities: Visual, Auditory, Reading/Writing, and Kinesthetic. Visual learners learn best through images, charts, and graphs; auditory learners prefer listening and discussing; reading/writing learners excel with text-based materials; and kinesthetic learners thrive on hands-on activities and movement.

Much of the research on cognitive styles in the learning domain casts a warning to teachers that tend to focus teaching and testing on a particular style that may alienate a percentage of the students. Robert J. Sternberg, Ph.D. wrote that “when your profile of thinking styles is a good match to an environment, you thrive. When it is a bad match, you suffer” ( 1997 ). We would hate for the potential of a young child to be destroyed early just because their leaning style conflicts with the teacher’s teaching and testing style. An early mismatch can lead to a disasters Golem effect , following the child throughout his schooling. Eventually, the child buys into the negative evaluation of their self, and lives up to the low expectations.

This is a difficult task with the growing level of mandatory curriculum and standardized testing. However, we are sacrificing child curiosity and creativity in the balance.

Optimistic-Pessimistic Thinking Styles

Martin Seligman presented theories on cognitive thinking styles in regards to optimism. He referred to his version as explanatory style . he contends that we evaluate information on three continuums. He wrote that “there are three crucial dimensions to your explanatory style: permanence, pervasiveness, and personalization” ( Seligman, 2011 ).

• Permanence refers to the level of permanence we see in personal traits. “If you believe the cause of your mess is permanent—stupidity, lack of talent, ugliness—you will not act to change it” ( 2011 ).
• Pervasiveness refers to how pervasive we perceive and event or behavior. When we see a flaw as something that contributes to all aspects of our lives, then our pervasive style of thinking greatly depresses our lives.
• Personalization refers to attributing external events to personal flaws or causes.

Cognitive Styles and Psychotherapy

A common foundational theory in many psychotherapies, such as cognitive behavioral therapy , is that cognitive thinking styles contribute to, or even cause, psychopathologies. Consequently, therapists target cognitive thinking and emotional patterns , believed to be associated to the pathology. Through various techniques, practitioners hope to change invasive cognitive styles to thinking styles more amenable to psychological wellbeing.

A common technique is cognitive reappraisal . This method takes cognitions and reappraises hurtful or damaging conclusions by finding alternate meanings that soothe rather than provoke discomforting emotions .

Psychology Concepts Associated with Cognitive Styles

Cognitive styles refer to the preferred ways in which individuals process information and solve problems. Several psychological concepts are associated with cognitive styles, including:

• Field Dependence-Independence : This concept distinguishes between individuals who rely more on external visual cues (field-dependent) and those who rely more on internal cues and perceptions (field-independent) 1 .
• Reflectivity-Impulsivity : Reflective individuals tend to take more time to consider information before responding, while impulsive individuals tend to respond quickly without much deliberation.
• Convergent-Divergent Thinking : Convergent thinkers look for a single, correct solution to a problem, whereas divergent thinkers generate multiple, creative solutions.
• Visualizer-Verbalizer Dimension : Some people prefer visual representations of information (visualizers), while others prefer verbal explanations (verbalizers) 1 .
• Holistic-Analytic Style : Holistic thinkers tend to see the big picture and patterns, whereas analytic thinkers focus on details and components.
• Sequential-Global Learning Style : Sequential learners understand information in linear steps, while global learners make sense of information in large jumps, often by first understanding the big picture.

These cognitive styles reflect individual differences in the way people perceive, think, solve problems, learn, and relate to others, influencing their behavior in various contexts.

A Few Words by Psychology Fanatic

Understanding cognitive styles can have significant implications for education and beyond. By recognizing and accommodating diverse learning styles, teachers can create inclusive environments that support the needs of all students. Similarly, in workplaces and teams, acknowledging and leveraging different thinking processes can enhance creativity, problem-solving, and cooperation.

It’s important to note that cognitive styles are not definitive labels, but rather a spectrum of preferences that individuals may exhibit across different situations. Furthermore, these preferences can evolve over time, influenced by experiences and personal development.

In conclusion, recognizing and embracing cognitive diversity is a fundamental aspect of fostering effective learning, problem-solving, and collaboration. By tailoring educational and professional experiences to accommodate various cognitive styles, we can create environments where everyone has the opportunity to thrive.

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References:

Blackburn, I., Jones, S., & Lewin, R. ( 1986 ). Cognitive style in depression. British Journal of Clinical Psychology, 25(4). DOI: 10.1111/j.2044-8260.1986.tb00704.x

Guilford, J. ( 1980 ). Cognitive Styles: What Are They?. Educational and Psychological Measurement, 40(3), 715-735. DOI: 10.1177/001316448004000315

Hallahan, D. ( 1970 ). Cognitive Styles. Journal of Learning Disabilities, 3(1), 4-9. DOI: 10.1177/002221947000300101

Ho, S., & Kozhevnikov, M. ( 2023 ). Cognitive style and creativity: The role of education in shaping cognitive style profiles and creativity of adolescents. British Journal of Educational Psychology, EarlyView. DOI: 10.1111/bjep.12615

Jung, Carl (1923/ 2016 ). Psychological Types. Routledge; 1st edition.

Liedtke, J., & Fromhage, L. ( 2019 ). Modelling the evolution of cognitive styles. BMC Evolutionary Biology, 19(1), 1-10. DOI: 10.1186/s12862-019-1565-2

Seligman, Martin ( 2011 ). Learned Optimism: How to Change Your Mind and Your Life. Vintage; Reprint edition.

Sternberg, Robert J. ( 1997 ). Thinking Styles. Cambridge University Press; First Edition.

Sternberg, R., & Grigorenko, E. ( 1997 ). Are Cognitive Styles Still in Style?. American Psychologist, 52(7), 700-712. DOI: 10.1037/0003-066X.52.7.700

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The Relationships between Cognitive Styles and Creativity: The Role of Field Dependence-Independence on Visual Creative Production

Marco giancola.

1 Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy; [email protected] (M.P.); [email protected] (S.D.)

Massimiliano Palmiero

Laura piccardi.

2 Department of Psychology, Sapienza University of Rome, 00185 Rome, Italy; [email protected]

3 Cognitive and Motor Rehabilitation and Neuroimaging Unit, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy

Simonetta D’Amico

Associated data.

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to subject confidentiality.

Previous studies explored the relationships between field dependent-independent cognitive style (FDI) and creativity, providing misleading and unclear results. The present research explored this problematic interplay through the lens of the Geneplore model, employing a product-oriented task: the Visual Creative Synthesis Task (VCST). The latter requires creating objects belonging to pre-established categories, starting from triads of visual components and consists of two steps: the preinventive phase and the inventive phase. Following the Amabile’s consensual assessment technique, three independent judges evaluated preinventive structures in terms of originality and synthesis whereas inventions were evaluated in terms of originality and appropriateness. The Embedded Figure Test (EFT) was employed in order to measure the individual’s predisposition toward the field dependence or the field independence. Sixty undergraduate college students (31 females) took part in the experiment. Results revealed that field independent individuals outperformed field dependent ones in each of the four VCST scores, showing higher levels of creativity. Results were discussed in light of the better predisposition of field independent individuals in mental imagery, mental manipulation of abstract objects, as well as in using their knowledge during complex tasks that require creativity. Future research directions were also discussed.

1. Introduction

Creativity has been widely recognized as the key to success in contemporary society, affecting art, science, economy, and everyday problem solving [ 1 , 2 ]. Given its relevance in human activities, creativity has received growing attention since the second half of the 20th century, when Guilford proposed the multifactorial Structure of Intellect Model [ 3 ], in which creative thinking encompassed convergent thinking (CT) and divergent thinking (DT). Whereas CT is stated as the ability to converge on prevailing ways of thinking in order to find a single, right, and ready-made solution to a problem that other people could also reach, DT represents a spontaneous and free-flowing form of thought and exemplifies the ability to find many new solutions to an open-ended problem. Additionally, DT is widely recognized as one of the main indicators of people’s creative potential [ 4 ], informing about the likelihood that one can act “outside the box”. Although Guilford’s work has represented a milestone in the literature of creativity, researchers have suggested alternative frameworks [ 5 , 6 ], including the Geneplore model [ 7 ]. The latter represents one of the most influential developments in the tradition of cognitive psychology [ 8 ], and exemplifies a twofold framework in which real-world creative production involves a cyclic motion between generative and explorative phases. The generative phase plays a crucial role in producing pre-inventive structures that are internal prototypes of inventions characterized by different degrees of creative potential and originality [ 9 ]. Whereas this phase requires several cognitive resources, including memory retrieval, mental synthesis, mental transformation, and categorical reduction [ 10 ], the explorative phase, employing cognitive and meta-cognitive processes such as attribute finding, conceptual interpretation, functional inference, and hypothesis testing, drives the examination and practical interpretation of preinventive structures to generate a creative outcome [ 11 ]. According to the product perspective [ 12 , 13 ], two main criteria are necessary to evaluate creative inventions: originality and appropriateness. Whereas the former encompasses the degree of novelty and uncommonness of productions, the latter refers to the relevance and usefulness, exemplifying the individual ability to produce an outcome that fits the needs and constraints of a given situation and is well situated in a given context [ 14 ]. Notably, even though different attributes for evaluating creative inventions can be found in the literature of creative production (e.g., elegance, aesthetics, and surprise), originality and appropriateness are the most accurate criteria, which reflect the full standard definition of creativity [ 15 ].

Given the multifaceted nature of creativity, the impact of cognitive factors on creative accomplishment has been long discussed in the past [ 16 ]. Cognitive style, also known as thinking style, represents a pivotal factor unquestionably related to creativity [ 17 ]. The expression cognitive style refers to how people acquire, organize, and use information [ 18 ]. Cognitive styles are usually conceptualized as bipolar, pervasive, and relatively stable over time, representing a critical dimension of the individual functioning. Although the key role of cognitive styles in human cognition and behavior has been extensively acknowledged, their universal explanatory power has not consistently been demonstrated. However, amongst all cognitive styles, the universal explanatory power of the field dependent-independent cognitive style (FDI) has been widely recognized [ 19 ]. In their seminal work, Witkin and colleagues defined FDI as “the extent to which the person perceives part of a field as discrete from the surrounding field as a whole, rather than embedded in the field” [ 20 ] (pp. 6–7). This cognitive style describes a stable and habitual tendency [ 21 , 22 ] characterized by two different poles: field dependence and field independence. Unlike field dependent subjects (FDs), field independent individuals (FIs) usually show less difficulty in separating information from the surrounding context [ 23 ] and are generally more focused on relevant information, inhibiting attention to irrelevant information coming from the environment [ 24 ]. Although FIs are generally defined as more flexible, open-minded, and capable of breaking down the routine than FDs, empirical evidence on the role of FDI on creativity provided unclear results [ 21 ], probably because of the involvement of specific cognitive processes (e.g., fluid intelligence, inhibition, working memory, and flexibility [ 25 , 26 ]), socio-cultural factors (e.g., Western vs. Eastern [ 27 ]), as well as the discrepancy between the scoring methods (e.g., empirically-based and rater-based scoring methods [ 28 ]), tasks used (e.g., divergent and convergent tasks, real-world creative production tasks [ 29 ]), and sampling bias. For a systematic review on the role of FDI in creativity, see Giancola et al. [ 30 ].

Regarding DT, some studies revealed that FIs outperformed FDs [ 31 , 32 ] in generating ideas, whereas others found non-significant results [ 33 , 34 ]. For instance, Li and colleagues [ 19 ] revealed that FIs outperformed FDs in scientific and social brainstorming tasks in terms of fluency and novelty, confirming previous research [ 32 , 35 ]. In addition, Lei and colleagues [ 17 ] found that field independence was related to fluency and originality but not to flexibility, whereas Niaz and colleagues [ 34 ] found no significant effect of FDI. Regarding CT, some authors stressed that FIs attained significantly higher scores than FDs in convergent measures [ 36 , 37 ], others found no significant effect of FDI [ 38 ]. Finally, for real-world creative production, to the knowledge of the current research, only two studies explored the impact of FDI on creative production. Specifically, Miller’s study [ 39 ] showed that FIs reported higher creativity scores than FDs in the creative collage making task. Similarly, Giancola et al. [ 29 ] found that FIs outperformed FDs in the ability to generate real-world creative objects. Notably, even though some authors found positive correlations amongst FDI and self-rated artistic abilities and artistic competencies [ 40 , 41 ], research on the impact of FDI on creative production remains scattered to date.

Therefore, the present research aimed to shed further light on the issue, employing the logic of the Geneplore model, which involves the combined effect of generation and exploration of ideas to generate real-world visual creative objects. Compared to Giancola et al. [ 29 ], who used a one-step procedure, priming participants with object category names while combining the visual stimuli, in the present study a two-step procedure was used. Specifically, participants were firstly instructed to construct pre-inventive forms combining the visual stimuli, and then interpret them within a specific conceptual category. This led to a better understanding of the role of FDI in the creative process, which encompasses both a visuo-spatial generative phase of undefined ideas and a conceptual/inferential phase of refined ideas. In this vein, the generative phase requires mainly divergent thinking to generate preinventive structures, whereas the explorative phase requires mainly convergent thinking to define and evaluate actual inventions [ 42 ].

Based on previous studies, the first two hypotheses were formulated as follows:

FIs outperform FDs in the preinventive phase because FIs are more divergent, thus more able in generating ideas than FDs [ 31 , 32 ];

FIs outperform FDs in the inventive phase because FIs are also more convergent, thus more analytic in evaluating ideas than FDs [ 36 , 37 ];

FIs do not outperform FDs, regardless of the phase of the creative process, because previous studies found inconsistent relationships between FDI and both divergent and convergent measures of the creative process [ 33 , 34 , 38 ].

2. Materials and Methods

2.1. participants and procedure.

Sixty undergraduate college students attending different courses at The University of L’Aquila (L’Aquila, Italy) participated in the study (mean age = 22.30 ± 3.31; age range = 19–32). Twenty-nine of them were males (48.3%), and thirty-one were females (51.7%). After signing the written informed consent to participate in the study, all participants were asked to complete an anamnesis questionnaire assessing biographical and educational information, general health state, and background or formal achievement in art. No participant reported psychiatric or neurological disorders, drug or alcohol addictions, and no participants declared a background or formal achievement in art. The experimental protocol was administered individually to each participant in a quiet room of the Socio-Cognitive Processes in Life Span Laboratory at The University of L’Aquila (L’Aquila, Italy). The experiment lasted approximately 45 min. The Local Ethics Committee approved this experiment in accordance with the Declaration of Helsinki.

2.2. Measures

2.2.1. assessment of field dependent independent cognitive style.

The Embedded Figure Test—EFT [ 43 ] is usually employed to evaluate the individual’s predisposition toward the field dependent or the field independent cognitive style. It is a paper and pencil test in which participants were requested to find a simple black and white shape within a geometric colored complex figure. The test consists of 24 cards (12 cards with simple shapes and 12 cards with complex figures) 12.9 × 7.7 cm (see Figure 1 ).

An example of an item taken from the Embedded Figure Test (EFT). ( A ) The geometric colored complex figure. ( B ) The simple black and white simple shape. ( C ) The simple shape within the complex figure.

The experimenter presented the complex-colored figure one by one for 15 s and the participant had to describe the figure in a loud voice. Then, the experimenter removed the complex figure and presented the simple one; after 10 s, he took away the simple black and white shape and presented once again the complex-colored figure. After that, participants had to find the simple black and white shape embedded in the complex figure. They were instructed to inform the experimenter as soon as they found the figure and trace its outlines using a pencil. When the participants declared to have found the simple black and white shape within the complex figure, the experimenter annotated the elapsed time (timing). If the response (tracing of the outlines) was wrong, the experimenter continued to take the time until the participant provided the correct response or until 180 s had elapsed. The total time was divided by the number of items (12) in order to compute the average time (RTs) which was used as the measure of the individual’s cognitive style. A shorter time indicated a higher predisposition towards field independence, whereas a longer time indicated a higher predisposition towards field dependence.

2.2.2. Assessment of Creativity

The Visual Creative Synthesis Task—VCST [ 7 ] aimed to create objects belonging to pre-established categories, starting from triads of visual components. The task consists of two steps: the preinventive phase and the inventive phase. Following Palmiero and colleagues [ 44 ], three triads of components and three categories were used. Preinventive phase: participants, after a practical example, were asked to combine the components into a preinventive structure, one for each triad. They could be changed in position, rotation, and size but not in their general structure. The triads of components were presented along with their name on a paper sheet (see Figure 2 ). Participants had 15 s to fix and memorize the components and 2 min to think of the preinventive structure for each triad. Inventive phase: after creating the three preinventive structures, participants were presented with a category name for each of them (1 furniture, 1 weapon, and 1 sport goods) and were requested to think of their inventions. Participants had 3 min to describe the functioning of inventions (1 min for each invention), also providing their title (see Figure 3 ). The description of the objects was also defined in terms of length of responses (i.e., number of words). The order of the triads was randomized.

The three triads of components for the Visual Creative Synthesis Task (VCST): ( 1 ) cube, bracket, cone (sport goods); ( 2 ) parallelepiped, dy-pyramid, horn (furniture); ( 3 ) strip, trapezoid, cylinder (weapons).

An example of creative production based on the triad n.3 made up of one stripe, one trapezoid, and one cylinder. Category: Weapons; Title: Grenade; Description: This cylinder is a grenade. The cylinder contains the explosive, the trapezoid is the trigger mechanism, and the strip controls the whole grenade and has a safety function.

Based on Amabile’s consensual assessment technique [ 45 ], three independent judges, two females and one male (mean age = 25.33 ± 4.50), evaluated preinventive structures and inventions. The judges were three psychology students who attended training on creativity and its assessment for a total of 20 h. During the training sessions, the main models and descriptive frameworks of creativity were explained, including the SOI and the Geneplore Model. In addition, students were shown examples of creative productions already evaluated in the past by judges, and they were trained to evaluate creative productions in terms of creativity. After the training, the evaluation sessions began. Notably, three basic parameters were used: originality and appropriateness, which are the most accurate and used criteria, reflecting the full standard definition of creativity [ 15 ], as well as synthesis, which was used specifically to evaluate the participants’ ability to holistically associate the elements during the preinventive phase of the VCST. Therefore, the preinventive structures were evaluated by each judge along a 5-point Likert-type scale in terms of originality, defined as a form being new and not derived from something else (from 1 = very poor originality to 5 = very high originality) and synthesis, defined as the extent to which components were well assembled (from 1 = very poor synthesis to 5 = very high synthesis). The inter-rater correlation (intra-class correlation coefficient—absolute agreement) were significant for both originality ( α = 0.92; p < 0.01) and synthesis ( α = 0.93; p < 0.01). Inventions were evaluated by each judge along a 5-point Likert-type scale in terms of originality defined as a product being new and not derived from something else (from 1 = very poor originality to 5 = very high originality), and appropriateness, defined as an invention with a practical instead of a hypothetical use (from 1 = very poor appropriateness to 5 = very high appropriateness). The inter-rater correlation (intra-class correlation coefficient—absolute agreement) was significant for both originality ( α = 0.94; p < 0.01) and appropriateness ( α = 0.96; p < 0.01).

Statistical analyses were performed using IBM SPSS Statistics version 24 for Windows (IBM Corporation, Armonk, NY, USA). Data were tested for normality and all measures were normally distributed (Kolmogorov–Smirnov Test: Z VCST—Pre—Originality = 0.75, ns; Z VCST—Pre—Synthesis = 0.73, ns; Z VCST—Inv—Originality = 0.76, ns; Z VCST—Inv—Appropriateness = 0.31, ns; Z VCST — Description length = 0.84, ns) except for the EFT (RTs): (Kolmogorov–Smirnov Test: Z EFT (RTs) = 0.02, sig). Additionally, the analysis of the distribution of the EFT using the interquartile range method (IQR) revealed that only four FDs were ‘far out’ from the mean. This suggests that in the present sample data were mostly skewed toward field dependence (e.g., high RTs) rather than field independence (e.g., low RTs). Table 1 reported descriptive statistics divided for group (field dependence vs. field independence).

Descriptive statistics divided by FDI groups.

Note. FDs = Field Dependents; FIs = Field Independents; VCST = Visual Creative Synthesis Task; Pre = Preinventive phase; Inv = Inventive phase.

Correlational analysis was computed using Spearman’s Rho (see Table 2 ). Results revealed that the EFT (RTs) was negatively correlated with the VCST—Preinventive phase Originality (r = −0.50, p < 0.01), VCST—Preinventive phase Synthesis (r = −0.48, p < 0.01), VCST—Inventive phase Originality (r = −0.51, p < 0.01) VCST—Inventive phase Appropriateness (r = −0.52, p < 0.01), and VCST—Description length (r = −0.30, p < 0.05). Notably, VCST—Description length positively correlated to VCST—Inventive phase Originality (r = 0.31, p < 0.05) VCST—Inventive phase Appropriateness (r = 0.29, p < 0.05). In addition, gender also correlated negatively to EFT (RTs) (r = −0.44, p < 0.01) and positively to VCST—Preinventive phase Originality (r = 0.34, p < 0.01), VCST—Preinventive phase Synthesis (r = 0.35, p < 0.01), VCST—Inventive phase Originality (r = 0.27, p < 0.05) and VCST—Inventive phase Appropriateness (r = 0.29, p < 0.05), meaning that males showed higher field dependence and creativity scores than females.

Means, standard deviations, and Spearman’s Rho inter-correlations.

Note. N = 60, gender was dummy coded (F = 0, M = 1) * p < 0.05 (two tailed) ** p < 0.01 (two tailed) EFT = Embedded Figure Test RTs = Response Times: VCST = Visual Creative Synthesis Task Pre = Preinventive phase Inv = Inventive phase.

In order to obtain a more accurate picture of the relationship between EFT-RTs and the creativity scores, possible gender confounding effects were checked using the Spearman’s Rho partial correlations (see Table 3 ). The latter showed that controlling for gender had a little effect on the strength of the relationships between EFT-RTs and the creativity scores, and the significance level was not affected at all. Therefore, the variable ‘gender’ was not further considered when analyzing the comparison between FDs and FIs in terms of creativity.

Spearman’s Rho partial correlations.

Note. N = 60, * p < 0.05 (two tailed) ** p < 0.01 (two tailed) EFT = Embedded Figure Test RTs = Response Times: VCST = Visual Creative Synthesis Task Pre = Preinventive phase Inv = Inventive phase.

Following Tascón and colleagues [ 46 ], since the EFT does not have a scale to divide FIs and FDs and taking into consideration that the individual’s predisposition toward field dependence and field independence is along a continuum, the median-split technique was applied. This method has also been used not only in previous studies on the interplay between FDI and creativity [ 36 , 38 , 47 ] but also in other research areas, including perception [ 48 ], spatial cognition [ 46 ], and problem solving [ 49 ]. Participants were divided by the median-split of the EFT score (average solution times). Therefore, subjects with lower scores than the median (31.23) were classified as FIs ( n = 30), whereas participants with higher scores than the median were classified as FDs ( n = 30).

Four Mann–Whitney U tests were performed in order to measure the differences between FDs and FIs in both preinventive and inventive phases. The significant level of the Mann–Whitney U tests was set as 5 % (α = 0.05). Results revealed that there are significant differences between FDs and FIs in terms of VCST Preinventive Originality (FDs rank = 23.83, FIs rank = 37.17, U = 250.000, p = 0.003), VCST Preinventive Synthesis (FDs rank = 23.47, FIs rank = 37.53, U = 239.000, p = 0.002), VCST Inventive Originality (FDs rank = 23.13, FIs rank = 37.87, U = 229.000, p = 0.001), and VCST Inventive Appropriateness (FDs rank = 23.35, FIs rank = 37.65, U = 235.000, p = 0.001).

4. Discussion

Previous research on the relationships between FDI and creativity revealed unclear results, demonstrating a lack of consensus amongst researchers [ 21 ]: mixed findings have been found taking into account creative thinking in terms of convergent [ 37 , 38 ] and divergent productions [ 32 , 34 ], whereas little work has been done on creative production [ 39 ]. Given this scenario, the current research was aimed at investigating the extent to which the individual’s predisposition towards field dependence and field independence affects creativity. To this aim, we measured FDI using the Embedded Figure Test, whereas creativity was evaluated by the VCST according to the logic of the Geneplore model. The VCST is a product-oriented task, which relies on preinventive and inventive phases. For the first time in this research field, we adopted such a twofold model, assuming that creative production requires both ideas generation and ideas evaluation, underpinned by divergent and convergent mental processes, respectively [ 4 ]. Specifically, the VCST relies on a generative process in which individuals produce preinventive structures characterized by different degrees of creative potential and on an exploratory process in which such structures are evaluated by considering their potential fruition analytically [ 9 ].

Regarding the preinventive phase, results revealed that FI negatively correlated to originality and synthesis. This result was also confirmed by the ANOVA, showing that FIs provided significantly higher scores in originality and synthesis than FDs, meaning that the faster were participants in identifying the simple shape embedded in the complex figure (field independence), the more original and well-assembled were their preinventive structures. Thus, the H1 was confirmed. Given the nature of the task used, it is not surprising that mental imagery plays a key role during the preinventive phase of VCST: indeed, the task requires mentally transforming, combining, and synthesizing visual components in order to generate preinventive structures, that is, mental prototypes of inventions. The pivotal role of mental imagery in creative tasks such as the VCST has been widely recognized in the past [ 10 , 50 , 51 , 52 ]. More specifically, spatial imagery and mental manipulation of spatial forms seem to be crucial in tasks involving objects’ construction [ 53 ], including creative inventions. Indeed, the ability to mentally manipulate shapes was found positively related with the originality score of preinventive structures [ 54 ] and the ability to generate shapes that were well-assembled and synthesized [ 10 , 55 ]. The role of spatial manipulation in creative tasks is also consistent with those studies using the think-aloud method in order to reveal mental processes actively involved in creativity. For instance, Palmiero and Piccardi’s study [ 56 ] revealed that spatial thoughts—containing spatial information of size and rotation—generated during the preinventive phase positively predicted the originality of productions during the invention phase. Although we did not detect mental imagery directly, the assumptions reported above could represent a relevant point to explain our results. Indeed, FIs seem to be more skilled than FDs in spatial abilities implying mental imagery. Specifically, FIs showed higher performance than FDs in tasks required to process different objects’ features such as shape and orientation [ 57 ] and in tasks tapping visual-spatial information [ 58 ]. For instance, Boccia and colleagues [ 59 ], in a sample of 50 young adults, found that FIs outperformed FDs in mental rotation test and Li and colleagues [ 60 ] revealed similar results in 2D and 3D map mental rotation, underlining that regardless of map dimensionality, as the degree of the image rotation increased, the accuracy of the FIs’ performance increased. Although the more flexible mental imagery and the better predisposition to use visual stimuli of FIs could represent a pivotal factor in this phase of the Geneplore cycle, undoubtedly, different mechanisms could affect it, and further investigations are needed.

Regarding the inventive phase, results revealed that FDI negatively correlated with originality and appropriateness. This result was also confirmed by the ANOVA, showing that FIs provided significantly higher scores in originality and appropriateness than FDs, meaning that the faster participants were in identifying the simple shape embedded in the complex figure (field independence), the more creative (original and appropriate) were their inventions. Results confirmed H2 and align with previous studies on real-world creative production [ 29 , 39 ] as well as research on both divergent thinking [ 17 , 19 , 32 ] and convergent thinking [ 36 , 37 ]. Two main explanations can explain the better performance of FIs in the VCST than FDs. First, the assumption of the pivotal role of mental imagery in the preinventive phase can be also extended to the inventive phase. For instance, Roskos-ewoldsen and colleagues [ 54 ], in a sample composed of 41 young and 41 older adults, found a positive relationship between the Paper Folding Test and originality score of productions in the Creative Invention Task. Similar results were also found by Palmiero and colleagues’ study [ 51 ], in which the individual vividness of mental imagery was positively related to the practicality score of the invention in the Mental Synthesis Task. Therefore, the nature of the VCST used in this study and the better predisposition of FIs than FDs in mentally manipulating spatial shapes could represent a possible explanation of our results during the inventive phase. Second, it has been found that the better predisposition of FIs in using their own knowledge and in extracting it from memory, especially in complex tasks in which the solution is unclear, positively affects creative performance [ 19 ]. This assumption is consistent with the two-step form of the VCST used in this research. Indeed, unlike the one-step form [ 29 , 51 ] in which the category of creative inventions is specified before combining the visual components, the category is specified in the two-step form only after the assembly of components. This makes the creative process more complex because, at least, in the combination phase, the goal of creative production is not defined, and participants have to adapt what they have previously assembled to the category provided by the task. In other words, during the two-step form of VCST, participants have to reorganize their prior knowledge in order to generate the creative product. Given that FIs, compared with FDs, have a better capacity to extract their own knowledge, this individual predisposition could be helpful to them in reorganizing and updating the structure previously generated in order to generate the creative invention.

Taken together these results showed that field independence positively affected both phases (pre-inventive and inventive) of the creative process. Although one would be expected that FDI differently impacted on the two phases, it is important to acknowledge that, on the one hand, the preinventive phase relies on field independence based on divergent thinking [ 31 , 32 ]; and, on the other hand, the inventive phase relies on field independence based on convergent analytic thinking [ 36 , 37 ]. That is, FIs can encompass the creative process given their ability in shifting between generative/divergent and explorative/convergent phases. This means that real-world creative production based on the Geneplore Model might be fully supported by the field independent cognitive style, given that the latter loads on both the preinventive and inventive phases. Although this interpretation is intriguing, it should be supported by further scientific evidence.

Notably, the VCST—Description length correlated positively to the originality and appropriateness of the inventive scores, meaning that higher the number of words used to describe the objects the higher the originality and appropriateness. This result suggested that the description length may represent a characteristic of creativity [ 61 ], especially of the inventive phase of the creative process. Although in this study we did not used a written-narrative task to measure creativity, this result confirmed that when it is necessary to make causal inferences and category reductions, the number of words, reflecting the complexity of the construct [ 62 ], can play a key role. Of course, this idea needs also to be verified by future studies.

5. Conclusions

To conclude, this research provides empirical evidence on a problematic and complex relationship involving FDI and creativity, and results seem to support the hypothesis that FIs outperform FDs in creative performance. Despite these findings, the current research shows a critical limit concerning the small sample size. This suggests that studies with more subjects should be carried out in order to reach more reliable conclusions. In addition, the field dependent and field independent groups are unbalanced in terms of gender. However, the study provides interesting future directions. First, although the Geneplore model represents a domain-general framework [ 11 ], the research focused only on visual real-world creative production. Further studies should encompass different domains of creativity (e.g., verbal and motor domains) in order to define an exhaustive picture of the impact of FDI on creativity. Yet, even though the VCST is not a time-based creativity task, future studies could assess the times necessary to carry on the creative process and related it to the attributes of creativity, such as originality and appropriateness. Then, in the present research, we stressed the pivotal role of mental imagery during the generation and evaluation of preinventive structure as well as during the invention phase. Future investigations could further explore the relationships amongst FDI, creativity, and mental imagery, considering how the vividness of mental images may spur or inhibit this cognitive style’s influence on creative performance.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, M.G. and M.P.; methodology, M.G. and M.P.; formal analysis, M.G. and M.P.; investigation, M.G. and M.P.; resources, L.P. and S.D.; data curation, M.G.; writing—original draft preparation, M.G.; writing—review and editing, M.G., M.P., L.P. and S.D.; supervision, S.D. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethic Committee of the University of L’Aquila.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The Power of Cognitive Diversity in Team Problem-Solving

In the realm of team problem-solving, cognitive diversity holds significant potential. This concept encapsulates the presence of distinct thinking styles, perspectives, and approaches within a team. By amalgamating various skills, knowledge, and experiences, teams with cognitive diversity can effectively solve problems at a faster pace. Moreover, cognitive diversity fosters creativity, innovation, critical thinking, and a broader range of ideas and perspectives. This essay delves into the importance of cognitive diversity in team problem-solving, the benefits it brings, the challenges it poses, and strategies to cultivate it within teams.

Table of Contents

Key Takeaways

• Teams with cognitive diversity solve problems faster.
• Cognitive diversity enhances creativity and innovation.
• Cognitive diversity helps teams consider multiple viewpoints and find better solutions.
• Strategies to foster cognitive diversity in teams include actively seeking out diverse perspectives, creating a culture of psychological safety, implementing inclusive hiring practices, and providing training on unconscious biases and cognitive diversity.

Importance of Cognitive Diversity in Team Problem-Solving

The importance of cognitive diversity in team problem-solving lies in its ability to stimulate critical thinking, promote a wider range of ideas and perspectives, and encourage creative solutions and out-of-the-box thinking. Teams with cognitive diversity are more adept at enhancing problem-solving speed through their varied thinking styles, approaches, and experiences. By incorporating diverse perspectives, teams can better understand the concept at hand and consider multiple viewpoints, leading to more comprehensive and effective problem-solving outcomes. Different cognitive styles complement each other, filling in knowledge gaps and avoiding groupthink and biases. This diversity of thought not only enhances problem-solving abilities but also fosters innovation and creativity. Therefore, recognizing and valuing cognitive diversity in teams is crucial for organizations seeking to optimize their problem-solving processes.

Impact of Diversity on Team Composition

Increased diversity of gender, ethnicity, and age in executive teams has been observed in efforts to achieve a more representative workforce. These efforts have shown progress in increasing the representation of diverse individuals in team compositions. The presence of diversity in executive teams and classrooms is becoming more visible, indicating a positive shift in achieving progress towards a more inclusive environment. However, it is important to note that more work needs to be done to further enhance diversity in team compositions. The benefits of increased representation include better decision-making and problem-solving, as diverse perspectives bring a wider range of ideas and viewpoints to the table. By actively seeking out diverse talent, implementing inclusive hiring practices, and fostering a culture that embraces different viewpoints, organizations can continue to make strides in achieving greater cognitive diversity in teams.

Benefits of Cognitive Diversity in Problem-Solving

An enhanced range of perspectives and ideas are fostered through the inclusion of diverse thinking styles, leading to more creative and innovative solutions. The benefits of cognitive diversity in problem-solving can be summarized as follows:

Promoting collaboration: Cognitive diversity stimulates critical thinking and analysis, encouraging individuals to work together and leverage their unique perspectives. This collaborative environment allows for the exploration of different ideas and the development of more comprehensive solutions.

Embracing different perspectives: Cognitive diversity brings together individuals with varying backgrounds, experiences, and knowledge. This diversity of perspectives helps to avoid groupthink and biases, enabling teams to consider multiple viewpoints and find more effective solutions to complex problems.

Encouraging creative solutions: Different cognitive styles complement each other and fill in knowledge gaps. This diversity of thinking encourages out-of-the-box thinking and sparks innovative ideas, leading to creative problem-solving approaches.

Challenges and Barriers to Achieving Cognitive Diversity

Challenges and barriers to achieving cognitive diversity include unconscious biases, limited access to diverse talent pools, and resistance to change. Unconscious biases in team dynamics can hinder the inclusion of diverse perspectives, as individuals may unknowingly favor those who think and behave in a similar manner. These biases can perpetuate homogeneity and make it difficult for different perspectives to be heard and valued. Limited access to diverse talent pools further exacerbates the issue, as organizations may struggle to attract and retain individuals with diverse backgrounds and ways of thinking. Additionally, resistance to change and a reluctance to embrace different viewpoints can impede efforts to foster cognitive diversity. Overcoming these challenges requires raising awareness about unconscious biases, creating inclusive hiring practices, and fostering a culture that values and embraces cognitive diversity.

Strategies to Foster Cognitive Diversity in Teams

One approach to promoting cognitive diversity within teams is to actively seek out and incorporate a wide range of perspectives and opinions. This can be achieved by creating diverse teams that bring together individuals with different backgrounds, experiences, and ways of thinking. By creating such teams, organizations can promote open dialogue and foster an environment where diverse perspectives are valued and encouraged.

Strategies to foster cognitive diversity in teams include:

Actively seeking out diverse perspectives and opinions: This involves actively recruiting individuals from diverse backgrounds and ensuring that their voices are heard and valued within the team.

Promoting open dialogue: Creating an environment where team members feel comfortable expressing their opinions and engaging in constructive discussions is crucial for promoting cognitive diversity.

Implementing inclusive hiring practices: By adopting inclusive hiring practices, organizations can attract a diverse pool of talent, ensuring that different perspectives and experiences are represented within the team.

Enhancing Problem-Solving Speed With Cognitive Diversity

Enhancing the speed of problem-solving can be achieved through the incorporation of diverse perspectives and opinions within teams. Cognitive diversity, which refers to differences in thinking styles, perspectives, and approaches, plays a crucial role in increasing efficiency and enhancing collaboration. By bringing together a variety of skills, knowledge, and experiences, cognitive diversity stimulates critical thinking and analysis, promoting a wider range of ideas and perspectives. Different cognitive styles complement each other and fill in knowledge gaps, leading to better decision-making and problem-solving. Moreover, cognitive diversity helps to avoid groupthink and biases, encouraging creative solutions and out-of-the-box thinking. To foster cognitive diversity in teams, organizations should actively seek out diverse perspectives, create a culture of psychological safety that encourages open dialogue, implement inclusive hiring practices, provide training on unconscious biases and cognitive diversity, and foster collaboration and teamwork to leverage its benefits.

Understanding the Concept of Cognitive Diversity

Different perspectives, thinking styles, and approaches contribute to cognitive diversity within teams. Cognitive diversity refers to the differences in how individuals perceive, think, and solve problems. It encompasses a range of characteristics such as educational background, professional experience, cultural upbringing, and personality traits. Here are some examples of cognitive diversity:

Analytical Thinkers: These individuals excel at breaking down complex problems into smaller, manageable components. They rely on data and evidence to make decisions and tend to be detail-oriented.

Creative Thinkers: These individuals thrive in generating new and innovative ideas. They approach problems from unconventional angles, often thinking outside the box and challenging the status quo.

Collaborative Thinkers: These individuals excel at building relationships and working in teams. They value cooperation and seek to integrate diverse perspectives to find optimal solutions.

The benefits of cognitive diversity in team problem-solving are numerous. It enhances collective intelligence by bringing together a variety of skills, knowledge, and experiences. It enables teams to consider multiple viewpoints, leading to more comprehensive and well-rounded solutions. Cognitive diversity stimulates critical thinking and analysis, promoting a wider range of ideas and perspectives. By complementing each other’s cognitive styles and filling in knowledge gaps, teams can avoid groupthink and biases. This encourages creative solutions and out-of-the-box thinking, ultimately leading to more innovative outcomes.

Leveraging Cognitive Diversity for Creativity and Innovation

By leveraging the varied perspectives and thinking styles within a team, opportunities for creativity and innovation can be maximized. Fostering collaboration and embracing different perspectives are key strategies in leveraging cognitive diversity. Collaboration allows team members to share ideas, challenge assumptions, and build on each other’s strengths. Embracing different perspectives encourages individuals to bring their unique insights and experiences to the table. This diversity of thought stimulates creativity and innovation by providing a wider range of ideas and approaches to problem-solving. When team members with different perspectives collaborate effectively, they can overcome biases and avoid groupthink, leading to more robust and innovative solutions. This approach fosters an environment that values diversity and creates space for creative thinking, ultimately enhancing the team’s ability to generate innovative ideas and solutions.

Overcoming Unconscious Biases for Inclusive Problem-Solving

Unconscious biases pose a challenge to achieving inclusion and diverse perspectives in the problem-solving process. To overcome this challenge, it is essential to raise awareness of unconscious biases and implement strategies that promote a more inclusive problem-solving environment.

Unconscious bias awareness: Organizations should provide training and education on unconscious biases to help individuals recognize and mitigate their biases. This awareness can help create a more inclusive and equitable problem-solving process.

Diversity talent pools: To overcome unconscious biases, organizations should actively seek out diverse talent pools. By expanding recruitment efforts and implementing inclusive hiring practices, organizations can ensure a broader range of perspectives and experiences are included in the problem-solving process.

Inclusive problem-solving practices: Encouraging open dialogue and creating a culture of psychological safety can foster an environment where diverse perspectives are valued and included. By leveraging the power of cognitive diversity, teams can overcome unconscious biases and achieve more effective and innovative problem-solving outcomes.

Building a Culture of Psychological Safety for Cognitive Diversity

Creating an inclusive and supportive environment that fosters open dialogue and psychological safety is crucial for promoting a culture that values and embraces diverse perspectives and thinking styles. This environment encourages individuals to express their unique viewpoints and ideas without fear of judgement or reprisal. It allows for the exploration of different perspectives, which can lead to more innovative and effective problem-solving. By encouraging diverse perspectives, teams can tap into a wider range of knowledge, experiences, and approaches, ultimately enhancing their ability to find creative solutions. Furthermore, creating an inclusive environment not only promotes cognitive diversity but also helps to break down barriers and biases that may hinder the inclusion of diverse perspectives. Therefore, organizations should prioritize the development of a culture of psychological safety that encourages open dialogue and values diverse perspectives.

The Role of Inclusive Hiring Practices in Promoting Cognitive Diversity

Implementing inclusive hiring practices is essential for fostering cognitive diversity within teams and organizations. By actively seeking out diverse talent pools, organizations can tap into a wider range of perspectives, experiences, and thinking styles. This can lead to enhanced problem-solving abilities and increased innovation within teams. Inclusive hiring practices can be implemented by:

Broadening recruitment channels: Organizations can reach out to a diverse range of candidates by utilizing various platforms and networks, including those specifically focused on underrepresented groups.

Removing bias from the hiring process: Implementing structured interviews and blind resume reviews can help reduce unconscious biases and ensure fair evaluation of candidates based on their qualifications and capabilities.

Promoting diversity and inclusion from within: Organizations can create a culture that values and celebrates diversity, ensuring that employees from all backgrounds feel welcome and supported.

Frequently Asked Questions

How does cognitive diversity enhance problem-solving speed in teams.

Cognitive diversity enhances problem-solving speed in teams by enhancing collaboration and decision-making efficiency. It allows for a wider range of ideas and perspectives, stimulates critical thinking, and encourages creative solutions, thus avoiding groupthink and biases.

What Are Some Examples of Unconscious Biases That Can Hinder the Inclusion of Diverse Perspectives?

Unconscious biases, such as affinity biases and confirmation biases, can hinder the inclusion of diverse perspectives in teams. Overcoming resistance to cognitive diversity requires awareness, education, and creating a culture that values and embraces different viewpoints.

How Can Organizations Overcome Resistance to Change and Encourage the Embrace of Different Viewpoints?

To overcome resistance to change and encourage the embrace of different viewpoints, organizations can implement strategies such as creating a culture of openness, providing education on the value of diversity, and fostering collaboration to leverage the benefits of cognitive diversity.

What Are Some Strategies for Actively Seeking Out Diverse Perspectives and Opinions?

Strategies for seeking diverse perspectives and fostering inclusion include actively soliciting input from individuals with different backgrounds and experiences, creating a safe environment for open dialogue, implementing inclusive hiring practices, providing education on unconscious biases, and promoting collaboration.

How Can Organizations Create a Culture of Psychological Safety That Encourages Open Dialogue and Diverse Thinking?

To create a culture of psychological safety that encourages open dialogue and diverse thinking, organizations can build trust by promoting transparency, actively listening to employee perspectives, and valuing diverse contributions. Additionally, empowering individuals through training and recognition can motivate them to share their unique viewpoints openly.

Lifelong learning of cognitive styles for physical problem-solving: The effect of embodied experience

• Brief Report
• Open access
• Published: 04 December 2023
• Volume 31 , pages 1364–1375, ( 2024 )

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• Kelsey R. Allen   ORCID: orcid.org/0000-0002-8461-0652 1   na1 ,
• Kevin A. Smith 1   na1 ,
• Laura-Ashleigh Bird 2 ,
• Joshua B. Tenenbaum 1 ,
• Tamar R. Makin 3 , 4   na1 &
• Dorothy Cowie 2   na1  

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‘Embodied cognition’ suggests that our bodily experiences broadly shape our cognitive capabilities. We study how embodied experience affects the abstract physical problem-solving styles people use in a virtual task where embodiment does not affect action capabilities. We compare how groups with different embodied experience – 25 children and 35 adults with congenital limb differences versus 45 children and 40 adults born with two hands – perform this task, and find that while there is no difference in overall competence, the groups use different cognitive styles to find solutions. People born with limb differences think more before acting but take fewer attempts to reach solutions. Conversely, development affects the particular actions children use, as well as their persistence with their current strategy. Our findings suggest that while development alters action choices and persistence, differences in embodied experience drive changes in the acquisition of cognitive styles for balancing acting with thinking.

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Introduction

Everyday experience is both constrained and enabled by the bodies we inhabit. Taller people can reach further, while people with two fully functioning hands can manipulate multiple objects at the same time. ‘Embodied cognition’ (Wilson, 2002 ) suggests that such constraints play a fundamental role in shaping our cognitive and perceptual experiences. Many versions of embodiment theory suggest that these effects reach even further, into how we reason about those experiences. Supporting this view, researchers have shown that when individuals’ bodies or skills are altered, e.g., through temporary training or by being born with limb differences, this can change their perceptual capacities (Aglioti et al., 2008 ; Hagura et al., 2017 ), spatial cognition (Makin et al., 2010 ), body representation (Maimon-Mor et al., 2020 ), or motor skills (Maimon-Mor et al., 2021 ). Here we ask if these effects of embodiment can be broader, by testing whether differences in embodied experience (through limb differences) affect the ways that people think about acting in the world, even when their capacities for action are made equal.

Individuals with limb differences must engage in independent motor problem-solving for many everyday behaviors, such as opening a jar. These experiences may change their cognitive styles for motor tasks in general. For example, with two hands, opening a jar can be accomplished by using one hand to stabilize the jar while the other one twists the lid ( A ). With a single hand ( B ), opening a jar can be accomplished by using one’s arm and torso to stabilize the jar. The Virtual Tools game ( C ) equalizes action possibilities and costs for individuals with different types of limbs by creating a virtual action space. (i) The aim is to move the red ball into the green goal. A participant selects a tool from three options (shapes in colored boxes) and places it in the scene (ii). Once placed, physics is “turned on” and objects can fall under gravity or collide with each other (iii); the blue and red lines represent the observed motion trajectories for the tool and the ball, respectively

Prior studies have rarely addressed how a lifetime of embodied experience affects the use of “cognitive styles”: the individual differences in how people allocate cognitive resources to thinking about and acting in the world (Messick, 1976 ; Kozhevnikov, 2007 ). Through short-term direct manipulations of people’s bodies or accessible actions, researchers have shown that people are sensitive to action costs – the amount of effort required to perform actions – for both motor planning (Izawa et al., 2008 ) and motor behaviors (Prévost et al., 2010 ). Priming people with different action costs in a perceptual decision-making task can even affect their decisions in a setting where action costs no longer apply (Hagura et al., 2017 ), suggesting that action costs can generalize beyond the immediate task. However, these studies transpire on the scale of minutes, and typically demonstrate behavioral effects where costs are manipulated within a task but do not show that long-term and generalized action costs are learned from embodied experience.

To study the effect of embodied experience over longer time-scales, researchers have investigated the perceptual and motor capabilities of individuals born with limb differences. However, the tasks used to study these capabilities often require judgments related to absent body parts, and therefore differences in behavior might be driven by differences in sensorimotor experience or available information. For example, while people with congenital limb differences are slower to judge whether a picture is of a left or right hand (Maimon-Mor et al. , 2020 ; though cf. Vannuscorps and Caramazza , 2016 ; Vannuscorps et al. , 2012 ), they lack first-person experience of that hand.

Here we test the hypothesis that growing up with a different body may affect the everyday cognitive styles individuals use to solve problems in their environments, even when the capabilities tested are divorced from particular bodily differences. For example, if people with congenital limb differences have learned that actions are in general more costly – perhaps as a result of difficulties using artifacts designed for people with two hands (see Fig.  1 ) – we might expect that they will differ in how they approach physical problems generally, even when action costs are equated. Such cognitive styles for action have been observed previously on shorter time-scales: e.g., individuals adapt their motor plans to their own levels of sensorimotor uncertainty and variability (Harris and Wolpert, 1998 ; Gallivan et al., 2018 ; Körding and Wolpert, 2004 ), including becoming more persistent (Leonard et al., 2017 ), or spending more time thinking before acting (Dasgupta et al., 2018 ). The differences in cognitive styles might be expected to emerge early in infancy, when experience begins driving motor skill acquisition (Adolph et al., 2018 ) but might also develop throughout childhood alongside more precise motor planning, control and tool use (Berard et al., 2006 ; Chicoine et al., 1992 ; Adalbjornsson et al., 2008 ).

To test the influence of embodied experience on the learning of cognitive styles, we studied behavior in a virtual physical problem-solving task where all participants had equal capabilities to interact with the world. This “Virtual Tools game” (Allen et al., 2020 ) requires people to use virtual objects as tools to solve a physical problem (e.g., getting the red ball into the green goal, Fig.  1 C) using a single limb to control a cursor, thus equating action costs. Allen et al. ( 2020 ) provide a set of performance metrics for this task that measure the cognitive styles participants use.

We chose participant groups to represent a diverse range of embodied experience: children (5 to 10-year-olds) and adults born with limb differences, and age-matched children and adults born with two hands. Children have less embodied experience than adults, while individuals with limb differences have dramatically different kinds of experience. By using a virtual task with simple controls, we equate manipulation capabilities and instead study how embodied experience affects the cognitive styles that support action planning and reasoning more generally.

We tested whether cognitive styles are affected by life experience, as indexed by age (children versus adults) and by limb differences. We first predicted that those having to devise unique solutions to everyday physical problems due to growing up with a limb difference might use different, perhaps even more efficient, ways of solving the virtual puzzles. To assess this, we considered the key outcome measures of attempt type, thinking time, attempts to solution, time to solution, and solution rate introduced by Allen et al. ( 2020 ). Second, given that tool use capabilities develop throughout childhood (Beck et al., 2011 ; Keen, 2011 ), we expected solution rates to improve with age. Finally, we tested whether differences in cognitive styles between those with and without limb differences would emerge early because compensatory behavior evolves early, or whether differences might grow with development as motor and cognitive skills develop. Overall, we found that participants with limb differences do use a different set of cognitive styles than those without – spending more time thinking while interacting less with the world. While performance does improve with age, we did not find evidence that the thinking/acting difference between participants with and without limb differences changes over development.

Participants

We recruited a total of 145 participants across four groups: 40 adults without limb differences (Adult-NLD), 35 adults with limb differences (Adult-LD), 45 children with no limb differences (Child-NLD), and 25 children with limb differences (Child-LD). LD and NLD participants were well matched for age (Child-LD mean: 7.91 years old, sd: 1.84, range 5.08–10.72; Child-NLD mean: 7.94 years old, sd: 1.74, range 5.02–10.56; Adult-LD mean: 40.7 years old, sd: 15.5, range 19–76; Adult-NLD mean: 41.2 years old, sd: 15.2, range 18–70). We extensively liaised with the limb-difference community, including through existing volunteer databases and two UK charities who support children with a limb difference and their families. We included participants with congenital upper limb anomalies, as summarized in Tables S2 and S3 in the Supplemental Material. Information on limb differences was self-reported but verified for a subset of participants (88% and 55% in children and adults, respectively). We recruited children and adults without limb differences to match the education level and age of the special population over the same period. We recruited a larger sample of children without limb differences to give us a greater understanding of the range of typical performance in a two-handed population. Two-handed children were recruited through an existing university volunteer families database and affiliated Facebook page. The experiment was approved for adults under protocols approved by UCL (9937/001), and all adults provided informed consent. The experiment was approved for children under a protocol approved by the ethics committee at Durham University (PSYCH-2019-08-30T10_08_45-mnvj24), and informed consent was provided by the legal guardian.

As with Allen et al. ( 2020 ), the experiment was run online on participants’ personal computers at home. All participants were provided with an identifying code used to link their performance with individual information (e.g., specific limb differences). The experiment progressed through two stages: motor pre-test, and Virtual Tools game. We also collected several additional demographic and clinical details (see below Table 1 ).

All participants were given the same experiment, with only three exceptions that differed between children and adults: (1) children received simplified instructions for all stages, (2) adults played one additional Virtual Tools level that we removed from the children’s experiment due to excessive challenge (see Section  S3 in the Supplemental Material), and (3) adults were given a more extensive questionnaire that included additional questions about the strategies they had used and video games they had played before.

The 14 levels of the Virtual Tools game (Allen et al., 2020 ) that participants played. These cover a wide variety of physical action concepts including “balancing,” “launching,” “catapulting,” “supporting,” and “tipping.” To play the game, please see https://sites.google.com/view/virtualtoolsgame

Motor pre-test

The motor pre-test was used to measure participants’ ability to control the cursor. Each trial began with a star centered in a 600x600  px area on the screen. Once the star was clicked, a 10- px radius circle appeared in a random position either 150  px or 250  px from the center. Participants were instructed to click on the circle as quickly and accurately as possible. Participants completed ten motor test trials (five at each distance from the center).

On each trial we measured (a) reaction time, and (b) the distance (in px ) between the center of the circle and the cursor click location. As a measure of participants’ basic motor accuracy, we took the median of both of those measures across all ten trials; we used the median to avoid skew from outlier trials, and found in pilot testing that this was a relatively stable measurement.

Virtual Tools game

On each level of the Virtual Tools game, participants were presented with a scene and three “tools” (see Fig.  1 C-i), along with a goal condition (e.g., “get the red object into the green goal area”). Participants could accomplish this goal by clicking on a tool and then an unobstructed part of the game area to place that tool (Fig.  1 C-ii). They could choose the location of the tool but its orientation was fixed to how it was displayed on the screen. As soon as the tool was placed, physics was “turned on” and objects could fall under gravity or collide with each other depending on the specific tool placement made (Fig.  1 C-iii). If the goal was not accomplished, participants could press a button to reset the scene to its initial state. Participants could attempt to solve the level as many times as they liked but were limited to a single tool placement for each attempt. Participants could move onto the next level once they had accomplished the goal, or after  60  s had passed.

Following Allen et al. ( 2020 ), participants were initially given instructions about how the game functioned, including the difference between static (black) and moving (blue/red) objects, goal areas, and how to place tools and reset the level. For familiarization with the interface and physics, participants were given one introductory level that required them to place tools at least three times without a goal, followed by two simple levels that they were required to solve that were not analyzed. For adults, this process was identical to that of Allen et al. ( 2020 ); children received simplified instructions (see Fig.  S1 in the Supplemental Material).

In the main task, participants were asked to solve 14 different levels, each with a different set of three tools, designed to probe knowledge of diverse physical principles (e.g., support, collisions, tipping; see Fig.  2 ). The solutions to each level are determined by the physics of the game, rather than being decided by the experimenters. Solution (“truth”) maps are provided in the supplement (Fig.  S2 ). As in Allen et al. ( 2020 ), on each level, we recorded all attempts from each participant – defined as the tool chosen and where it was placed; the time elapsed between the start of the level and the time of the attempt; and whether the level was solved. Examples of participants’ play in different levels is shown in Fig.  3 .

Examples of different participant trajectories through different levels. Each panel is an individual attempt by a participant, labeled by the attempt number. The starting positions of objects are shown as more transparent, while their final positions after physics is “turned on” are shown as opaque. Available tools for the levels depicted are shown on the right. ( A ) Participants with limb differences tend to spend more time between attempts while taking fewer overall attempts to solve levels. In this level, the goal is to knock the container over so that the ball touches the ground. An adult with a limb difference first uses the hook object to try to do this but by the third attempt realizes they can place an object underneath the container to tip it over. An adult without a limb difference persists with placing objects above the container in rapid succession until they are ultimately successful. ( B ) In this level, the goal is to get the red ball into the container. Children often do so by placing a tool very close to the ball, while adults are more likely to drop a tool from further up. Both this child and adult are ultimately successful. ( C ) Adults tend to perseverate with attempt types more than children. In this level, the goal is to get the red ball into the container, which can be achieved by placing a tool on the platform next to the block. Before finding solutions, this child switched between attempt types (from dropping a block at the bottom, to putting a wedge on the wrong side of the platform, to finally putting it on the correct side of the platform) more than the adult (who focused on the same attempt type but tried this attempt type many times before being successful)

For the main analyses, we used four different overall performance metrics defined by Allen et al. ( 2020 ) to measure different facets of performance: (1) whether the level was solved (solution rate), (2) time until the solving attempt was performed (time to solution), (3) how many attempts were taken until the level was solved (attempts to solution), (4) times to the first attempt and the average time between attempts (thinking time). We also analyzed the specific kinds of attempts taken (attempt type) using the same methodology introduced in Allen et al. ( 2020 ) to cluster and classify participants. All measures were automatically extracted from the recorded tool placements and timings.

Additional measures

After both the motor test and Virtual Tools task, participants were given a short questionnaire to ask what device they used to control the cursor, and, for participants with limb differences, how the cursor was controlled. Additionally, for the adult participants we included the questions asked in Allen et al. ( 2020 ), including prior video game experience, and free-form responses about strategies they had used on the task. In separate surveys, we gathered demographics and interface information (Section  S2.2 in the Supplemental Material), limb-differences information (Section   S2.3 and Tables  S2 and S3 ), and verbal and nonverbal IQ (for a subset of children only; Section S2.1 ). Age was used as a covariate in our analyses, while gender, device, and limb usage were studied as possible moderators (see Section   S8 in the Supplemental Material). Free-form strategy descriptions were read in order to discover non-standard ways of solving the levels but were not directly analyzed.

Finally, in exploratory analyses of attempt types, we also directly analyze the kinds of errors different groups of participants made in Section   S6 . These errors include using an unworkable tool, placing the tool above vs. below the correct object, or placing it at the wrong precise location relative to the correct object.

For the motor test, performance was analyzed using linear models to predict the dependent variable (either median reaction time or click distance) as a function of both age group and limb difference group, controlling for the effect of differences due to age in years separately for adults and children.

For all of the Virtual Tools performance metrics, variables were modeled using linear mixed effect models, using random intercepts for participants and levels. Additionally, we included age in years and median motor test response times as covariates, parameterized separately for children and adults. We had prespecified these as covariates since we believed that they would account for general performance differences; nonetheless analyses without covariates produced similar results (see Section   S7 in the Supplemental Material). For all but the solution metric, we conditioned our analyses only on successful levels, as we were interested in the mental processes that led to solutions, and not processes that might be indicative of frustration or perseverance; however, analyzing all levels produced a qualitatively similar pattern of results (see Section  S9 in the Supplemental Material).

In some analyses we attempt to differentiate the type of participants’ attempts, either to test whether the type of attempt is different across groups, or to test whether participants are switching types between attempts. Measuring attempt type directly in this game is challenging, as different combinations of tools / positions can have overall similar effects (see, e.g., Fig.  3 . Which of these should be considered the same type of attempt?). We therefore resort to an indirect measure which nonetheless provides intuitive notions of attempt type. Specifically, we use the classification methodology of (Allen et al., 2020 ) that groups attempts using nonparametric clustering. This allows the “type of attempt” to be defined by the data, grouping placements together in ways that can allow for custom definitions of “similar” across levels without requiring any explicit definitions. To compare attempt types across groups, we applied a leave-one-out classification analysis where, for each participant, we formed probability distributions over the tool identities and spatial positions by all other members of their group and those from members of the other group, then calculated the relative likelihood that the tool placement for a given level was a member of the correct group. To investigate whether attempt types change from attempt to attempt, we formed clusters over tool spatial positions using all attempts from all participants within a level. Two consecutive attempts were considered a “switch” if each attempt came from a separate cluster.

To determine clusters for each different level, we aggregated all attempts across all participants, taking the [ x ,  y ] spatial positions of each attempt as variables but ignoring the specific choice of tool. Footnote 1 We applied clusters separately for each participant group (children vs. adults, limb differences vs. no limb differences) but aggregated data across all participants from each group. Applying a Dirichlet Process mixture modeling package then gave us clusters for each level and group, as well as the probabilities that each attempt belonged to each cluster. We defined a “switch” as occurring if, for consecutive attempts, the cluster assigned as the highest likelihood was different for both of those attempts. Examples of discovered clusters for the data presented in Fig.  8 are shown in Fig. S6 .

We will discuss in turn (1) the equating of basic motor abilities across groups, (2) participants’ overall performance metrics, (3) differences in cognitive styles that arise during solution finding, and (4) the detailed kinds of attempts each group made. For each section we will focus on the effect of embodiment but also note the effects of age where present.

Basic motor abilities across groups

We used the motor pre-test to examine whether there were group differences in cursor control which could affect performance on the Virtual Tools game. Children could control the cursor, with an average pixel error of 7.65 px (95% CI=[6.74, 8.55]) and reaction time of 3.04 s (95% CI=[2.67, 3.41]), albeit less accurately and more slowly than adults (error: 2.92 px , 95% CI=[2.52, 3.31]; RT: 1.91 s , 95% CI=[1.73, 2.09]). Exploratory analysis showed that children’s control improved linearly with age ( \(t(65) = 3.09,~p=0.003\) , Fig.  4 ).

Reaction time on the motor task by participant age and group. Participants with limb differences were slightly faster on this task, suggesting that any differences in time to act or solve problems in the Virtual Tools game are not driven by differences in cursor control capabilities

Importantly, individuals with and without limb differences performed comparably on both motor error and reaction time. While there was a difference in median click-time between participants with and without limb differences, participants with limb differences were slightly faster (by 356 ms , 95% CI=[14, 698]; F (1,135) \(=\) 4.15, \(p =\) 0.044), though they clicked marginally further away from the target (by 0.79 px , 95% CI=[ \(-0.14\) , 1.72]; F (1,135) \(=\) 2.78, \(p =\) 0.098). There was no interaction found between age group and limb difference for either motor speed ( F (1,134) \(=\) 1.76, \(p =\) 0.19) or error ( F (1,134) \(=\) 0.001, \(p =\) 0.98). The differences found in motor control were relatively inconsequential for the Virtual Tools game – 0.79 px additional error would have little effect on 600 x 600 px game screens, and an extra 356  ms would be hard to detect with an average time between attempts of over 10  s . We therefore showed that both groups should have a level playing field for interacting with the Virtual Tools game.

Solution rate (percentage of levels solved by each participant) as a function of age for children ( left ) and adults ( right ) with limb differences (LD) and with no limb differences (NLD). Grey areas represent standard error regions on the regression lines

Performance metrics across groups

In the Virtual Tools game, we initially tested whether limb difference and age affected overall solution rates or time (in seconds) to reach a solution. We found gross differences in solution rates between children and adults (adults: 85%, children: 77%; \({x^2}(1)=\) 11.20, \(p = \) 0.0008; Fig. 5 ) but no effect of limb difference ( \({x^2}(1) = \) 1.21, \(p = \) 0.27), nor any interaction between age and limb group ( \({x^2}(1) = \)  0, \(p =\) 0.99). Exploratory analysis showed that age in years additionally predicted success ( \({x^2}(2) = \) 12, \(p =\) 0.0025): while children’s solution rates improved with age (log-odds increase per year: 0.290, 95% CI =[0.038, 0.543]), adults’ worsened (log-odds decrease per year: 0.290, 95% CI =[0.006, 0.053]).

The efficiency of finding solutions measured by number of attempts ( A ), time to solution as measured in seconds ( B ), time to first attempt ( C ), and time between attempts ( D ). Means with standard errors are shown. Participants with and without limb differences did not reliably differ on time to solution but participants with limb differences solved the levels in fewer attempts, and took more time until the first attempt and between attempts

On time to solution, children were similarly slower than adults ( \({x^2}(1) ={40.0}, p = {2.6*10^{-10}}\) ; Fig.  6 B) but we found no effect of limb differences ( \({x^2}(1) ={0.41}, p = {0.52}\) ), nor was there an interaction between limb group and age group ( \({x^2}(1) = {0.22}, p = {0.64}\) ). Adults slowed down with age (average additional 1.16 s per year, 95% CI=[0.87, 1.46], ( \({x^2}(1) ={59.4}, p = {1.3*10^{-14}})\) , while children non-significantly sped up (2.64 s less per year, 95% CI=[ \(-1.63\) , 6.90], \({x^2}(1) ={1.47}, p = {0.23}\) ).

Thus we found that while age causes noticeable changes in overall performance on the Virtual Tools game, limb differences do not. Nonetheless, participants might achieve similar overall levels of performance in different ways. We therefore next considered whether participants with or without limb differences might demonstrate distinctions on the more detailed performance metrics specified in Allen et al. ( 2020 ).

Cognitive styles in solution finding

We investigated whether there was a difference in the number of attempts that participants with and without limb differences took to solve each level. Participants with limb differences (LD) took fewer attempts on average to come to a solution than the participants with no limb differences (NLD; \(83.9\%\) of the attempts, 95% CI=[ \(72.2\%\) , \(97.6\%\) ]; \({x^2}(1) = \) 5.19, \(p =\) 0.023; Fig. 6 A). Conversely, they took more time for each attempt, including taking more time before the first attempt (3.79 s more, 95% CI = [1.99, 5.59]; \({x^2}(1) ={17.0}, p = {3.7*10^{-5}}\) ; Fig. 6 C), and between all subsequent attempts (2.80 s more on average, 95% CI = [1.51, 4.10]; \({x^2}(1) ={17.9}, p = {2.3*10^{-5}}\) ; Fig. 6 D). Again, we found differences by age, with children taking fewer attempts (89 \(\%\) of the attempts as adults, \({x^2}(1) = {6.51}, p = {0.011}\) ); more time to the first attempt (6.1 s more, \({x^2}(1) ={13.4}, p = {0.00025}\) ); and more time between attempts ( \({x^2}(1) ={41.7}, p = {1.1*10^{-10}}\) ) but no evidence for an interaction between age and limb differences for any of these measures (number of attempts: \({x^2}(1) = \) 2.07, \(p =\) 0.15; time to first attempt: \({x^2}(1) = {0.10}, p = {0.76}\) ; time between attempts: \({x^2}(1) ={0.16}, p = {0.69}\) ). Footnote 2

Together, these results suggest that individuals born with limb differences learn a different cognitive style for physical problem-solving: they learn to spend more time considering the problem and less on gathering information from their attempts. While it is not possible to conclusively determine why individuals with limb differences spent more time on each attempt (i.e., it could relate to initiation costs (Khalighinejad et al., 2021 ) or habit formation from prior experience (Wong et al., 2017 )), this extra consideration time was connected at the group level to fewer overall actions being needed to solve the problem. We therefore tentatively interpret the difference in reaction time as “thinking” – some internal computation that supports solving problems with fewer numbers of attempts.

Attempt types

We first investigated whether there were differences in the types of first attempts taken by participants with and without limb differences, using the methodology described in the Methods: Analysis section to classify whether the types of attempts made by participants with limb differences better matched those of other participants with limb differences than those without, and vice versa. If this measure is on average reliably above chance on a level, this suggests that the two groups are beginning their solution search in different ways. However, we did not find statistically reliable effects. For qualitative differences between groups see Fig.  8 , and Section  S10 of the Supplemental Material for further details. For direct analyses of “error” types (what kinds of errors each group makes), please refer to Section  S6 of the Supplemental Material.

Comparing tool placements across children and adults born with two hands. Bar plots show means and standard errors. ( A ) Examples of first attempts for both adults and children without limb differences on two levels. Each point shows an individual participant’s attempt, with the position being where they placed the tool, and the color representing which tool they chose (tools shown in colored boxes to the right of each level). ( B ) We tested whether we could classify participants’ age group based on attempt type for each level. The dashed line represents 50 \(\%\) (chance). ( C ) The likelihood that children would “switch” attempt types. Please see Methods: Analysis and Section S5 in the Supplemental Material for details on how attempt type switching was measured

Using the same methodology, we found that across all levels, children’s and adults’ first attempt types can be differentiated (Fig.  7 B; note that in this cluster analysis comparing age groups, we cannot jointly model the effect of limb differences and age, so we report analyses separately for each group; NLD: \(t(84) = {4.91} p = {4.5^*10^{-6}}\) ; LD: \(t(57) =\) 3.05, \(p =\) 0.0035). This analysis suggests that children are starting with some sort of different action than adults but it cannot tell us exactly what differs.

To explicitly test for what differs, we measured whether we could find differences between the two groups using more structured analyses of error types (e.g., using an unworkable tool, placing the tool above vs. below the correct object) but could not find reliable differences (see Section  S6 ). However, we did find that adults on average placed their tool vertically farther from the object than children (average vertical distance in children: 74 px , adults: 91 px ; \({x^2}(1) = \) 5.20, \(p = \) 0.023), which is likely driving some of the differences. Nonetheless, we cannot make strong claims about why children and adults differ along this vertical dimension – e.g., children might just be more conservative, might be worse at understanding how gravity transfers into this environment, or might differ for a variety of other reasons. Furthermore, given the small absolute difference in vertical positions between groups, more work may be needed to understand this result.

Comparison of tool placements on the first attempt for children and adults with and without limb differences. Each point represents an individual participant’s first attempt, with the position being where they placed the tool, and the color representing membership in “attempt type” clusters determined by Dirichlet Process Mixture Modeling over tool positions. Points of the same color therefore represent participants who chose first attempts belonging to the same attempt type

Since children are often more exploratory than adults (Gopnik et al., 2001 ), we also tested whether they might be more likely than adults to try new attempt types over the course of a single level. Children’s and adults without limb differences’ attempts were used to form clusters describing different tool “attempt types” (see Methods: Analysis for details), and we assigned each attempt to one of these (Fig.  8 ). Within a level, children were more likely to try new attempt types than adults (children: \(39\%\) attempt type switches, adults: \(33\%\) ; \({x^2}(1) = \) 10.2, \(p = \) 0.0014), suggesting that their lower solution rates might be due to either increased exploration of inefficient attempt types, or giving up on promising attempt types early. Using a more structured measure of switching based on the same error types above (a “switch” being defined as changing the qualitative spatial position of the tool relative to an object in the scene, or switching which object in the scene to interact with), we similarly found a difference in switching behavior, with children switching \(56\%\) of the time, and adults \(47\%\) of the time ( \(p=0.0009\) ). While statistically significant, given the relatively small absolute difference in switching behavior, more work may be needed to investigate this result further.

We used a virtual physics problem-solving game to study how growing up in a different body affects a high-level cognitive task unrelated to body or hand representations. We found minimal differences in the specific kinds of actions used by individuals with and without limb differences, suggesting that fundamental aspects of physical problem-solving are not dependent on similar kinds of manipulation experience. However, we also found that individuals with limb differences, regardless of age, spent more time considering virtual physical problems, and took fewer attempts to find solutions. While congenital limb difference is not directly associated with cognitive differences (see Section  S2.1 in the Supplemental Material), growing up with a limb difference may cause cascading effects on many aspects of development, relating to different opportunities to interact with the environment. For example, children with limb differences may be unable to solve problems by imitating their parents or peers, or may face challenges in using tools designed for two hands. In either case, they might come to appreciate the value of thinking more about solutions to physical problems before acting, and over time this could grow into a general cognitive style for interacting with the world. What is striking is that this learned cognitive style extends to a task in which action possibilities are equated across groups – indeed, individuals without limb differences were not slower to control the cursor in our task (see also (Maimon-Mor et al., 2021 )).

We call these differences in “cognitive styles,” as they fall under the definition of “consistent individual differences in preferred ways of organizing and processing information and experience” (Messick, 1976 ). However, while much of the previous research on cognitive styles has focused on how these individual differences might arise due to personality differences (Kozhevnikov, 2007 ), here we suggest that an alternative driver of different cognitive styles might be the body that people inhabit, which in turn affects the costs of interacting with the world.

Cognitive styles are often thought to be learned through experience, similarly to studies of motor learning in adults (Huang et al., 2012 ). The difference here is that the target of learning is not the motor plan itself but when to deploy those motor plans. While people’s information sampling has been shown to be sensitive to the costs of obtaining that information (Juni et al., 2016 ; Jones et al., 2019 ) and motor cost manipulations have been shown to affect the efficiency of motor reaching actions (Summerside et al., 2018 ), it has not previously been shown that motor differences directly affect the cognitive styles that people employ.

Our findings also bridge two different approaches to understanding human tool use. Tool use is theorized to be supported by specific sensorimotor knowledge of tool manipulation under the “manipulation-based” (embodied cognition) approach (Buxbaum and Kalénine, 2010 ; Gonzalez et al., 1991 ; van Elk et al., 2014 ), or generic physical knowledge under the “reasoning-based” approach (Allen et al., 2020 ; Osiurak and Badets, 2016 ). These theories have produced suggestions that there are distinct cognitive systems supporting different kinds of tool knowledge (Orban and Caruana, 2014 ; Goldenberg and Spatt, 2009 ). However, our results suggest a connection between the two systems: by its virtual nature and novel objects, the Virtual Tools game must rely on reasoning-based systems for tool use, yet we find that manipulation capabilities affect this reasoning. Thus we suggest that the development of the reasoning-based system is grounded in the embodied way that we interact with the world.

Our results extend existing knowledge about children’s problem-solving and tool use. Children can use and select known tools by 2–3 years old (Keen, 2011 ) but do not reliably innovate new tools until 8–9 years (Beck et al., 2011 ), perhaps due to its increased cognitive demands (Rawlings & Legare, 2020 ). Children’s performance in our game is likely driven by these same planning and attentional skills, which underpin a useful balance between exploration and exploitation in a large solution space (Gopnik, 2020 ). Specifically, while children at this age can avoid perseveration (Rawlings & Legare, 2020 ; Cutting et al., 2011 ), our results suggest over-exploration may be an issue, as children switched attempt types more often than adults. The central difficulty with tool innovation and other physical problem-solving tasks at this age may be the need to search through large solution spaces, where children’s propensity for exploration (Oudeyer and Smith, 2016 ; Gopnik, 2020 ) comes at the cost of short-term gains in solution-finding.

Our findings suggest new interactions between embodiment, development, and cognitive styles and raise important questions for future work. The present study focused on a single task - the Virtual Tools game - because it is similar to manipulation tasks while still not requiring manipulation directly. Thus, we expected cognitive styles learned from lifelong differences in action costs and possibilities to carry over into this task, while still providing an equal playing field for all our participants.

One might ask how broadly these differences in cognitive styles would be expected to generalize, e.g. across domains and environments. Some previous work indirectly supports the notion of a broad effect across motor-related tasks. For example, individuals with a limb difference responded more slowly in motor planning (Philip et al., 2015 ) and hand laterality judgements (Maimon-Mor et al., 2020 ). These findings were previously interpreted as a consequence of having fewer available resources to accumulate evidence for tasks related to judgements about hands. However, in light of the present findings, and because the previous observed effects were also found for the intact hand, these results can be interpreted as a generally greater reliance on planning before acting for this population. Yet, these tasks all contain a visuospatial and motor component, so it is still unclear how broad this effect is, e.g., whether it transfers to abstract logical reasoning tasks or social interactions. It is also difficult to determine, based on our single study, whether the group differences we observed are directly or indirectly caused by different embodied experience considering the many developmental differences individuals growing up with a different body will experience. For example, life experiences for individuals born with different bodies may cause them to be generally more cautious, contemplative, or creative.

Thus, the current study provides a starting point for further investigation of how we learn to deploy our cognitive resources based on our embodied experiences. Being born with a different body does not change the fundamental ways in which people try to act on the world but it can change the styles they acquire in order to plan and act efficiently in their environments.

Open Practices Statement

This study was not preregistered. The data and analyses have been made available on a permanent third-party archive https://github.com/k-r-allen/embodied_experience . The stimuli for the study can be found at https://sites.google.com/view/virtualtoolsgame and in the Supplemental Materials.

Availability of data and materials

The data have been made available on a permanent third-party archive https://github.com/k-r-allen/embodied_experience . The stimuli for the study can be found at https://sites.google.com/view/virtualtoolsgame and in the Supplemental Materials.

Code Availability

The code has been made available on a permanent third-party archive https://github.com/k-r-allen/embodied_experience .

Tool orientation could not be controlled, so was not included as a variable

When testing for the effects of limb differences in children and adults separately, we found reliable differences in attempt timing in both children (first attempt: 4.13 s , 95% CI = [0.96, 7.30], \({x^2}(1) = \) 6.52, \(p =\) 0.011; between attempts: 2.57 s , 95% CI = [0.31, 4.84], \({x^2}(1) = \) 4.96, \(p =\) 0.026) and adults (first attempt: 3.29 s , 95% CI = [1.32, 5.26], \({x^2}(1) = \) 10.7, \(p =\) 0.0011; between attempts: 2.63 s , 95% CI = [1.26, 4.01], \({x^2}(1) =\) 14.2, \(p = \) 0.00017). However, while we found that adults with limb differences use fewer attempts than those without ( \(75.1\%\) of the attempts, 95% CI = [ \(59.1\%\) , \(95.4\%\) ], \({x^2}(1) = \) 5.49, \(p =\) 0.019), children with limb differences took numerically fewer attempts than those without but this does not reach statistical significance ( \(94.9\%\) of the attempts, 95% CI = [ \(78.2\%\) , \(115.0\%\) ], \({x^2}(1) = \) 0.29, \(p =\) 0.59). Thus, while we can claim that participants with no limb differences overall took more attempts, we did not have enough evidence to discriminate whether this is because these differences are consistent across age groups, or whether the distinction grows through development.

Adalbjornsson, C. F., Fischman, M. G., & Rudisill, M. E. (2008). The end-state comfort effect in young children. Research Quarterly for Exercise and Sport, 79 (1), 36–41.

Article   PubMed   Google Scholar  

Adolph, K. E., Hoch, J. E., & Cole, W. G. (2018). Development (of walking): 15 suggestions. Trends in Cognitive Sciences, 22 (8), 699–711.

Article   PubMed   PubMed Central   Google Scholar  

Aglioti, S. M., Cesari, P., Romani, M., & Urgesi, C. (2008). Action anticipation and motor resonance in elite basketball players. Nature Neuroscience, 11 (9), 1109–1116.

Allen, K. R., Smith, K. A., & Tenenbaum, J. B. (2020). Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning. Proceedings of the National Academy of Sciences, 117 (47), 29302–29310.

Article   Google Scholar  

Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67 (1), 1–48. https://doi.org/10.18637/jss.v067.i01

Beck, S. R., Apperly, I. A., Chappell, J., Guthrie, C., & Cutting, N. (2011). Making tools isn’t child’s play. Cognition, 119 (2), 301–306. https://doi.org/10.1016/j.cognition.2011.01.003

Berard, J. R., & Vallis, L. A. (2006). Characteristics of single and double obstacle avoidance strategies: A comparison between adults and children. Experimental brain research, 175 (1), 21–31.

Buxbaum, L., & Kalénine, S. (2010). Action knowledge, visuomotor activation, and embodiment in the two action systems. Annals of the New York Academy of Sciences, 1191 , 201–218.

Chicoine, A.-J., Lassonde, M., & Proteau, L. (1992). Developmental aspects of sensorimotor integration. Developmental Neuropsychology, 8 (4), 381–394.

Cutting, N., Apperly, I. A., & Beck, S. R. (2011). Why do children lack the flexibility to innovate tools? Journal of experimental child psychology, 109 (4), 497–511.

Dasgupta, I., Smith, K. A., Schulz, E., Tenenbaum, J. B., & Gershman, S. J. (2018). Learning to act by integrating mental simulations and physical experiments. In Proceedings of the 40th Annual meeting of the cognitive science society . https://doi.org/10.1101/321497

Gallivan, J. P., Chapman, C. S., Wolpert, D. M., & Flanagan, J. R. (2018). Decision-making in sensorimotor control. Nature Reviews Neuroscience, 19 (9), 519–534.

Goldenberg, G., & Spatt, J. (2009). The neural basis of tool use. Brain, 132 (6), 1645–1655. https://doi.org/10.1093/brain/awp080

Gonzalez Rothi, L. J., Ochipa, C., & Heilman, K. M. (1991). A cognitive neuropsychological model of limb praxis. Cognitive Neuropsychology, 8 (6), 443–458.

Gopnik, A. (2020). Childhood as a solution to explore-exploit tensions. Philosophical Transactions of the Royal Society B, 375 (1803), 20190502.

Gopnik, A., Sobel, D. M., Schulz, L. E., & Glymour, C. (2001). Causal learning mechanisms in very young children: Two-, three-, and four-year-olds infer causal relations from patterns of variation and covariation. Developmental Psychology, 37 (5), 620.

Hagura, N., Haggard, P., & Diedrichsen, J. (2017). Perceptual decisions are biased by the cost to act. ELife, 6 , 1–20.

Google Scholar  

Harris, C. M., & Wolpert, D. M. (1998). Signal-dependent noise determines motor planning. Nature, 394 (6695), 780–784.

Huang, H. J., Kram, R., & Ahmed, A. A. (2012). Reduction of metabolic cost during motor learning of arm reaching dynamics. Journal of Neuroscience, 32 (6), 2182–2190.

Izawa, J., Rane, T., Donchin, O., & Shadmehr, R. (2008). Motor adaptation as a process of reoptimization. Journal of Neuroscience, 28 (11), 2883–2891.

Jones, P. R., Landin, L., McLean, A., Juni, M. Z., Maloney, L. T., Nardini, M., & Dekker, T. M. (2019). Efficient visual information sampling develops late in childhood. Journal of Experimental Psychology: General, 148 (7), 1138.

Juni, M. Z., Gureckis, T. M., & Maloney, L. T. (2016). Information sampling behavior with explicit sampling costs. Decision, 3 (3), 147.

Keen, R. (2011). The development of problem solving in young children: A critical cognitive skill. Annual Review of Psychology, 62 , 1–21.

Khalighinejad, N., Garrett, N., Priestley, L., Lockwood, P., & Rushworth, M. F. (2021). A habenula-insular circuit encodes the willingness to act. Nature Communications, 12 (1), 6329.

Körding, K. P., & Wolpert, D. M. (2004). Bayesian integration in sensorimotor learning. Nature, 427 (6971), 244–247.

Kozhevnikov, M. (2007). Cognitive styles in the context of modern psychology: Toward an integrated framework of cognitive style. Psychological Bulletin, 133 (3), 464.

Leonard, J. A., Lee, Y., & Schulz, L. E. (2017). Infants make more attempts to achieve a goal when they see adults persist. Science, 357 (6357), 1290–1294.

Maimon-Mor, R. O., Schone, H. R., Moran, R., Brugger, P., & Makin, T. R. (2020). Motor control drives visual bodily judgements. Cognition, 196 , 104120.

Maimon-Mor, R. O., Schone, H. R., Slater, D. H., Faisal, A. A., & Makin, T. R. (2021). Early life experience sets hard limits on motor learning as evidenced from artificial arm use. Elife, 10, e66320.

Makin, T. R., Wilf, M., Schwartz, I., & Zohary, E. (2010). Amputees “neglect’’ the space near their missing hand. Psychological Science, 21 (1), 55–57.

Messick, S. (1976). Personality consistencies in cognition and creativity. In Individuality in learning (pp. 4-23). Jossey-Bass.

Orban, G. A., & Caruana, F. (2014). The neural basis of human tool use. Front Psychol, 5 ,. https://doi.org/10.3389/fpsyg.2014.00310

Osiurak, F., & Badets, A. (2016). Tool use and affordance: Manipulation-based versus reasoning-based approaches. Psychological Review, 123 (5), 534.

Oudeyer, P.-Y., & Smith, L. B. (2016). How evolution may work through curiosity-driven developmental process. Topics in Cognitive Science, 8 (2), 492–502.

Philip, B. A., Buckon, C., Sienko, S., Aiona, M., Ross, S., & H., F. S. (2015). Maturation and experience in action representation: Bilateral deficits in unilateral congenital amelia. Neuropsychologia, 75 , 420–430.

Prévost, C., Pessiglione, M., Météreau, E., Cléry-Melin, M.-L., & Dreher, J.-C. (2010). Separate valuation subsystems for delay and effort decision costs. Journal of Neuroscience, 30 (42), 14080–14090.

Rawlings, B., & Legare, C. H. (2020). Toddlers, tools, and tech: The cognitive ontogenesis of innovation. Trends in Cognitive Sciences .

Summerside, E. M., Shadmehr, R., & Ahmed, A. A. (2018). Vigor of reaching movements: reward discounts the cost of effort. Journal of Neurophysiology, 119 (6), 2347–2357.

van Elk, M., van Schie, H., & Bekkering, H. (2014). Action semantics: A unifying conceptual framework for the selective use of multimodal and modality-specific object knowledge. Physics of Life Reviews, 11 (2), 220–250.

Vannuscorps, G., & Caramazza, A. (2016). Typical action perception and interpretation without motor simulation. Proceedings of the National Academy of Sciences, 113 (1), 86–91.

Vannuscorps, G., Pillon, A., & Andres, M. (2012). Effect of biomechanical constraints in the hand laterality judgment task: Where does it come from? Frontiers in Human Neuroscience, 6 , 299.

Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., & SciPy 1.0 Contributors,. (2020). SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17 , 261–272. https://doi.org/10.1038/s41592-019-0686-2

Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9 (4), 625–636. https://doi.org/10.3758/BF03196322

Wong, A. L., Goldsmith, J., Forrence, A. D., Haith, A. M., & Krakauer, J. W. (2017). Reaction times can reflect habits rather than computations. eLife, 6 , e28075. https://doi.org/10.7554/eLife.28075

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Acknowledgements

We thank Mathew Kollamkulam for collecting the data from the adult samples, Opcare, Limbo and Reach for assistance with participant recruitment, and all participating families and volunteers. The study was supported by a Wellcome Trust Senior Research Fellowship (215575/Z/19/Z) and a Medical Research Council grant (MC_UU_00030/10), awarded to TRM. KRA, KAS, and JBT were supported by National Science Foundation Science Technology Center Award CCF-1231216; Office of Naval Research Multidisciplinary University Research Initiative (ONR MURI) N00014-13-1-0333; and research grants from ONR, Honda, and Mitsubishi Electric. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission.

Open Access funding provided by the MIT Libraries. The study was supported by a Wellcome Trust Senior Research Fellowship (215575/Z/19/Z) and a Medical Research Council grant (MC_UU_00030/10), awarded to TRM. KRA, KAS, and JBT were supported by National Science Foundation Science Technology Center Award CCF-1231216; Office of Naval Research Multidisciplinary University Research Initiative (ONR MURI) N00014-13-1-0333; and research grants from ONR, Honda, and Mitsubishi Electric.

Author information

Kelsey R. Allen and Kevin A. Smith contributed equally to this work. Tamar R. Makin and Dorothy Cowie supervised equally for this work.

Authors and Affiliations

Department of Brain and Cognitive Sciences, MIT and Center for Brains, Minds, and Machines, Cambridge, MA, USA

Kelsey R. Allen, Kevin A. Smith & Joshua B. Tenenbaum

Department of Psychology, Durham University, Durham, UK

Laura-Ashleigh Bird & Dorothy Cowie

MRC Cognition Brain Sciences Unit, University of Cambridge, Cambridge, UK

Tamar R. Makin

Institute of Cognitive Neuroscience, University College London, London, UK

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All authors contributed to the study conceptualization. KRA and KAS developed materials. LAB recruited participants under the supervision of TRM and DC. KRA and KAS analyzed the data. All authors contributed to the writing of the manuscript.

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Correspondence to Kelsey R. Allen .

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The experiment was approved for adults under protocols approved by UCL (9937/001), and all adults provided informed consent. The experiment was approved for children under a protocol approved by the ethics committee at Durham University (PSYCH-2019-08-30T10_08_45-mnvj24), and informed consent was provided by the legal guardian.

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Allen, K.R., Smith, K.A., Bird, LA. et al. Lifelong learning of cognitive styles for physical problem-solving: The effect of embodied experience. Psychon Bull Rev 31 , 1364–1375 (2024). https://doi.org/10.3758/s13423-023-02400-4

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Accepted : 03 October 2023

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DOI : https://doi.org/10.3758/s13423-023-02400-4

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Problem solving and cognitive style: An error analysis

N Ratnaningsih 1 , E Hidayat 1 and S Santika 1

Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series , Volume 1657 , The 2nd International Seminar on Applied Mathematics and Mathematics Education (2nd ISAMME) 2020 5 August 2020, Cimahi, Indonesia Citation N Ratnaningsih et al 2020 J. Phys.: Conf. Ser. 1657 012035 DOI 10.1088/1742-6596/1657/1/012035

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1 Program Studi Pendidikan Matematika, Universitas Siliwangi, Jl. Siliwangi No. 24, Tasikmalaya 46115, Jawa Barat, Indonesia

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Problem-solving and cognitive style are two things that are important to note, but the reality is rarely considered especially cognitive style. Student cognitive style can influence problem-solving abilities. This study aims to investigate student errors based on Newman in problem solving, in terms of independent and dependent field cognitive style. The study was conducted on junior high school students in Indonesia, the method used is exploratory involving 2 Subject. Subjects are taken from independent and dependent field style cognitive groups. Stages of problem-solving according to Polya's includes: understanding the problem, devising a plan, carrying out the plan, and look back. Errors according to Newman include errors in reading, comprehension, transformation, process skills and encoding. Data were collected using the Group Embedded Figure Test (GEFT) and problem-solving test. The conclusion shows that subject with field dependent cognitive style with the initials SFD tend to make errors at the transformation stage in making models, process skills in manipulating algebra and calculation processes, and conclusions for making errors in the previous stage. Whereas subject with independent field cognitive style with the initials SFI tend to make errors at the process skill stage in manipulating algebra and calculation processes.

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