define problem solving cognitive psychology

7.3 Problem-Solving

Learning objectives.

By the end of this section, you will be able to:

• Describe problem solving strategies
• Define algorithm and heuristic
• Explain some common roadblocks to effective problem solving

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

The study of human and animal problem solving processes has provided much insight toward the understanding of our conscious experience and led to advancements in computer science and artificial intelligence. Essentially much of cognitive science today represents studies of how we consciously and unconsciously make decisions and solve problems. For instance, when encountered with a large amount of information, how do we go about making decisions about the most efficient way of sorting and analyzing all the information in order to find what you are looking for as in visual search paradigms in cognitive psychology. Or in a situation where a piece of machinery is not working properly, how do we go about organizing how to address the issue and understand what the cause of the problem might be. How do we sort the procedures that will be needed and focus attention on what is important in order to solve problems efficiently. Within this section we will discuss some of these issues and examine processes related to human, animal and computer problem solving.

PROBLEM-SOLVING STRATEGIES

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

Problems themselves can be classified into two different categories known as ill-defined and well-defined problems (Schacter, 2009). Ill-defined problems represent issues that do not have clear goals, solution paths, or expected solutions whereas well-defined problems have specific goals, clearly defined solutions, and clear expected solutions. Problem solving often incorporates pragmatics (logical reasoning) and semantics (interpretation of meanings behind the problem), and also in many cases require abstract thinking and creativity in order to find novel solutions. Within psychology, problem solving refers to a motivational drive for reading a definite “goal” from a present situation or condition that is either not moving toward that goal, is distant from it, or requires more complex logical analysis for finding a missing description of conditions or steps toward that goal. Processes relating to problem solving include problem finding also known as problem analysis, problem shaping where the organization of the problem occurs, generating alternative strategies, implementation of attempted solutions, and verification of the selected solution. Various methods of studying problem solving exist within the field of psychology including introspection, behavior analysis and behaviorism, simulation, computer modeling, and experimentation.

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

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

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

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

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

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

Further problem solving strategies have been identified (listed below) that incorporate flexible and creative thinking in order to reach solutions efficiently.

Additional Problem Solving Strategies :

• Abstraction – refers to solving the problem within a model of the situation before applying it to reality.
• Analogy – is using a solution that solves a similar problem.
• Brainstorming – refers to collecting an analyzing a large amount of solutions, especially within a group of people, to combine the solutions and developing them until an optimal solution is reached.
• Divide and conquer – breaking down large complex problems into smaller more manageable problems.
• Hypothesis testing – method used in experimentation where an assumption about what would happen in response to manipulating an independent variable is made, and analysis of the affects of the manipulation are made and compared to the original hypothesis.
• Lateral thinking – approaching problems indirectly and creatively by viewing the problem in a new and unusual light.
• Means-ends analysis – choosing and analyzing an action at a series of smaller steps to move closer to the goal.
• Method of focal objects – putting seemingly non-matching characteristics of different procedures together to make something new that will get you closer to the goal.
• Morphological analysis – analyzing the outputs of and interactions of many pieces that together make up a whole system.
• Proof – trying to prove that a problem cannot be solved. Where the proof fails becomes the starting point or solving the problem.
• Reduction – adapting the problem to be as similar problems where a solution exists.
• Research – using existing knowledge or solutions to similar problems to solve the problem.
• Root cause analysis – trying to identify the cause of the problem.

The strategies listed above outline a short summary of methods we use in working toward solutions and also demonstrate how the mind works when being faced with barriers preventing goals to be reached.

One example of means-end analysis can be found by using the Tower of Hanoi paradigm . This paradigm can be modeled as a word problems as demonstrated by the Missionary-Cannibal Problem :

Missionary-Cannibal Problem

Three missionaries and three cannibals are on one side of a river and need to cross to the other side. The only means of crossing is a boat, and the boat can only hold two people at a time. Your goal is to devise a set of moves that will transport all six of the people across the river, being in mind the following constraint: The number of cannibals can never exceed the number of missionaries in any location. Remember that someone will have to also row that boat back across each time.

Hint : At one point in your solution, you will have to send more people back to the original side than you just sent to the destination.

The actual Tower of Hanoi problem consists of three rods sitting vertically on a base with a number of disks of different sizes that can slide onto any rod. The puzzle starts with the disks in a neat stack in ascending order of size on one rod, the smallest at the top making a conical shape. The objective of the puzzle is to move the entire stack to another rod obeying the following rules:

• 1. Only one disk can be moved at a time.
• 2. Each move consists of taking the upper disk from one of the stacks and placing it on top of another stack or on an empty rod.
• 3. No disc may be placed on top of a smaller disk.

  Figure 7.02. Steps for solving the Tower of Hanoi in the minimum number of moves when there are 3 disks.

Figure 7.03. Graphical representation of nodes (circles) and moves (lines) of Tower of Hanoi.

The Tower of Hanoi is a frequently used psychological technique to study problem solving and procedure analysis. A variation of the Tower of Hanoi known as the Tower of London has been developed which has been an important tool in the neuropsychological diagnosis of executive function disorders and their treatment.

GESTALT PSYCHOLOGY AND PROBLEM SOLVING

As you may recall from the sensation and perception chapter, Gestalt psychology describes whole patterns, forms and configurations of perception and cognition such as closure, good continuation, and figure-ground. In addition to patterns of perception, Wolfgang Kohler, a German Gestalt psychologist traveled to the Spanish island of Tenerife in order to study animals behavior and problem solving in the anthropoid ape.

As an interesting side note to Kohler’s studies of chimp problem solving, Dr. Ronald Ley, professor of psychology at State University of New York provides evidence in his book A Whisper of Espionage  (1990) suggesting that while collecting data for what would later be his book  The Mentality of Apes (1925) on Tenerife in the Canary Islands between 1914 and 1920, Kohler was additionally an active spy for the German government alerting Germany to ships that were sailing around the Canary Islands. Ley suggests his investigations in England, Germany and elsewhere in Europe confirm that Kohler had served in the German military by building, maintaining and operating a concealed radio that contributed to Germany’s war effort acting as a strategic outpost in the Canary Islands that could monitor naval military activity approaching the north African coast.

While trapped on the island over the course of World War 1, Kohler applied Gestalt principles to animal perception in order to understand how they solve problems. He recognized that the apes on the islands also perceive relations between stimuli and the environment in Gestalt patterns and understand these patterns as wholes as opposed to pieces that make up a whole. Kohler based his theories of animal intelligence on the ability to understand relations between stimuli, and spent much of his time while trapped on the island investigation what he described as  insight , the sudden perception of useful or proper relations. In order to study insight in animals, Kohler would present problems to chimpanzee’s by hanging some banana’s or some kind of food so it was suspended higher than the apes could reach. Within the room, Kohler would arrange a variety of boxes, sticks or other tools the chimpanzees could use by combining in patterns or organizing in a way that would allow them to obtain the food (Kohler & Winter, 1925).

While viewing the chimpanzee’s, Kohler noticed one chimp that was more efficient at solving problems than some of the others. The chimp, named Sultan, was able to use long poles to reach through bars and organize objects in specific patterns to obtain food or other desirables that were originally out of reach. In order to study insight within these chimps, Kohler would remove objects from the room to systematically make the food more difficult to obtain. As the story goes, after removing many of the objects Sultan was used to using to obtain the food, he sat down ad sulked for a while, and then suddenly got up going over to two poles lying on the ground. Without hesitation Sultan put one pole inside the end of the other creating a longer pole that he could use to obtain the food demonstrating an ideal example of what Kohler described as insight. In another situation, Sultan discovered how to stand on a box to reach a banana that was suspended from the rafters illustrating Sultan’s perception of relations and the importance of insight in problem solving.

Grande (another chimp in the group studied by Kohler) builds a three-box structure to reach the bananas, while Sultan watches from the ground.  Insight , sometimes referred to as an “Ah-ha” experience, was the term Kohler used for the sudden perception of useful relations among objects during problem solving (Kohler, 1927; Radvansky & Ashcraft, 2013).

Solving puzzles.

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

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

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

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

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

What steps did you take to solve this puzzle? You can read the solution at the end of this section.

Pitfalls to problem solving.

   Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

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

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

The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in the table below.

Were you able to determine how many marbles are needed to balance the scales in the figure below? You need nine. Were you able to solve the problems in the figures above? Here are the answers.

   Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. Roadblocks to problem solving include a mental set, functional fixedness, and various biases that can cloud decision making skills.

References:

Openstax Psychology text by Kathryn Dumper, William Jenkins, Arlene Lacombe, Marilyn Lovett and Marion Perlmutter licensed under CC BY v4.0. https://openstax.org/details/books/psychology

Review Questions:

1. A specific formula for solving a problem is called ________.

a. an algorithm

b. a heuristic

c. a mental set

d. trial and error

2. Solving the Tower of Hanoi problem tends to utilize a  ________ strategy of problem solving.

a. divide and conquer

b. means-end analysis

d. experiment

3. A mental shortcut in the form of a general problem-solving framework is called ________.

4. Which type of bias involves becoming fixated on a single trait of a problem?

a. anchoring bias

b. confirmation bias

c. representative bias

d. availability bias

5. Which type of bias involves relying on a false stereotype to make a decision?

6. Wolfgang Kohler analyzed behavior of chimpanzees by applying Gestalt principles to describe ________.

a. social adjustment

b. student load payment options

c. emotional learning

d. insight learning

7. ________ is a type of mental set where you cannot perceive an object being used for something other than what it was designed for.

a. functional fixedness

c. working memory

Critical Thinking Questions:

1. What is functional fixedness and how can overcoming it help you solve problems?

2. How does an algorithm save you time and energy when solving a problem?

Personal Application Question:

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

anchoring bias

availability heuristic

confirmation bias

functional fixedness

hindsight bias

problem-solving strategy

representative bias

trial and error

working backwards

Answers to Exercises

algorithm:  problem-solving strategy characterized by a specific set of instructions

anchoring bias:  faulty heuristic in which you fixate on a single aspect of a problem to find a solution

availability heuristic:  faulty heuristic in which you make a decision based on information readily available to you

confirmation bias:  faulty heuristic in which you focus on information that confirms your beliefs

functional fixedness:  inability to see an object as useful for any other use other than the one for which it was intended

heuristic:  mental shortcut that saves time when solving a problem

hindsight bias:  belief that the event just experienced was predictable, even though it really wasn’t

mental set:  continually using an old solution to a problem without results

problem-solving strategy:  method for solving problems

representative bias:  faulty heuristic in which you stereotype someone or something without a valid basis for your judgment

trial and error:  problem-solving strategy in which multiple solutions are attempted until the correct one is found

working backwards:  heuristic in which you begin to solve a problem by focusing on the end result

Share This Book

• Increase Font Size

5 Effective Problem-Solving Strategies

Got a problem you’re trying to solve? Strategies like trial and error, gut instincts, and “working backward” can help. We look at some examples and how to use them.

We all face problems daily. Some are simple, like deciding what to eat for dinner. Others are more complex, like resolving a conflict with a loved one or figuring out how to overcome barriers to your goals.

No matter what problem you’re facing, these five problem-solving strategies can help you develop an effective solution.

What are problem-solving strategies?

To effectively solve a problem, you need a problem-solving strategy .

If you’ve had to make a hard decision before then you know that simply ruminating on the problem isn’t likely to get you anywhere. You need an effective strategy — or a plan of action — to find a solution.

In general, effective problem-solving strategies include the following steps:

• Define the problem.
• Come up with alternative solutions.
• Decide on a solution.
• Implement the solution.

Problem-solving strategies don’t guarantee a solution, but they do help guide you through the process of finding a resolution.

Using problem-solving strategies also has other benefits . For example, having a strategy you can turn to can help you overcome anxiety and distress when you’re first faced with a problem or difficult decision.

The key is to find a problem-solving strategy that works for your specific situation, as well as your personality. One strategy may work well for one type of problem but not another. In addition, some people may prefer certain strategies over others; for example, creative people may prefer to depend on their insights than use algorithms.

It’s important to be equipped with several problem-solving strategies so you use the one that’s most effective for your current situation.

1. Trial and error

One of the most common problem-solving strategies is trial and error. In other words, you try different solutions until you find one that works.

For example, say the problem is that your Wi-Fi isn’t working. You might try different things until it starts working again, like restarting your modem or your devices until you find or resolve the problem. When one solution isn’t successful, you try another until you find what works.

Trial and error can also work for interpersonal problems . For example, if your child always stays up past their bedtime, you might try different solutions — a visual clock to remind them of the time, a reward system, or gentle punishments — to find a solution that works.

2. Heuristics

Sometimes, it’s more effective to solve a problem based on a formula than to try different solutions blindly.

Heuristics are problem-solving strategies or frameworks people use to quickly find an approximate solution. It may not be the optimal solution, but it’s faster than finding the perfect resolution, and it’s “good enough.”

Algorithms or equations are examples of heuristics.

An algorithm is a step-by-step problem-solving strategy based on a formula guaranteed to give you positive results. For example, you might use an algorithm to determine how much food is needed to feed people at a large party.

However, many life problems have no formulaic solution; for example, you may not be able to come up with an algorithm to solve the problem of making amends with your spouse after a fight.

3. Gut instincts (insight problem-solving)

While algorithm-based problem-solving is formulaic, insight problem-solving is the opposite.

When we use insight as a problem-solving strategy we depend on our “gut instincts” or what we know and feel about a situation to come up with a solution. People might describe insight-based solutions to problems as an “aha moment.”

For example, you might face the problem of whether or not to stay in a relationship. The solution to this problem may come as a sudden insight that you need to leave. In insight problem-solving, the cognitive processes that help you solve a problem happen outside your conscious awareness.

4. Working backward

Working backward is a problem-solving approach often taught to help students solve problems in mathematics. However, it’s useful for real-world problems as well.

Working backward is when you start with the solution and “work backward” to figure out how you got to the solution. For example, if you know you need to be at a party by 8 p.m., you might work backward to problem-solve when you must leave the house, when you need to start getting ready, and so on.

5. Means-end analysis

Means-end analysis is a problem-solving strategy that, to put it simply, helps you get from “point A” to “point B” by examining and coming up with solutions to obstacles.

When using means-end analysis you define the current state or situation (where you are now) and the intended goal. Then, you come up with solutions to get from where you are now to where you need to be.

For example, a student might be faced with the problem of how to successfully get through finals season . They haven’t started studying, but their end goal is to pass all of their finals. Using means-end analysis, the student can examine the obstacles that stand between their current state and their end goal (passing their finals).

They could see, for example, that one obstacle is that they get distracted from studying by their friends. They could devise a solution to this obstacle by putting their phone on “do not disturb” mode while studying.

Let’s recap

Whether they’re simple or complex, we’re faced with problems every day. To successfully solve these problems we need an effective strategy. There are many different problem-solving strategies to choose from.

Although problem-solving strategies don’t guarantee a solution, they can help you feel less anxious about problems and make it more likely that you come up with an answer.

Last medically reviewed on November 1, 2022

8 sources collapsed

• Chu Y, et al. (2011). Human performance on insight problem-solving: A review. https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1094&context=jps
• Dumper K, et al. (n.d.) Chapter 7.3: Problem-solving in introductory psychology. https://opentext.wsu.edu/psych105/chapter/7-4-problem-solving/
• Foulds LR. (2017). The heuristic problem-solving approach. https://www.tandfonline.com/doi/abs/10.1057/jors.1983.205
• Gick ML. (1986). Problem-solving strategies. https://www.tandfonline.com/doi/abs/10.1080/00461520.1986.9653026
• Montgomery ME. (2015). Problem solving using means-end analysis. https://sites.psu.edu/psych256sp15/2015/04/19/problem-solving-using-means-end-analysis/
• Posamentier A, et al. (2015). Problem-solving strategies in mathematics. Chapter 3: Working backwards. https://www.worldscientific.com/doi/10.1142/9789814651646_0003
• Sarathy V. (2018). Real world problem-solving. https://www.frontiersin.org/articles/10.3389/fnhum.2018.00261/full
• Woods D. (2000). An evidence-based strategy for problem solving. https://www.researchgate.net/publication/245332888_An_Evidence-Based_Strategy_for_Problem_Solving

Read this next

Making big decisions can be a difficult task. Setting deadlines and asking for support can help you confidently move ahead.

Dealing with a problem can fee a lot more manageable when you have a plan. Try these 5 steps for becoming a better problem-solver.

A lack of communication in relationships doesn't have to be a dealbreaker. Learn how to improve your communication skills at work and at home.

Sleep deprivation, stress, or underlying health conditions can lead to an inability to focus. Self-help techniques can help improve your concentration.

Positive thinking is an essential practice to improve your overall health and well-being. Discover how to incorporate positive thinking into your…

Dreaming about babies can hold different meanings for everyone. Although theories vary, biological and psychological factors may influence your dreams.

If you're seeking to boost your concentration, practicing mindfulness, chewing gum, and brain games are just a few techniques to try. Learn how they…

Creating a schedule and managing stress are ways to make your days go by faster. Changing your perception of time can also improve your overall…

Experiencing unwanted and difficult memories can be challenging. But learning how to replace negative memories with positive ones may help you cope.

Engaging in brain exercises, like sudoku puzzles and learning new languages, enhances cognitive abilities and improves overall well-being.

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

What Is Cognitive Psychology?

The Science of How We Think

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

Steven Gans, MD is board-certified in psychiatry and is an active supervisor, teacher, and mentor at Massachusetts General Hospital.

Topics in Cognitive Psychology

• Current Research
• Cognitive Approach in Practice

Careers in Cognitive Psychology

How cognitive psychology differs from other branches of psychology, frequently asked questions.

Cognitive psychology is the study of internal mental processes—all of the workings inside your brain, including perception, thinking, memory, attention, language, problem-solving, and learning. Learning about how people think and process information helps researchers and psychologists understand the human brain and assist people with psychological difficulties.

This article discusses what cognitive psychology is—its history, current trends, practical applications, and career paths.

Findings from cognitive psychology help us understand how people think, including how they acquire and store memories. By knowing more about how these processes work, psychologists can develop new ways of helping people with cognitive problems.

Cognitive psychologists explore a wide variety of topics related to thinking processes. Some of these include: 

• Attention --our ability to process information in the environment while tuning out irrelevant details
• Choice-based behavior --actions driven by a choice among other possibilities
• Decision-making
• Information processing
• Language acquisition --how we learn to read, write, and express ourselves
• Problem-solving
• Speech perception -how we process what others are saying
• Visual perception --how we see the physical world around us

History of Cognitive Psychology

Although it is a relatively young branch of psychology , it has quickly grown to become one of the most popular subfields. Cognitive psychology grew into prominence between the 1950s and 1970s.

Prior to this time, behaviorism was the dominant perspective in psychology. This theory holds that we learn all our behaviors from interacting with our environment. It focuses strictly on observable behavior, not thought and emotion. Then, researchers became more interested in the internal processes that affect behavior instead of just the behavior itself. 

This shift is often referred to as the cognitive revolution in psychology. During this time, a great deal of research on topics including memory, attention, and language acquisition began to emerge. 

In 1967, the psychologist Ulric Neisser introduced the term cognitive psychology, which he defined as the study of the processes behind the perception, transformation, storage, and recovery of information.

Cognitive psychology became more prominent after the 1950s as a result of the cognitive revolution.

Current Research in Cognitive Psychology

The field of cognitive psychology is both broad and diverse. It touches on many aspects of daily life. There are numerous practical applications for this research, such as providing help coping with memory disorders, making better decisions , recovering from brain injury, treating learning disorders, and structuring educational curricula to enhance learning.

Current research on cognitive psychology helps play a role in how professionals approach the treatment of mental illness, traumatic brain injury, and degenerative brain diseases.

Thanks to the work of cognitive psychologists, we can better pinpoint ways to measure human intellectual abilities, develop new strategies to combat memory problems, and decode the workings of the human brain—all of which ultimately have a powerful impact on how we treat cognitive disorders.

The field of cognitive psychology is a rapidly growing area that continues to add to our understanding of the many influences that mental processes have on our health and daily lives.

From understanding how cognitive processes change as a child develops to looking at how the brain transforms sensory inputs into perceptions, cognitive psychology has helped us gain a deeper and richer understanding of the many mental events that contribute to our daily existence and overall well-being.

The Cognitive Approach in Practice

In addition to adding to our understanding of how the human mind works, the field of cognitive psychology has also had an impact on approaches to mental health. Before the 1970s, many mental health treatments were focused more on psychoanalytic , behavioral , and humanistic approaches.

The so-called "cognitive revolution" put a greater emphasis on understanding the way people process information and how thinking patterns might contribute to psychological distress. Thanks to research in this area, new approaches to treatment were developed to help treat depression, anxiety, phobias, and other psychological disorders .

Cognitive behavioral therapy and rational emotive behavior therapy are two methods in which clients and therapists focus on the underlying cognitions, or thoughts, that contribute to psychological distress.

What Is Cognitive Behavioral Therapy?

Cognitive behavioral therapy (CBT) is an approach that helps clients identify irrational beliefs and other cognitive distortions that are in conflict with reality and then aid them in replacing such thoughts with more realistic, healthy beliefs.

If you are experiencing symptoms of a psychological disorder that would benefit from the use of cognitive approaches, you might see a psychologist who has specific training in these cognitive treatment methods.

These professionals frequently go by titles other than cognitive psychologists, such as psychiatrists, clinical psychologists , or counseling psychologists , but many of the strategies they use are rooted in the cognitive tradition.

Many cognitive psychologists specialize in research with universities or government agencies. Others take a clinical focus and work directly with people who are experiencing challenges related to mental processes. They work in hospitals, mental health clinics, and private practices.

Research psychologists in this area often concentrate on a particular topic, such as memory. Others work directly on health concerns related to cognition, such as degenerative brain disorders and brain injuries.

Treatments rooted in cognitive research focus on helping people replace negative thought patterns with more positive, realistic ones. With the help of cognitive psychologists, people are often able to find ways to cope and even overcome such difficulties.

Reasons to Consult a Cognitive Psychologist

• Alzheimer's disease, dementia, or memory loss
• Brain trauma treatment
• Cognitive therapy for a mental health condition
• Interventions for learning disabilities
• Perceptual or sensory issues
• Therapy for a speech or language disorder

Whereas behavioral and some other realms of psychology focus on actions--which are external and observable--cognitive psychology is instead concerned with the thought processes behind the behavior. Cognitive psychologists see the mind as if it were a computer, taking in and processing information, and seek to understand the various factors involved.

A Word From Verywell

Cognitive psychology plays an important role in understanding the processes of memory, attention, and learning. It can also provide insights into cognitive conditions that may affect how people function.

Being diagnosed with a brain or cognitive health problem can be daunting, but it is important to remember that you are not alone. Together with a healthcare provider, you can come up with an effective treatment plan to help address brain health and cognitive problems.

Your treatment may involve consulting with a cognitive psychologist who has a background in the specific area of concern that you are facing, or you may be referred to another mental health professional that has training and experience with your particular condition.

Ulric Neisser is considered the founder of cognitive psychology. He was the first to introduce the term and to define the field of cognitive psychology. His primary interests were in the areas of perception and memory, but he suggested that all aspects of human thought and behavior were relevant to the study of cognition.

A cognitive map refers to a mental representation of an environment. Such maps can be formed through observation as well as through trial and error. These cognitive maps allow people to orient themselves in their environment.

While they share some similarities, there are some important differences between cognitive neuroscience and cognitive psychology. While cognitive psychology focuses on thinking processes, cognitive neuroscience is focused on finding connections between thinking and specific brain activity. Cognitive neuroscience also looks at the underlying biology that influences how information is processed.

Cognitive psychology is a form of experimental psychology. Cognitive psychologists use experimental methods to study the internal mental processes that play a role in behavior.

Sternberg RJ, Sternberg K. Cognitive Psychology . Wadsworth/Cengage Learning. 

Krapfl JE. Behaviorism and society . Behav Anal. 2016;39(1):123-9. doi:10.1007/s40614-016-0063-8

Cutting JE. Ulric Neisser (1928-2012) . Am Psychol . 2012;67(6):492. doi:10.1037/a0029351

Ruggiero GM, Spada MM, Caselli G, Sassaroli S. A historical and theoretical review of cognitive behavioral therapies: from structural self-knowledge to functional processes .  J Ration Emot Cogn Behav Ther . 2018;36(4):378-403. doi:10.1007/s10942-018-0292-8

Parvin P. Ulric Neisser, cognitive psychology pioneer, dies . Emory News Center.

APA Dictionary of Psychology. Cognitive map . American Psychological Association.

Forstmann BU, Wagenmakers EJ, Eichele T, Brown S, Serences JT. Reciprocal relations between cognitive neuroscience and formal cognitive models: opposites attract? . Trends Cogn Sci . 2011;15(6):272-279. doi:10.1016/j.tics.2011.04.002

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

• Subject List
• Take a Tour
• For Authors
• Subscriber Services
• Publications
• African American Studies
• African Studies
• American Literature
• Anthropology
• Architecture Planning and Preservation
• Art History
• Atlantic History
• Biblical Studies
• British and Irish Literature
• Childhood Studies
• Chinese Studies
• Cinema and Media Studies
• Communication
• Criminology
• Environmental Science
• Evolutionary Biology
• International Law
• International Relations
• Islamic Studies
• Jewish Studies
• Latin American Studies
• Latino Studies
• Linguistics
• Literary and Critical Theory
• Medieval Studies
• Military History
• Political Science
• Public Health
• Renaissance and Reformation
• Social Work
• Urban Studies
• Victorian Literature
• Browse All Subjects

How to Subscribe

• Free Trials

In This Article Expand or collapse the "in this article" section Problem Solving and Decision Making

Introduction.

• General Approaches to Problem Solving
• Representational Accounts
• Problem Space and Search
• Working Memory and Problem Solving
• Domain-Specific Problem Solving
• The Rational Approach
• Prospect Theory
• Dual-Process Theory
• Cognitive Heuristics and Biases

Related Articles Expand or collapse the "related articles" section about

About related articles close popup.

Lorem Ipsum Sit Dolor Amet

Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Aliquam ligula odio, euismod ut aliquam et, vestibulum nec risus. Nulla viverra, arcu et iaculis consequat, justo diam ornare tellus, semper ultrices tellus nunc eu tellus.

• Artificial Intelligence, Machine Learning, and Psychology
• Counterfactual Reasoning
• Critical Thinking
• Heuristics and Biases
• Protocol Analysis
• Psychology and Law

Other Subject Areas

Forthcoming articles expand or collapse the "forthcoming articles" section.

• Data Visualization
• Executive Functions in Childhood
• Remote Work
• Find more forthcoming articles...
• Export Citations
• Share This Facebook LinkedIn Twitter

Problem Solving and Decision Making by Emily G. Nielsen , John Paul Minda LAST REVIEWED: 26 June 2019 LAST MODIFIED: 26 June 2019 DOI: 10.1093/obo/9780199828340-0246

Problem solving and decision making are both examples of complex, higher-order thinking. Both involve the assessment of the environment, the involvement of working memory or short-term memory, reliance on long term memory, effects of knowledge, and the application of heuristics to complete a behavior. A problem can be defined as an impasse or gap between a current state and a desired goal state. Problem solving is the set of cognitive operations that a person engages in to change the current state, to go beyond the impasse, and achieve a desired outcome. Problem solving involves the mental representation of the problem state and the manipulation of this representation in order to move closer to the goal. Problems can vary in complexity, abstraction, and how well defined (or not) the initial state and the goal state are. Research has generally approached problem solving by examining the behaviors and cognitive processes involved, and some work has examined problem solving using computational processes as well. Decision making is the process of selecting and choosing one action or behavior out of several alternatives. Like problem solving, decision making involves the coordination of memories and executive resources. Research on decision making has paid particular attention to the cognitive biases that account for suboptimal decisions and decisions that deviate from rationality. The current bibliography first outlines some general resources on the psychology of problem solving and decision making before examining each of these topics in detail. Specifically, this review covers cognitive, neuroscientific, and computational approaches to problem solving, as well as decision making models and cognitive heuristics and biases.

General Overviews

Current research in the area of problem solving and decision making is published in both general and specialized scientific journals. Theoretical and scholarly work is often summarized and developed in full-length books and chapter. These may focus on the subfields of problem solving and decision making or the larger field of thinking and higher-order cognition.

back to top

Users without a subscription are not able to see the full content on this page. Please subscribe or login .

Oxford Bibliographies Online is available by subscription and perpetual access to institutions. For more information or to contact an Oxford Sales Representative click here .

• About Psychology »
• Meet the Editorial Board »
• Abnormal Psychology
• Academic Assessment
• Acculturation and Health
• Action Regulation Theory
• Action Research
• Addictive Behavior
• Adolescence
• Adoption, Social, Psychological, and Evolutionary Perspect...
• Advanced Theory of Mind
• Affective Forecasting
• Affirmative Action
• Ageism at Work
• Allport, Gordon
• Alzheimer’s Disease
• Ambulatory Assessment in Behavioral Science
• Analysis of Covariance (ANCOVA)
• Animal Behavior
• Animal Learning
• Anxiety Disorders
• Art and Aesthetics, Psychology of
• Assessment and Clinical Applications of Individual Differe...
• Attachment in Social and Emotional Development across the ...
• Attention-Deficit/Hyperactivity Disorder (ADHD) in Adults
• Attention-Deficit/Hyperactivity Disorder (ADHD) in Childre...
• Attitudinal Ambivalence
• Attraction in Close Relationships
• Attribution Theory
• Authoritarian Personality
• Bayesian Statistical Methods in Psychology
• Behavior Therapy, Rational Emotive
• Behavioral Economics
• Behavioral Genetics
• Belief Perseverance
• Bereavement and Grief
• Biological Psychology
• Birth Order
• Body Image in Men and Women
• Bystander Effect
• Categorical Data Analysis in Psychology
• Childhood and Adolescence, Peer Victimization and Bullying...
• Clark, Mamie Phipps
• Clinical Neuropsychology
• Clinical Psychology
• Cognitive Consistency Theories
• Cognitive Dissonance Theory
• Cognitive Neuroscience
• Communication, Nonverbal Cues and
• Comparative Psychology
• Competence to Stand Trial: Restoration Services
• Competency to Stand Trial
• Computational Psychology
• Conflict Management in the Workplace
• Conformity, Compliance, and Obedience
• Consciousness
• Coping Processes
• Correspondence Analysis in Psychology
• Counseling Psychology
• Creativity at Work
• Cross-Cultural Psychology
• Cultural Psychology
• Daily Life, Research Methods for Studying
• Data Science Methods for Psychology
• Data Sharing in Psychology
• Death and Dying
• Deceiving and Detecting Deceit
• Defensive Processes
• Depressive Disorders
• Development, Prenatal
• Developmental Psychology (Cognitive)
• Developmental Psychology (Social)
• Diagnostic and Statistical Manual of Mental Disorders (DSM...
• Discrimination
• Dissociative Disorders
• Drugs and Behavior
• Eating Disorders
• Ecological Psychology
• Educational Settings, Assessment of Thinking in
• Effect Size
• Embodiment and Embodied Cognition
• Emerging Adulthood
• Emotional Intelligence
• Empathy and Altruism
• Employee Stress and Well-Being
• Environmental Neuroscience and Environmental Psychology
• Ethics in Psychological Practice
• Event Perception
• Evolutionary Psychology
• Expansive Posture
• Experimental Existential Psychology
• Exploratory Data Analysis
• Eyewitness Testimony
• Eysenck, Hans
• Factor Analysis
• Festinger, Leon
• Five-Factor Model of Personality
• Flynn Effect, The
• Forensic Psychology
• Forgiveness
• Friendships, Children's
• Fundamental Attribution Error/Correspondence Bias
• Gambler's Fallacy
• Game Theory and Psychology
• Geropsychology, Clinical
• Global Mental Health
• Habit Formation and Behavior Change
• Health Psychology
• Health Psychology Research and Practice, Measurement in
• Heider, Fritz
• History of Psychology
• Human Factors
• Humanistic Psychology
• Implicit Association Test (IAT)
• Industrial and Organizational Psychology
• Inferential Statistics in Psychology
• Insanity Defense, The
• Intelligence
• Intelligence, Crystallized and Fluid
• Intercultural Psychology
• Intergroup Conflict
• International Classification of Diseases and Related Healt...
• International Psychology
• Interviewing in Forensic Settings
• Intimate Partner Violence, Psychological Perspectives on
• Introversion–Extraversion
• Item Response Theory
• Law, Psychology and
• Lazarus, Richard
• Learned Helplessness
• Learning Theory
• Learning versus Performance
• LGBTQ+ Romantic Relationships
• Lie Detection in a Forensic Context
• Life-Span Development
• Locus of Control
• Loneliness and Health
• Mathematical Psychology
• Meaning in Life
• Mechanisms and Processes of Peer Contagion
• Media Violence, Psychological Perspectives on
• Mediation Analysis
• Memories, Autobiographical
• Memories, Flashbulb
• Memories, Repressed and Recovered
• Memory, False
• Memory, Human
• Memory, Implicit versus Explicit
• Memory in Educational Settings
• Memory, Semantic
• Meta-Analysis
• Metacognition
• Metaphor, Psychological Perspectives on
• Microaggressions
• Military Psychology
• Mindfulness
• Mindfulness and Education
• Minnesota Multiphasic Personality Inventory (MMPI)
• Money, Psychology of
• Moral Conviction
• Moral Development
• Moral Psychology
• Moral Reasoning
• Nature versus Nurture Debate in Psychology
• Neuroscience of Associative Learning
• Nonergodicity in Psychology and Neuroscience
• Nonparametric Statistical Analysis in Psychology
• Observational (Non-Randomized) Studies
• Obsessive-Complusive Disorder (OCD)
• Occupational Health Psychology
• Olfaction, Human
• Operant Conditioning
• Optimism and Pessimism
• Organizational Justice
• Parenting Stress
• Parenting Styles
• Parents' Beliefs about Children
• Path Models
• Peace Psychology
• Perception, Person
• Performance Appraisal
• Personality and Health
• Personality Disorders
• Personality Psychology
• Person-Centered and Experiential Psychotherapies: From Car...
• Phenomenological Psychology
• Placebo Effects in Psychology
• Play Behavior
• Positive Psychological Capital (PsyCap)
• Positive Psychology
• Posttraumatic Stress Disorder (PTSD)
• Prejudice and Stereotyping
• Pretrial Publicity
• Prisoner's Dilemma
• Problem Solving and Decision Making
• Procrastination
• Prosocial Behavior
• Prosocial Spending and Well-Being
• Psycholinguistics
• Psychological Literacy
• Psychological Perspectives on Food and Eating
• Psychology, Political
• Psychoneuroimmunology
• Psychophysics, Visual
• Psychotherapy
• Psychotic Disorders
• Publication Bias in Psychology
• Reasoning, Counterfactual
• Rehabilitation Psychology
• Relationships
• Reliability–Contemporary Psychometric Conceptions
• Religion, Psychology and
• Replication Initiatives in Psychology
• Research Methods
• Risk Taking
• Role of the Expert Witness in Forensic Psychology, The
• Sample Size Planning for Statistical Power and Accurate Es...
• Schizophrenic Disorders
• School Psychology
• School Psychology, Counseling Services in
• Self, Gender and
• Self, Psychology of the
• Self-Construal
• Self-Control
• Self-Deception
• Self-Determination Theory
• Self-Efficacy
• Self-Esteem
• Self-Monitoring
• Self-Regulation in Educational Settings
• Self-Report Tests, Measures, and Inventories in Clinical P...
• Sensation Seeking
• Sex and Gender
• Sexual Minority Parenting
• Sexual Orientation
• Signal Detection Theory and its Applications
• Simpson's Paradox in Psychology
• Single People
• Single-Case Experimental Designs
• Skinner, B.F.
• Sleep and Dreaming
• Small Groups
• Social Class and Social Status
• Social Cognition
• Social Neuroscience
• Social Support
• Social Touch and Massage Therapy Research
• Somatoform Disorders
• Spatial Attention
• Sports Psychology
• Stanford Prison Experiment (SPE): Icon and Controversy
• Stereotype Threat
• Stereotypes
• Stress and Coping, Psychology of
• Student Success in College
• Subjective Wellbeing Homeostasis
• Taste, Psychological Perspectives on
• Teaching of Psychology
• Terror Management Theory
• Testing and Assessment
• The Concept of Validity in Psychological Assessment
• The Neuroscience of Emotion Regulation
• The Reasoned Action Approach and the Theories of Reasoned ...
• The Weapon Focus Effect in Eyewitness Memory
• Theory of Mind
• Therapy, Cognitive-Behavioral
• Thinking Skills in Educational Settings
• Time Perception
• Trait Perspective
• Trauma Psychology
• Twin Studies
• Type A Behavior Pattern (Coronary Prone Personality)
• Unconscious Processes
• Video Games and Violent Content
• Virtues and Character Strengths
• Women and Science, Technology, Engineering, and Math (STEM...
• Women, Psychology of
• Work Well-Being
• Workforce Training Evaluation
• Wundt, Wilhelm
• Privacy Policy
• Cookie Policy
• Legal Notice
• Accessibility

Powered by:

• [162.248.224.4]
• 162.248.224.4

Last updated 27/06/24: Online ordering is currently unavailable due to technical issues. We apologise for any delays responding to customers while we resolve this. For further updates please visit our website: https://www.cambridge.org/news-and-insights/technical-incident

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings .

Login Alert

• > The Psychology of Problem Solving
• > Recognizing, Defining, and Representing Problems

Book contents

• Frontmatter
• Contributors
• PART I INTRODUCTION
• 1 Recognizing, Defining, and Representing Problems
• 2 The Acquisition of Expert Performance as Problem Solving: Construction and Modification of Mediating Mechanisms through Deliberate Practice
• PART II RELEVANT ABILITIES AND SKILLS
• PART III STATES AND STRATEGIES
• PART IV CONCLUSION AND INTEGRATION

1 - Recognizing, Defining, and Representing Problems

Published online by Cambridge University Press:  05 June 2012

What are the problems that you are currently trying to solve in your life? Most of us have problems that have been posed to us (e.g., assignments from our supervisors). But we also recognize problems on our own (e.g., you might have noticed the need for additional parking space in the city where you work). After identifying the existence of a problem, we must define its scope and goals. The problem of parking space is often seen as a need for more parking lots or parking garages. However, in order to solve this problem creatively, it may be useful to turn it around and redefine it as a problem of too many vehicles requiring a space in which to sit during the workday. In that case, you may be prompted to redefine the problem: You decide to organize a carpool among people who use downtown parking lots and institute a daytime local taxi service using these privately owned vehicles. Thus, you solve the problem not as you originally posed it but as you later reconceived it.

Problem solving does not usually begin with a clear statement of the problem; rather, most problems must be identified in the environment; then they must be defined and represented mentally. The focus of this chapter is on these early stages of problem solving: problem recognition, problem definition, and problem representation.

THE PROBLEM-SOLVING CYCLE

Psychologists have described the problem-solving process in terms of a cycle (Bransford & Stein, 1993; Hayes, 1989; Sternberg, 1986).

Access options

Save book to kindle.

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle .

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service .

• Recognizing, Defining, and Representing Problems
• By Jean E. Pretz , Yale University, Adam J. Naples , Yale University, Robert J. Sternberg , Yale University
• Edited by Janet E. Davidson , Lewis and Clark College, Portland , Robert J. Sternberg , Yale University, Connecticut
• Book: The Psychology of Problem Solving
• Online publication: 05 June 2012
• Chapter DOI: https://doi.org/10.1017/CBO9780511615771.002

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox .

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive .

Cognitive Approach in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

Cognitive psychology is the scientific study of the mind as an information processor. It concerns how we take in information from the outside world, and how we make sense of that information.

Cognitive psychology studies mental processes, including how people perceive, think, remember, learn, solve problems, and make decisions.

Cognitive psychologists try to build cognitive models of the information processing that occurs inside people’s minds, including perception, attention, language, memory, thinking, and consciousness.

Cognitive psychology became of great importance in the mid-1950s. Several factors were important in this:

• Dissatisfaction with the behaviorist approach in its simple emphasis on external behavior rather than internal processes.
• The development of better experimental methods.
• Comparison between human and computer processing of information . Using computers allowed psychologists to try to understand the complexities of human cognition by comparing it with computers and artificial intelligence.

The emphasis of psychology shifted away from the study of conditioned behavior and psychoanalytical notions about the study of the mind, towards the understanding of human information processing using strict and rigorous laboratory investigation.

Summary Table

Theoretical Assumptions

Mediational processes occur between stimulus and response:

The behaviorist approach only studies external observable (stimulus and response) behavior that can be objectively measured.

They believe that internal behavior cannot be studied because we cannot see what happens in a person’s mind (and therefore cannot objectively measure it).

However, cognitive psychologists consider it essential to examine an organism’s mental processes and how these influence behavior.

Cognitive psychology assumes a mediational process occurs between stimulus/input and response/output. 

These are mediational processes because they mediate (i.e., go-between) between the stimulus and the response. They come after the stimulus and before the response.

Instead of the simple stimulus-response links proposed by behaviorism, the mediational processes of the organism are essential to understand.

Without this understanding, psychologists cannot have a complete understanding of behavior.

The mediational (i.e., mental) event could be memory , perception , attention or problem-solving, etc. 

• Perception : how we process and interpret sensory information.
• Attention : how we selectively focus on certain aspects of our environment.
• Memory : how we encode, store, and retrieve information.
• Language : how we acquire, comprehend, and produce language.
• Problem-solving and decision-making : how we reason, make judgments, and solve problems.
• Schemas : Cognitive psychologists assume that people’s prior knowledge, beliefs, and experiences shape their mental processes. 

For example, the cognitive approach suggests that problem gambling results from maladaptive thinking and faulty cognitions, which both result in illogical errors.

Gamblers misjudge the amount of skill involved with ‘chance’ games, so they are likely to participate with the mindset that the odds are in their favour and that they may have a good chance of winning.

Therefore, cognitive psychologists say that if you want to understand behavior, you must understand these mediational processes.

Psychology should be seen as a science:

This assumption is based on the idea that although not directly observable, the mind can be investigated using objective and rigorous methods, similar to how other sciences study natural phenomena. 

Controlled experiments

The cognitive approach believes that internal mental behavior can be scientifically studied using controlled experiments . It uses the results of its investigations to make inferences about mental processes.  Cognitive psychology uses highly controlled laboratory experiments to avoid the influence of extraneous variables . This allows the researcher to establish a causal relationship between the independent and dependent variables. These controlled experiments are replicable, and the data obtained is objective (not influenced by an individual’s judgment or opinion) and measurable. This gives psychology more credibility.

Operational definitions

Cognitive psychologists develop operational definitions to study mental processes scientifically. These definitions specify how abstract concepts, such as attention or memory, can be measured and quantified (e.g., verbal protocols of thinking aloud). This allows for reliable and replicable research findings.

Falsifiability

Falsifiability in psychology refers to the ability to disprove a theory or hypothesis through empirical observation or experimentation. If a claim is not falsifiable, it is considered unscientific.

Cognitive psychologists aim to develop falsifiable theories and models, meaning they can be tested and potentially disproven by empirical evidence.

This commitment to falsifiability helps to distinguish scientific theories from pseudoscientific or unfalsifiable claims.

Empirical evidence

Cognitive psychologists rely on empirical evidence to support their theories and models. They collect data through various methods, such as experiments, observations, and questionnaires, to test hypotheses and draw conclusions about mental processes.

Cognitive psychologists assume that mental processes are not random but are organized and structured in specific ways. They seek to identify the underlying cognitive structures and processes that enable people to perceive, remember, and think.

Cognitive psychologists have made significant contributions to our understanding of mental processes and have developed various theories and models, such as the multi-store model of memory , the working memory model , and the dual-process theory of thinking.

Humans are information processors:

The idea of information processing was adopted by cognitive psychologists as a model of how human thought works.

The information processing approach is based on several assumptions, including:

• Information is processed by a series of systems : The information processing approach proposes that a series of cognitive systems, such as attention, perception, and memory, process information from the environment. Each system plays a specific role in processing the information and passing it along to the next stage.
• Processing systems transform information : As information passes through these cognitive systems, it is transformed or modified in systematic ways. For example, incoming sensory information may be filtered by attention, encoded into memory, or used to update existing knowledge structures.
• Research aims to specify underlying processes and structures : The primary goal of research within the information processing approach is to identify, describe, and understand the specific cognitive processes and mental structures that underlie various aspects of cognitive performance, such as learning, problem-solving, and decision-making.
• Human information processing resembles computer processing : The information processing approach draws an analogy between human cognition and computer processing. Just as computers take in information, process it according to specific algorithms, and produce outputs, the human mind is thought to engage in similar processes of input, processing, and output.

Computer-Mind Analogy

The computer-brain metaphor, or the information processing approach, is a significant concept in cognitive psychology that likens the human brain’s functioning to that of a computer.

This metaphor suggests that the brain, like a computer, processes information through a series of linear steps, including input, storage, processing, and output.

According to this assumption, when we interact with the environment, we take in information through our senses (input).

This information is then processed by various cognitive systems, such as perception, attention, and memory. These systems work together to make sense of the input, organize it, and store it for later use.

During the processing stage, the mind performs operations on the information, such as encoding, transforming, and combining it with previously stored knowledge. This processing can involve various cognitive processes, such as thinking, reasoning, problem-solving, and decision-making.

The processed information can then be used to generate outputs, such as actions, decisions, or new ideas. These outputs are based on the information that has been processed and the individual’s goals and motivations.

This has led to models showing information flowing through the cognitive system, such as the multi-store memory model.

The information processing approach also assumes that the mind has a limited capacity for processing information, similar to a computer’s memory and processing limitations.

This means that humans can only attend to and process a certain amount of information at a given time, and that cognitive processes can be slowed down or impaired when the mind is overloaded.

The Role of Schemas

A schema is a “packet of information” or cognitive framework that helps us organize and interpret information. It is based on previous experience.

Cognitive psychologists assume that people’s prior knowledge, beliefs, and experiences shape their mental processes. They investigate how these factors influence perception, attention, memory, and thinking.

Schemas help us interpret incoming information quickly and effectively, preventing us from being overwhelmed by the vast amount of information we perceive in our environment.

Schemas can often affect cognitive processing (a mental framework of beliefs and expectations developed from experience). As people age, they become more detailed and sophisticated.

However, it can also lead to distortion of this information as we select and interpret environmental stimuli using schemas that might not be relevant.

This could be the cause of inaccuracies in areas such as eyewitness testimony. It can also explain some errors we make when perceiving optical illusions.

1. Behaviorist Critique

B.F. Skinner criticizes the cognitive approach. He believes that only external stimulus-response behavior should be studied, as this can be scientifically measured.

Therefore, mediation processes (between stimulus and response) do not exist as they cannot be seen and measured.

Behaviorism assumes that people are born a blank slate (tabula rasa) and are not born with cognitive functions like schemas , memory or perception .

Due to its subjective and unscientific nature, Skinner continues to find problems with cognitive research methods, namely introspection (as used by Wilhelm Wundt).

2. Complexity of mental experiences

Mental processes are highly complex and multifaceted, involving a wide range of cognitive, affective, and motivational factors that interact in intricate ways.

The complexity of mental experiences makes it difficult to isolate and study specific mental processes in a controlled manner.

Mental processes are often influenced by individual differences, such as personality, culture, and past experiences, which can introduce variability and confounds in research .

3. Experimental Methods 

While controlled experiments are the gold standard in cognitive psychology research, they may not always capture real-world mental processes’ complexity and ecological validity.

Some mental processes, such as creativity or decision-making in complex situations, may be difficult to study in laboratory settings.

Humanistic psychologist Carl Rogers believes that using laboratory experiments by cognitive psychology has low ecological validity and creates an artificial environment due to the control over variables .

Rogers emphasizes a more holistic approach to understanding behavior.

The cognitive approach uses a very scientific method that is controlled and replicable, so the results are reliable.

However, experiments lack ecological validity because of the artificiality of the tasks and environment, so they might not reflect the way people process information in their everyday lives.

For example, Baddeley (1966) used lists of words to find out the encoding used by LTM.

However, these words had no meaning to the participants, so the way they used their memory in this task was probably very different from what they would have done if the words had meaning for them.

This is a weakness, as the theories might not explain how memory works outside the laboratory.

4. Computer Analogy

The information processing paradigm of cognitive psychology views the minds in terms of a computer when processing information.

However, although there are similarities between the human mind and the operations of a computer (inputs and outputs, storage systems, and the use of a central processor), the computer analogy has been criticized.

For example, the human mind is characterized by consciousness, subjective experience, and self-awareness , which are not present in computers.

Computers do not have feelings, emotions, or a sense of self, which play crucial roles in human cognition and behavior.

The brain-computer metaphor is often used implicitly in neuroscience literature through terms like “sensory computation,” “algorithms,” and “neural codes.” However, it is difficult to identify these concepts in the actual brain.

5. Reductionist

The cognitive approach is reductionist as it does not consider emotions and motivation, which influence the processing of information and memory. For example, according to the Yerkes-Dodson law , anxiety can influence our memory.

Such machine reductionism (simplicity) ignores the influence of human emotion and motivation on the cognitive system and how this may affect our ability to process information.

Early theories of cognitive approach did not always recognize physical ( biological psychology ) and environmental (behaviorist approach) factors in determining behavior.

However, it’s important to note that modern cognitive psychology has evolved to incorporate a more holistic understanding of human cognition and behavior.

1. Importance of cognitive factors versus external events

Cognitive psychology emphasizes the role of internal cognitive processes in shaping emotional experiences, rather than solely focusing on external events.

Beck’s cognitive theory suggests that it is not the external events themselves that lead to depression, but rather the way an individual interprets and processes those events through their negative schemas.

This highlights the importance of addressing cognitive factors in the treatment of depression and other mental health issues.

Social exchange theory (Thibaut & Kelly, 1959) emphasizes that relationships are formed through internal mental processes, such as decision-making, rather than solely based on external factors.

The computer analogy can be applied to this concept, where individuals observe behaviors (input), process the costs and benefits (processing), and then make a decision about the relationship (output).

2. Interdisciplinary approach

While early cognitive psychology may have neglected physical and environmental factors, contemporary cognitive psychology has increasingly integrated insights from other approaches.

Cognitive psychology draws on methods and findings from other scientific disciplines, such as neuroscience , computer science, and linguistics, to inform their understanding of mental processes.

This interdisciplinary approach strengthens the scientific basis of cognitive psychology.

Cognitive psychology has influenced and integrated with many other approaches and areas of study to produce, for example, social learning theory , cognitive neuropsychology, and artificial intelligence (AI).

3. Real World Applications

Another strength is that the research conducted in this area of psychology very often has applications in the real world.

By highlighting the importance of cognitive processing, the cognitive approach can explain mental disorders such as depression.

Beck’s cognitive theory of depression argues that negative schemas about the self, the world, and the future are central to the development and maintenance of depression.

These negative schemas lead to biased processing of information, selective attention to negative aspects of experience, and distorted interpretations of events, which perpetuate the depressive state.

By identifying the role of cognitive processes in mental disorders, cognitive psychology has informed the development of targeted interventions.

Cognitive behavioral therapy aims to modify the maladaptive thought patterns and beliefs that underlie emotional distress, helping individuals to develop more balanced and adaptive ways of thinking.

CBT’s basis is to change how people process their thoughts to make them more rational or positive.

Through techniques such as cognitive restructuring, behavioral experiments, and guided discovery, CBT helps individuals to challenge and change their negative schemas, leading to improvements in mood and functioning.

Cognitive behavioral therapy (CBT) has been very effective in treating depression (Hollon & Beck, 1994), and moderately effective for anxiety problems (Beck, 1993). 

Issues and Debates

Free will vs. determinism.

The cognitive approach’s position is unclear. It argues that cognitive processes are influenced by experiences and schemas, which implies a degree of determinism.

On the other hand, cognitive-behavioral therapy (CBT) operates on the premise that individuals can change their thought patterns, suggesting a capacity for free will.

Nature vs. Nurture

The cognitive approach takes an interactionist view of the debate, acknowledging the influence of both nature and nurture on cognitive processes.

It recognizes that while some cognitive abilities, such as language acquisition, may have an innate component (nature), experiences and learning (nurture) also shape the way information is processed.

Holism vs. Reductionism

The cognitive approach tends to be reductionist in its methodology, as it often studies cognitive processes in isolation.

For example, researchers may focus on memory processes without considering the influence of other cognitive functions or environmental factors.

While this approach allows for more controlled study, it may lack ecological validity, as in real life, cognitive processes typically interact and function simultaneously.

Idiographic vs. Nomothetic

The cognitive approach is primarily nomothetic, as it seeks to establish general principles and theories of information processing that apply to all individuals.

It aims to identify universal patterns and mechanisms of cognition rather than focusing on individual differences.

History of Cognitive Psychology

• Wolfgang Köhler (1925) – Köhler’s book “The Mentality of Apes” challenged the behaviorist view by suggesting that animals could display insightful behavior, leading to the development of Gestalt psychology.
• Norbert Wiener (1948) – Wiener’s book “Cybernetics” introduced concepts such as input and output, which influenced the development of information processing models in cognitive psychology.
• Edward Tolman (1948) – Tolman’s work on cognitive maps in rats demonstrated that animals have an internal representation of their environment, challenging the behaviorist view.
• George Miller (1956) – Miller’s paper “The Magical Number 7 Plus or Minus 2” proposed that short-term memory has a limited capacity of around seven chunks of information, which became a foundational concept in cognitive psychology.
• Allen Newell and Herbert A. Simon (1972) – Newell and Simon developed the General Problem Solver, a computer program that simulated human problem-solving, contributing to the growth of artificial intelligence and cognitive modeling.
• George Miller and Jerome Bruner (1960) – Miller and Bruner established the Center for Cognitive Studies at Harvard, which played a significant role in the development of cognitive psychology as a distinct field.
• Ulric Neisser (1967) – Neisser’s book “Cognitive Psychology” formally established cognitive psychology as a separate area of study, focusing on mental processes such as perception, memory, and thinking.
• Richard Atkinson and Richard Shiffrin (1968) – Atkinson and Shiffrin proposed the Multi-Store Model of memory, which divided memory into sensory, short-term, and long-term stores, becoming a key model in the study of memory.
• Eleanor Rosch’s (1970s) research on natural categories and prototypes, which influenced the study of concept formation and categorization.
• Endel Tulving’s (1972) distinction between episodic and semantic memory, which further developed the understanding of long-term memory.
• Baddeley and Hitch’s (1974) proposal of the Working Memory Model, which expanded on the concept of short-term memory and introduced the idea of a central executive.
• Marvin Minsky’s (1975) framework of frames in artificial intelligence, which influenced the understanding of knowledge representation in cognitive psychology.
• David Rumelhart and Andrew Ortony’s (1977) work on schema theory, which described how knowledge is organized and used for understanding and remembering information.
• Amos Tversky and Daniel Kahneman’s (1970s-80s) research on heuristics and biases in decision making, which led to the development of behavioral economics and the study of judgment and decision-making.
• David Marr’s (1982) computational theory of vision, which provided a framework for understanding visual perception and influenced the field of computational cognitive science.
• The development of connectionism and parallel distributed processing (PDP) models in the 1980s, which provided an alternative to traditional symbolic models of cognitive processes.
• Noam Chomsky’s (1980s) theory of Universal Grammar and the language acquisition device, which influenced the study of language and cognitive development.
• The emergence of cognitive neuroscience in the 1990s, which combined techniques from cognitive psychology, neuroscience, and computer science to study the neural basis of cognitive processes.

Atkinson, R. C., & Shiffrin, R. M. (1968). Chapter: Human memory: A proposed system and its control processes. In Spence, K. W., & Spence, J. T. The psychology of learning and motivation (Volume 2). New York: Academic Press. pp. 89–195.

Baddeley, A. D., & Hitch, G. (1974). Working memory. In G. H. Bower (Ed.), The Psychology of Learning and Motivation: Advances in Research and Theory (Vol. 8, pp. 47-89). Academic Press.

Beck, A. T, & Steer, R. A. (1993). Beck Anxiety Inventory Manual. San Antonio: Harcourt Brace and Company.

Chomsky, N. (1986). Knowledge of Language: Its Nature, Origin, and Use . Praeger.

Gazzaniga, M. S. (Ed.). (1995). The Cognitive Neurosciences. MIT Press.

Hollon, S. D., & Beck, A. T. (1994). Cognitive and cognitive-behavioral therapies. In A. E. Bergin & S.L. Garfield (Eds.), Handbook of psychotherapy and behavior change (pp. 428—466) . New York: Wiley.

Köhler, W. (1925). An aspect of Gestalt psychology. The Pedagogical Seminary and Journal of Genetic Psychology, 32(4) , 691-723.

Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information . W. H. Freeman.

Miller, G. A. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review , 63 (2): 81–97.

Minsky, M. (1975). A framework for representing knowledge. In P. H. Winston (Ed.), The Psychology of Computer Vision (pp. 211-277). McGraw-Hill.

Neisser, U (1967). Cognitive psychology . Appleton-Century-Crofts: New York

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

Rosch, E. H. (1973). Natural categories. Cognitive Psychology, 4 (3), 328-350.

Rumelhart, D. E., & McClelland, J. L. (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations. MIT Press.

Rumelhart, D. E., & Ortony, A. (1977). The representation of knowledge in memory. In R. C. Anderson, R. J. Spiro, & W. E. Montague (Eds.), Schooling and the Acquisition of Knowledge (pp. 99-135). Erlbaum.

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185 (4157), 1124-1131.

Thibaut, J., & Kelley, H. H. (1959). The social psychology of groups . New York: Wiley.

Tolman, E. C., Hall, C. S., & Bretnall, E. P. (1932). A disproof of the law of effect and a substitution of the laws of emphasis, motivation and disruption. Journal of Experimental Psychology, 15(6) , 601.

Tolman E. C. (1948). Cognitive maps in rats and men . Psychological Review. 55, 189–208

Tulving, E. (1972). Episodic and semantic memory. In E. Tulving & W. Donaldson (Eds.), Organization of Memory (pp. 381-403). Academic Press.

Wiener, N. (1948). Cybernetics or control and communication in the animal and the machine . Paris, (Hermann & Cie) & Camb. Mass. (MIT Press).

Further Reading

• Why Your Brain is Not a Computer
• Cognitive Psychology Historial Development

Related Articles

Soft Determinism In Psychology

Branches of Psychology

Cognitive Psychology

Automatic Processing in Psychology: Definition & Examples

Controlled Processing in Psychology: Definition & Examples

How Ego Depletion Can Drain Your Willpower

What is the Default Mode Network?

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 counsellor 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.

• Find Counselling
• Find a Support Group
• Find Online Therapy
• Richmond - Tweed
• Newcastle - Maitland
• Canberra - ACT
• Sunshine Coast
• Asperger's
• Bipolar Disorder
• Chronic Pain
• Eating Disorders
• Passive Aggression
• Personality
• Goal Setting
• Positive Psychology
• Stopping Smoking
• Low Sexual Desire
• Relationships
• Child Development
• Self Tests NEW
• Therapy Center
• Diagnosis Dictionary
• Types of Therapy

At any moment, someone’s aggravating behavior or our own bad luck can set us off on an emotional spiral that could derail our entire day. Here’s how we can face triggers with less reactivity and get on with our lives.

• Emotional Intelligence
• Gaslighting
• Affective Forecasting
• Neuroscience

• All Access Pass
• PLR Articles
• PLR Courses
• PLR Social Media

Grab Our Free eBook to Learn How to Grow Your Wellness Business Exponentially!

What is cognitive psychology (a definition)​, why is cognitive psychology important​.

• Psychotherapy: Cognitive psychology led to the development of cognitive behavior therapy , one of the most widely used types of therapy used today. It uses a combination of behavioral and cognitive techniques focusing on examining and modifying our unhelpful thought processes and behaviors (Gaudiano, 2008).  
• Education: Because of its focus on learning, attention, and memory, cognitive psychology has made important contributions to education. For example, research shows that teachers should present information in a way that creates meaning by connecting it to existing knowledge, thus making it easier to remember (Regehr & Normal, 1996).
• Decision-making: We make decisions according to our perceptions, attention, and memory—all subjects that cognitive psychology studies. Cognitive psychology has also made significant contributions to understanding how and why we use biases (stereotyping) and heuristics (simple rules of thumb) in decision-making. Understanding our use of biases and heuristics helps us develop strategies to overcome them and make more informed decisions. This has practical applications in relationships, business, law, economics, and public policy. 
• Artificial Intelligence (AI): Studying our mental processes allows researchers to design AI systems that mimic human intelligence which makes them better at interacting with people. To create even more effective AI, researchers also aim to recreate the way humans process subjective mental experiences like emotion  (Zhao et al., 2022). In addition, discoveries from cognitive psychology help researchers create more intuitive interfaces that align with our cognitive capabilities.  ​

History of Cognitive Psychology

• In 1870, German psychologist Wilhelm Wundt was the first to approach psychology as a science.  Before then, the mind was considered from a philosophical standpoint. Wundt attempted to investigate the mind through introspection , by systematically observing conscious experiences, the same way scientists in other fields observe things in the world (Braisby & Galletly, 2012). By observing patterns in reported experiences, researchers believed they were able to determine what was going on in the mind. 
• Edward Titchener, a student of Wundt, developed this further by introducing the concept of structuralism. Structuralism attempts to break our experiences down into basic elements to analyze them. Titchener divided experiences into sensations (sights, sounds, tastes), images (thoughts, ideas), and affections (emotions).
• Behaviorism Dominance: From the 1920s to the 1950s, behaviorism (or behavioral psychology ) was the dominant approach to studying behavior. This theory arose out of the criticism that introspection wasn’t scientific enough because the inner workings of the mind couldn’t be observed. By contrast, behaviorism focused on observable behavior. It claims that all behavior can be explained by examining positive reinforcements (rewards) and punishments. Thoughts, memories, and emotions were deemed “unscientific”. 
• Cognitive Revolution: In the 1950s and 60s, researchers returned to studying mental processes. This came about for several reasons. First, while behaviorism does a good job of describing behavior, it’s not so good at explaining it. Also, behaviorism was criticized because it didn’t explain complex cognitive processes and subjective experiences. Another major influencer that sparked the return to cognitive theory was renowned linguist Noam Chomsky. He argued that language acquisition couldn’t be explained only with behaviorist concepts and that much of it is innate rather than learned. 
• Mind as Computer: The development of computer science in the 1960s and 70s contributed to seeing the human mind as an information processing system and comparing it to a computer. This is often referred to as the Computational Theory of Mind (CTM). Just like a computer, the mind has inputs (through the senses), software or algorithms (the mind), hardware and memory (the brain), and outputs (behaviors).
• Cognitive Neuroscience: Starting in the 1980s, the use of brain imaging techniques such as magnetic resonance imaging (MRI) allowed researchers to combine cognitive psychology with neuroscience . This technology allows researchers to observe the brain during cognitive processes. This led to the new field of cognitive neuroscience that explores the relationship of our mental processes with brain activity. 
• Contemporary Subfields: Cognitive psychology continues to evolve and has recently led to several subfields. These include attention, perception, memory, language, decision-making, and problem-solving. We’ll dive into some of these later on.

Cognitive Psychology Theories

• Cognitive Load Theory (CLT): This theory is about the relationship between working memory and long-term memory. It states that working memory has a limited capacity and once that capacity has been reached, we become overloaded and learning suffers. Things that make us feel overloaded include information complexity, irrelevant information that is distracting, and efforting to connect new information to things we already know. This theory has important implications for education and for presenting new information effectively (Bannert, 2002).
• Cognitive Development: Developed by Jean Piaget in 1955, cognitive development theory says that children go through several stages (and sub-stages) as their thinking processes develop. Cognitive development theories attempt to explain the mechanisms driving how and why children’s thinking and perceiving change as they grow and mature. Many preschool and primary school programs are based on this model.
• Information Processing Theory : This theory describes our mind as a computer. It sees the brain as a processor that takes inputs (from senses, attention, and memory) and produces outputs (behaviors) like a computer. 
• Theory of Mind : This is the ability to infer what is going on in someone else’s mind, the ability to understand that someone else has different desires and emotions from yourself. This helps us to understand and predict others’ behavior.
• Dual Process Theory: This theory says that we have two different systems of thought. One is quick, unconscious, intuitive, and based on associations and emotions. The other is slow, thoughtful, conscious , and based on reason (Gronchi & Giovannelli, 2018). This theory explains how we can make decisions based on both fast, instinctive thinking and slower, deliberative reason.  ​

Cognitive Psychology Concepts

• Cognitive Development: Our cognitive abilities develop and change throughout childhood, going through different stages.
• Mental Models or Schemas: This refers to how we create mental frameworks or representations of the world to understand and interact with our environment. They serve as filters through which we perceive and interpret information, influencing our thoughts, actions, and decisions. Mental models are usually subconscious  and automatic. 
• Cognitive Biases: We are all vulnerable to systematic errors in thinking when interpreting information. We tend to oversimplify things, using generalizations and stereotypes, so we can process a lot of information quickly and easily.
• Heuristics: Related to biases, heuristics are “rules of thumb”, shortcuts that allow us to make quick, although sometimes poor decisions.

Cognitive Psychology Approach​

• The experimental approach focuses on behavioral data gained through structured experiments. I’ll describe some experiments next.
• The computational psychology approach uses computer and mathematical models that are designed to mimic human behavior in cognitive tasks.
• Cognitive neuroscience looks at brain measurements and how they relate to thinking and perceiving.

Examples of Cognitive Psychology Research

• False memory formation: A study from 1974 showed that our memory is affected by the way a question is asked. Participants were shown a video of a car accident and then asked questions about what happened, like how fast the cars were going. When asked “How fast were the cars going when they smashed into each other?” they gave consistently higher speed estimates than when another word was used instead of smashed, such as hit, contacted, or collided. Also, when the word “smashed” was used, more people answered “yes” to the question “Was there broken glass?” even though there wasn’t any (Loftus & Palmer, 1974).
• Biases and heuristics: A lot of research has been done on how and why our minds use biases, or stereotypes, and heuristics which are mental shortcuts. Some of the first research was done in 1974 by Tversky and Kahneman. They found that we use mental shortcuts or “rules of thumb” to make decisions easier when information is limited or when situations are complex. The same goes for biases. When we don’t have the time or the information required to make a judgment about someone, we tend to use biases. Both biases and heuristics can lead to wrong conclusions and poor decisions (Tversky & Kahneman, 1974).
• Dual-Task Performance: Have you ever felt like you missed parts of a lecture while taking notes? A study performed in 1994 gave participants two simple tasks to perform at the same time. Something like reading and watching a visual display. Unsurprisingly, performance was worse when doing both tasks as compared to doing each task individually (Pashler, 1994). This has implications for divided attention. (Ahem…texting and driving.)
• Task Switching: Similar to the above, a 2003 study looked at the cognitive processes involved in switching between tasks. Results indicate that our responses are slower and more prone to errors after switching tasks (Monsell, 2003). I definitely feel a delay in my ability to focus right after changing tasks. This has some important implications considering how often we switch between tasks every day. (Popping back and forth between computer work to email to chat to phone to interacting with people around you, etc…).
• The Stroop Effect: This classic study looked at the interference between automatic and controlled, conscious brain processes. Study participants were shown color words that were printed in a color different from the word. For example, the word “red” is printed in blue ink. Participants were instructed to name the color of the ink, rather than reading the word, and reaction times were measured. It’s harder than it sounds! Reaction times were slower and participants were more error-prone (MacLeod, 2015). This is because it’s difficult to “turn off” the unconscious impulse to read the word. You can try out the Stroop experiment here .
• The Incubation Effect: Have you ever had this experience? You’re struggling to figure out a problem, so you take a break and do something completely different, like go for a walk or listen to music. Then you come back to the problem, and the answer just comes to you. This is called the “incubation effect”. Studies show that we are more likely to solve problems when we have this incubation period (Smith & Blankenship, 1989). Researchers believe this is because the unconscious mind continues to make connections and process information when the conscious mind is focused on something else. This suggests that allowing the mind to wander can sometimes lead to unexpected insights.

Cognitive Psychology and Attention

Cognitive psychology and memory, cognitive psychology and perception.

Cognitive Psychology & Neuroscience

Cognitive Psychology Strengths and Weaknesses

• Scientific approach: Cognitive psychology is grounded in the scientific method and uses rigorous research methods to study mental processes. This approach allows for the development of reliable theories and the testing of hypotheses.
• Clinical experiments: Connected to the scientific approach, cognitive psychology uses controlled laboratory experiments which give researchers a high level of control which means that measurements and results are more reliable.
• Practical applications: It has many practical applications, some of which were described above. For example, it has led to improvements in psychotherapy, education, and technology design. Understanding our cognitive processes has helped us learn how to improve memory, problem-solving, and decision-making.
• Effective for treating anxiety: Treating anxiety is one of cognitive psychology’s most widely used practical applications. Anxiety is one of the leading mental health issues today, affecting about 18% of U.S. adults. Cognitive-behavioral therapy has shown to be an effective treatment for many (APA, 2016).
• Too much control? Although the control in a laboratory experiment gives precise results, it may not apply to “real-life” situations where there are many outside variables that affect our thinking and behavior. 
• Reductionism: Researchers tend to reduce complex cognitive processes into small components and study them individually. This may overlook the holistic nature of how the mind works, potentially missing how the different processes connect and work together. The mind is complicated!
• Limited ability to observe: Since mental processes are internal and subjective, researchers are unable to directly observe what is going on in someone’s mind. They rely on self-reports of research participants which can be unreliable (Alahmad, 2020). However, this is getting better with more advanced brain imaging devices.
• Neglects some influences: Cognitive psychology often doesn’t take into account some influences on behavior such as social, cultural, and educational factors (Alahmad, 2020). These factors can also affect how you think and process information.

Video: What is Cognitive Psychology:

Articles Related to  Cognitive Psychology​

• Definition of Psychology: Types, Concepts, & Examples
• Minimizing: Definition in Psychology, Theory, & Examples
• The Halo Effect: Definition, Examples, & Theory
• ​ Social Psychology: Definition, Theory, & Examples​
• ​ Neuropsychology: Definition, Research, & History ​​ ​​ ​​ ​​​​ ​​ ​

Books Related to Cognitive Psychology​

• Cognitive Psychology: Connecting Mind, Research and Everyday Experience
• Cognitive Behavioral Therapy - An Alternative Treatment for Greater Personal Happiness and Contentment: CBT Anxiety & Cognitive Psychology Series
• Cognitive Psychology: Theory, Process, and Methodology

Final Thoughts on Cognitive Psychology​

Don't forget to grab our free ebook to learn how to grow your wellness business exponentially.

• Alahmad, M. (2020). Strengths and weaknesses of cognitive theory. ​ Budapest International Research and Critics Institute-Journal , 3 (3), 1584-1593.
• APA. (2016). Beyond worry: How psychologists help with anxiety disorders . American Psychological Association. 
• American Psychological Association. (2023). Apa Dictionary of Psychology . American Psychological Association. https://dictionary.apa.org/perception
• Baddeley, A. (1988). Cognitive psychology and human memory. Trends in neurosciences , 11 (4), 176-181.
• Bannert, M. (2002). Managing cognitive load—recent trends in cognitive load theory. Learning and instruction , 12 (1), 139-146.
• Bassett, D. S., & Gazzaniga, M. S. (2011). Understanding complexity in the human brain. Trends in cognitive sciences , 15 (5), 200-209.
• Braisby, N., & Gellatly, A. (Eds.). (2012). Cognitive psychology . Oxford University Press.
• Forstmann, B. U., Wagenmakers, E. J., Eichele, T., Brown, S., & Serences, J. T. (2011). Reciprocal relations between cognitive neuroscience and formal cognitive models: opposites attract? . Trends in cognitive sciences , 15 (6), 272-279. ​
• Gaudiano, B. A. (2008). Cognitive-behavioural therapies: achievements and challenges. BMJ Ment Health , 11 (1), 5-7.
• Gronchi, G., & Giovannelli, F. (2018). Dual process theory of thought and default mode network: A possible neural foundation of fast thinking. Frontiers in psychology , 9 , 1237
• Lachter, J., Forster, K. I., & Ruthruff, E. (2004). Forty-five years after Broadbent (1958): still no identification without attention. Psychological review , 111 (4), 880.
• MacLeod, C. M. (2015). The stroop effect. Encyclopedia of color science and technology , 1-6.
• Loftus, E. F., & Palmer, J. C. (1974). Reconstruction of automobile destruction: An example of the interaction between language and memory. Journal of verbal learning and verbal behavior , 13 (5), 585-589.
• Monsell, S. (2003). Task switching. Trends in cognitive sciences , 7 (3), 134-140.
• Pashler, H. (1994). Dual-task interference in simple tasks: data and theory. Psychological bulletin , 116 (2), 220.
• Regehr, G., & Norman, G. R. (1996). Issues in cognitive psychology: implications for professional education. Academic Medicine , 71 (9), 988-1001
• Smith, S. M., & Blankenship, S. E. (1989). Incubation effects. Bulletin of the Psychonomic Society , 27 (4), 311-314.
• Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and Biases. Science , 185 (4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124  ​
• Happiness ​
• Stress Management
• Self-Confidence
• Manifestation
• ​ All Articles...
• All-Access Pass​
• ​​PLR Content Packages
• PLR Courses ​

Cognitive Psychology

• Precursors to Cognitive Psychology
• Emergence of Cognitive Psychology
• Research Methods
• Brain and Cognition: Neurons
• Brain and Cognition: Brain Structure
• Introduction to perception
• Top-down and bottom-up theories of perception
• Problem Solving
• Problem solving and insight
• Problem solving in animals
• Welcome page
• What is a Wiki Site?
• How to edit pages?
• How to join this site?
• Site members
• Recent changes
• List all pages
• Site Manager

It seems you have no tags attached to pages. To attach a tag simply click on the tags button at the bottom of any page.

Add a new page

edit this panel

What is problem solving?

A problem arises when we need to overcome some obstacle in order to get from our current state to a desired state. Problem solving is the process that an organism implements in order to try to get from the current state to the desired state.

An historical review of approaches to problem solving

The behaviourist approach.

Behaviourist researchers argued that problem solving was a reproductive process; that is, organisms faced with a problem applied behaviour that had been successful on a previous occasion. Successful behaviour was itself believed to have been arrived at through a process of trial-and-error. In 1911 Edward Thorndike had developed his law of effect after observing cats discover how to escape from the cage into which he had placed them. This greatly influenced the behaviourist view of problem solving:

The Gestalt approach

By contrast, Gestalt psychologists argued that problem solving was a productive process. In particular, in the process of thinking about a problem individuals sometimes "restructured" their representation of the problem, leading to a flash of insight that enabled them to reach a solution. In The Mentality of Apes (1915) Wolfgang Köhler described a series of studies with apes in which the animals appeared to demonstrate insight in problem solving situations. A description of these studies, with photographs, can be found here .

The Gestalt psychologists described several aspects of thought that acted as barriers to successful problem solving. One of these was called the Einstellung effect , now more commonly referred to as mental set or entrenchment . This occurs when a problem solver becomes fixated on applying a strategy that has previously worked, but is less helpful for the current problem. Luchins (1942) reported a study in which people had to use three jugs of differing capacity (measured in cups) to measure out a required amount of water (given by the experimenter). Some people were given a series of "practice" trials prior to attempting the critical problems. These practice problems could be solved by filling Jug B, then tipping water from Jug B into Jug A until it is filled, and then twice using the remainging contents of Jug A to fill Jug C. Expressed as a formula, this is B - A - 2C. However, although this formula could be applied to the subsequent "critical" problems, these also had simpler solutions, such as A - C. People who had experienced the practice problems mostly tried to apply the more complex solution to these later problems, unlike people who had not experienced the earlier problems (who mostly found the simpler solutions).

Another barrier to problem solving is functional fixedness , whereby individuals fail to recognize that objects can be used for a purpose other than that they were designed for. Maier (1930) illustrated this with his two string problem .

For a real life example of overcoming fuctional fixedness, see: Overcoming functional fixedness: Apollo 13

Questions : What do you think of Köhler's claim that his apes had demonstrated insight? What proportion of Maier's participants spontaneously found the solution before getting any kind of hint? What did Maier do that led some people to get the correct solution? (these questions require some research)

The cognitive approach to problem solving

Problem space theory.

In 1972, Allen Newell and Herbert Simon published the book Human Problem Solving , in which they outlined their problem space theory of problem solving. In this theory, people solve problems by searching in a problem space . The problem space consists of the initial (current) state, the goal state, and all possible states in between. The actions that people take in order to move from one state to another are known as operators . Consider the eight puzzle . The problem space for the eight puzzle consists of the initial arrangement of tiles, the desired arrangement of tiles (normally 1, 2, 3….8), and all the possible arrangements that can be arrived at in between. However, problem spaces can be very large so the key issue is how people navigate their way through the possibilities, given their limited working memory capacities. In other words, how do they choose operators? For many problems we possess domain knowledge that helps us decide what to do. But for novel problems Newell and Simon proposed that operator selection is guided by cognitive short-cuts, known as heuristics . The simplest heuristic is repeat-state avoidance or backup avoidance 1 , whereby individuals prefer not to take an action that would take them back to a previous problem state. This is unhelpful when a person has taken an inappropriate action and actually needs to go back a step or more.

Another heuristic is difference reduction , or hill-climbing , whereby people take the action that leads to the biggest similarity between current state and goal state. Before reading further, see if you can solve the following problem:

In the hobbits and orcs problem the task instructions are as follows:

On one side of a river are three hobbits and three orcs. They have a boat on their side that is capable of carrying two creatures at a time across the river. The goal is to transport all six creatures across to the other side of the river. At no point on either side of the river can orcs outnumber hobbits (or the orcs would eat the outnumbered hobbits). The problem, then, is to find a method of transporting all six creatures across the river without the hobbits ever being outnumbered.

The solution to this problem, together with an explanation of how difference reduction is often applied, can be found by clicking here .

A more sophisticated heuristic is means-ends analysis . Like difference reduction, the means-ends analysis heuristic looks for the action that will lead to the greatest reduction in difference between the current state and goal state, but also specifies what to do if that action cannot be taken. Means-ends analysis can be specified as follows 2 :

• Compare the current state with the goal state. If there is no difference between them, the problem is solved.
• If there is a difference between the current state and the goal state, set a goal to solve that difference. If there is more than one difference, set a goal to solve the largest difference.
• Select an operator that will solve the difference identified in Step 2.
• If the operator can be applied, apply it. If it cannot, set a new goal to reach a state that would allow the application of the operator.
• Return to Step 1 with the new goal set in Step 4.

The application of means-ends analysis can be illustrated with the Tower of Hanoi problem .

In 1957 Newell and Simon developed the General Problem Solver , a computer program that used means-ends analysis to find solutions to a range of well-defined problems - problems that have clear paths (if not easy ones) to a goal state. In their 1972 book on problem solving they reported the verbal protocols of participants engaged in problem solving, which showed a close match between the steps that they took and those taken by the General Problem Solver.

Acquiring operators

There are three ways in which operators can be acquired:

• Trial-and-error. As noted above, this formed the basis of the behaviourist account of problem solving.
• Direct instruction.
• Analogies. Analogies are examples from one domain (the source), whose elements can be used to aid problem solving in another domain (the target). However, novices often struggle to spot analogies, as described here .

Next: Problem solving and insight

Cognitive psychology

Curator: Barbara Anne Dosher

Zhong-Lin Lu

Eugene M. Izhikevich

Robert P. O'Shea

Benjamin Bronner

Tobias Denninger

Max Coltheart

Dr. Zhong-Lin Lu , Neuroscience Graduate Program, University of Southern California, Los Angeles, CA

Dr. Barbara Anne Dosher , Department of Cognitive Science, University of California, Irvine

Cognitive psychology is the scientific investigation of human cognition, that is, all our mental abilities – perceiving, learning, remembering, thinking, reasoning, and understanding. The term “cognition” stems from the Latin word “ cognoscere” or "to know". Fundamentally, cognitive psychology studies how people acquire and apply knowledge or information. It is closely related to the highly interdisciplinary cognitive science and influenced by artificial intelligence, computer science, philosophy, anthropology, linguistics , biology, physics, and neuroscience .

Cognitive psychology in its modern form incorporates a remarkable set of new technologies in psychological science. Although published inquiries of human cognition can be traced back to Aristotle’s ‘’De Memoria’’ (Hothersall, 1984), the intellectual origins of cognitive psychology began with cognitive approaches to psychological problems at the end of the 1800s and early 1900s in the works of Wundt, Cattell, and William James (Boring, 1950).

Cognitive psychology declined in the first half of the 20th century with the rise of “ behaviorism " –- the study of laws relating observable behavior to objective, observable stimulus conditions without any recourse to internal mental processes (Watson, 1913; Boring, 1950; Skinner, 1950). It was this last requirement, fundamental to cognitive psychology, that was one of behaviorism's undoings. For example, lack of understanding of the internal mental processes led to no distinction between memory and performance and failed to account for complex learning (Tinklepaugh, 1928; Chomsky, 1959). These issue led to the decline of behaviorism as the dominant branch of scientific psychology and to the “Cognitive Revolution”.

The Cognitive Revolution began in the mid-1950s when researchers in several fields began to develop theories of mind based on complex representations and computational procedures (Miller, 1956; Broadbent, 1958; Chomsky, 1959; Newell, Shaw, & Simon, 1958). Cognitive psychology became predominant in the 1960s (Tulving, 1962; Sperling, 1960). Its resurgence is perhaps best marked by the publication of Ulric Neisser’s book, ‘’Cognitive Psychology’’, in 1967. Since 1970, more than sixty universities in North America and Europe have established cognitive psychology programs.

Assumptions

Cognitive psychology is based on two assumptions: (1) Human cognition can at least in principle be fully revealed by the scientific method, that is, individual components of mental processes can be identified and understood, and (2) Internal mental processes can be described in terms of rules or algorithms in information processing models. There has been much recent debate on these assumptions (Costall and Still, 1987; Dreyfus, 1979; Searle, 1990).

Very much like physics, experiments and simulations/modelling are the major research tools in cognitive psychology. Often, the predictions of the models are directly compared to human behaviour. With the ease of access and wide use of brain imaging techniques, cognitive psychology has seen increasing influence of cognitive neuroscience over the past decade. There are currently three main approaches in cognitive psychology: experimental cognitive psychology, computational cognitive psychology, and neural cognitive psychology. Experimental cognitive psychology treats cognitive psychology as one of the natural sciences and applies experimental methods to investigate human cognition. Psychophysical responses, response time, and eye tracking are often measured in experimental cognitive psychology. Computational cognitive psychology develops formal mathematical and computational models of human cognition based on symbolic and subsymbolic representations, and dynamical systems . Neural cognitive psychology uses brain imaging (e.g., EEG , MEG , fMRI , PET, SPECT, Optical Imaging) and neurobiological methods (e.g., lesion patients) to understand the neural basis of human cognition. The three approaches are often inter-linked and provide both independent and complementary insights in every sub-domain of cognitive psychology.

Sub-domains of Cognitive Psychology

Traditionally, cognitive psychology includes human perception , attention , learning , memory , concept formation , reasoning , judgment and decision-making , problem solving , and language processing . For some, social and cultural factors, emotion , consciousness , animal cognition , evolutionary approaches have also become part of cognitive psychology.

• Perception: Those studying perception seek to understand how we construct subjective interpretations of proximal information from the environment. Perceptual systems are composed of separate senses (e.g., visual, auditory, somatosensory) and processing modules (e.g., form, motion; Livingston & Hubel, 1988; Ungerleider & Mishkin, 1982; Julesz, 1971) and sub-modules (e.g., Lu & Sperling, 1995) that represent different aspects of the stimulus information. Current research also focuses on how these separate representations and modules interact and are integrated into coherent percepts. Cognitive psychologists have studied these properties empirically with psychophysical methods and brain imaging. Computational models, based on physiological principles, have been developed for many perceptual systems (Grossberg & Mingolla, 1985; Marr, 1982; Wandell, 1995).
• Attention : Attention solves the problem of information overload in cognitive processing systems by selecting some information for further processing, or by managing resources applied to several sources of information simultaneously (Broadbent, 1957; Posner, 1980; Treisman, 1969). Empirical investigation of attention has focused on how and why attention improves performance, or how the lack of attention hinders performance (Posner, 1980; Weichselgartner & Sperling, 1987; Chun & Potter, 1995; Pashler, 1999). The theoretical analysis of attention has taken several major approaches to identify the mechanisms of attention: the signal-detection approach (Lu & Dosher, 1998) and the similarity-choice approach (Bundesen, 1990; Logan, 2004). Related effects of biased competition have been studied in single cell recordings in animals (Reynolds, Chelazzi, & Desimone, 1999). Brain imaging studies have documented effects of attention on activation in early visual cortices, and have investigated the networks for attention control (Kanwisher & Wojciulik, 2000).
• Learning: Learning improves the response of the organism to the environment. Cognitive psychologists study which new information is acquired and the conditions under which it is acquired. The study of learning begins with an analysis of learning phenomena in animals (i.e., habituation, conditioning , and instrumental, contingency, and associative learning) and extends to learning of cognitive or conceptual information by humans (Kandel, 1976; Estes, 1969; Thompson, 1986). Cognitive studies of implicit learning emphasize the largely automatic influence of prior experience on performance, and the nature of procedural knowledge (Roediger, 1990). Studies of conceptual learning emphasize the nature of the processing of incoming information, the role of elaboration, and the nature of the encoded representation (Craik, 2002). Those using computational approaches have investigated the nature of concepts that can be more easily learned, and the rules and algorithms for learning systems (Holland, Holyoak, Nisbett, & Thagard, 1986). Those using lesion and imaging studies investigate the role of specific brain systems (e.g., temporal lobe systems) for certain classes of episodic learning, and the role of perceptual systems in implicit learning (Tulving, Gordon Hayman, & MacDonald, 1991; Gabrieli, Fleischman, Keane, Reminger, & Morell, 1995; Grafton, Hazeltine, & Ivry, 1995).
• Memory : The study of the capacity and fragility of human memory is one of the most developed aspects of cognitive psychology. Memory study focuses on how memories are acquired, stored, and retrieved. Memory domains have been functionally divided into memory for facts, for procedures or skills, and working and short-term memory capacity. The experimental approaches have identified dissociable memory types (e.g., procedural and episodic; Squire & Zola, 1996) or capacity limited processing systems such as short-term or working memory (Cowan, 1995; Dosher, 1999). Computational approaches describe memory as propositional networks, or as holographic or composite representations and retrieval processes (Anderson, 1996, Shiffrin & Steyvers, 1997). Brain imaging and lesion studies identify separable brain regions active during storage or retrieval from distinct processing systems (Gabrieli, 1998).
• Concept Formation: Concept or category formation refers to the ability to organize the perception and classification of experiences by the construction of functionally relevant categories. The response to a specific stimulus (i.e., a cat) is determined not by the specific instance but by classification into the category and by association of knowledge with that category (Medin & Ross, 1992). The ability to learn concepts has been shown to depend upon the complexity of the category in representational space, and by the relationship of variations among exemplars of concepts to fundamental and accessible dimensions of representation (Ashby, 2000). Certain concepts largely reflect similarity structures, but others may reflect function, or conceptual theories of use (Medin, 1989). Computational models have been developed based on aggregation of instance representations, similarity structures and general recognition models, and by conceptual theories (Barsalou, 2003). Cognitive neuroscience has identified important brain structures for aspects or distinct forms of category formation (Ashby, Alfonso-Reese, Turken, and Waldron, 1998).
• Judgment and decision: Human judgment and decision making is ubiquitous – voluntary behavior implicitly or explicitly requires judgment and choice. The historic foundations of choice are based in normative or rational models and optimality rules, beginning with expected utility theory (von Neumann & Morgenstern 1944; Luce, 1959). Extensive analysis has identified widespread failures of rational models due to differential assessment of risks and rewards (Luce and Raiffa, 1989), the distorted assessment of probabilities (Kahneman & Tversky, 1979), and the limitations in human information processing (i.e., Russo & Dosher, 1983). New computational approaches rely on dynamic systems analyses of judgment and choice (Busemeyer & Johnson, 2004), and Bayesian belief networks that make choices based on multiple criteria (Fenton & Neil, 2001) for more complex situations. The study of decision making has become an active topic in cognitive neuroscience (Bechara, Damasio and Damasio, 2000).
• ‘’’Reasoning:’’’ Reasoning is the process by which logical arguments are evaluated or constructed. Original investigations of reasoning focused on the extent to which humans correctly applied the philosophically derived rules of inference in deduction (i.e., A implies B; If A then B), and the many ways in which humans fail to appreciate some deductions and falsely conclude others. These were extended to limitations in reasoning with syllogisms or quantifiers (Johnson-Laird, Byne and Schaeken, 1992; Rips and Marcus, 1977). Inductive reasoning, in contrast, develops a hypothesis consistent with a set of observations or reasons by analogy (Holyoak and Thagard, 1995). Often reasoning is affected by heuristic judgments, fallacies, and the representativeness of evidence, and other framing phenomena (Kahneman, Slovic, Tversky, 1982). Computational models have been developed for inference making and analogy (Holyoak and Thagard, 1995), logical reasoning (Rips and Marcus, 1977), and Bayesian reasoning (Sanjana and Tenenbaum, 2003).
• Problem Solving: The cognitive psychology of problem solving is the study of how humans pursue goal directed behavior. The computational state-space analysis and computer simulation of problem solving of Newell and Simon (1972) and the empirical and heuristic analysis of Wickelgren (1974) together have set the cognitive psychological approach to problem solving. Solving a problem is conceived as finding operations to move from the initial state to a goal state in a problem space using either algorithmic or heuristic solutions. The problem representation is critical in finding solutions (Zhang, 1997). Expertise in knowledge rich domains (i.e., chess) also depends on complex pattern recognition (Gobet & Simon, 1996). Problem solving may engage perception, memory, attention, and executive function, and so many brain areas may be engaged in problem solving tasks, with an emphasis on pre-frontal executive functions.
• Language Processing: While linguistic approaches focus on the formal structures of languages and language use (Chomsky, 1965), cognitive psychology has focused on language acquisition, language comprehension, language production, and the psychology of reading (Kintsch 1974; Pinker, 1994; Levelt, 1989). Psycholinguistics has studied encoding and lexical access of words, sentence level processes of parsing and representation, and general representations of concepts, gist, inference, and semantic assumptions. Computational models have been developed for all of these levels, including lexical systems, parsing systems, semantic representation systems, and reading aloud (Seidenberg, 1997; Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001; Just, Carpenter, and Woolley, 1982; Thorne, Bratley & Dewar, 1968; Schank and Abelson, 1977; Massaro, 1998). The neuroscience of language has a long history in the analysis of lesions (Wernicke, 1874; Broca, 1861), and has also been extensively studied with cognitive imaging (Posner et al, 1988).

Applications

Cognitive psychology research has produced an extensive body of principles, representations, and algorithms. Successful applications range from custom-built expert systems to mass-produced software and consumer electronics: (1) Development of computer interfaces that collaborate with users to meet their information needs and operate as intelligent agents, (2) Development of a flexible information infrastructure based on knowledge representation and reasoning methods, (3) Development of smart tools in the financial industry, (4) Development of mobile, intelligent robots that can perform tasks usually reserved for humans, (5) Development of bionic components of the perceptual and cognitive neural system such as cochlear and retinal implants.

• Anderson, J.R. (1996) The architecture of Cognition. Mahwah, NJ: L. Erlbaum Associates.
• Ashby, F. G. (2000) A stochastic version of general recognition theory. Journal of Mathematical Psychology 44: 310-329.
• Ashby,F. G., Alfonso-Reese, L. A., Turken, A. U., & Waldron, E. M. (1998) A neuropsychological theory of multiple systems in category learning. Psychological Review 105: 442-481.
• Barsalou, L.W. (2003) Abstraction in perceptual symbol systems. Philosophical Transactions of the Royal Society of London: Biological Sciences 358:.
• Bechara, A., Damasio, H. and Damasio, A. (2000) Emotion , decision making and the orbitofrontal cortex. Cerebral Cortex 10: 295-307.
• Boring, E. G. (1950). A history of experimental psychology (2nd ed.). New York: Appleton-Century-Crofts.
• Broadbent, D. E. (1957) A mechanical model for human attention and immediate memory. Psychological Review 64: 205-215.
• Bundesen, C. (1990) A Theory Of Visual-Attention. Psychological Review 97: 523-547.
• Busemeyer, J. R., & Johnson, J. G. (2004). Computational models of decision making. In D. Koehler & N. Harvey (Eds.), Handbook of judgment and decision making (pp. 133–154). Oxford, England: Blackwell.
• Chomsky, N. (1959) Review of Verbal Behavior, by B.F. Skinner. Language 35: 26-57.
• Chomsky, N. (1965) Aspects of the theory of syntax. Cambridge, MA: MIT Press. |
• Chun, M. M. and Potter, M. C. (1995) A two-stage model for multiple target detection in rapid serial visual presentation. Journal of Experimental Psychology: Human Perception & Performance 21: 109-127.
• Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001) DRC: A Dual Route Cascaded model of visual word recognition and reading aloud. Psychological Review , 108, 204 - 256.
• Costall, A. and Still, A. (eds) (1987) Cognitive Psychology in Question. Brighton: Harvester Press Ltd.
• Cowan, N. (1995) Attention and memory: an integrated framework, New York: Oxford University Press.
• Craik, F. I. M. (2002) Levels of processing: Past, present... and future? Memory 10: 305-318.
• Dosher, B.A. (1999) Item interference and time delays in working memory: Immediate serial recall. International Journal of Psychology Special Issue: Short term/working memory, 34: 276-284.
• Dreyfus, H. L. (1979) What computers can't do: the limits of artificial intelligence, New York : Harper and Row.
• Estes, W. K. (1969) Reinforcement in human learning. In J. Tapp (Ed.), Reinforcement and behavior. New York: Academic Press.
• Fenton, N. and Neil, M. (2001) Making Decisions: using Bayesian nets and MCDA, Knowledge-based Systems 14: 307-325.
• Gabrieli, J. D. E. (1998) Cognitive neuroscience of human memory. Annual Review of Psychology 49: 87-115.
• Gabrieli, J.D.E., Fleischman, D.A., Keane, M.M., Reminger, S.L. and Morrell, F. (1995) Double dissociation between memory systems underlying explicit and implicit memory in the human brain. Psychological Science 6: 76-82.
• Gobet, F. and Simon, H. A. (1996) Recall of random and distorted chess positions: implications for the theory of expertise. Memory & cognition 24: 493-503.
• Grafton, S. T., Hazeltine, E., and Ivry, R. (1995) Functional mapping of sequence learning in normal humans. Journal of Cognitive Neuroscience, 7: 497–510.
• Grossberg, S. and Mingolla, E. (1985) Neural dynamics of form perception: boundary completion, illusory figures, and neon color spreading. Psychological Review, 92: 173-211.
• Holland, J. H., Holyoak, K. J., Nisbett, R. E., and Thagard, P. R. (1986) Induction. Cambridge, MA: MIT Press.
• Holyoak, K. J. and Thagard, P. (1995) Mental leaps analogy in creative thought, Cambridge, MA: MIT Press.
• Hothersall, David (1984) History of Psychology, NY: Random House.
• Johnson-Laird, P. N., Byrne, R. M. J. and Schaeken, W. (1992) Propositional reasoning by model, Psychology Review 99: 418-439.
• Julesz, B. (1971) Foundations of cyclopean perception. Chicago: University of Chicago Press.
• Just, M. A., Carpenter, P. A., & Woolley, J. D. (1982) Paradigms and processes and in reading comprehension. Journal of Experimental Psychology: General 3: 228-238.
• Kahneman, D. and Tversky, A. (1979) Prospect Theory: An Analysis of Decision under Risk. Econometrica 47: 263-292.
• Kahneman, D., Slovic, P. and Tversky, A. (1982) Judgment under uncertainty: heuristics and biases, New York: Cambridge University Press.
• Kandel, E. R. (1976) Cellular basis of behavior: An introduction to behavioural neurobiology. San Francisco: W. H. Freeman.
• Kanwisher N and Wojciulik E. (2000) Visual attention: Insights from brain imaging. Nature Review Neuroscience 1: 91-100.
• Kintsch, W. (1974) The representation of meaning in memory, Hillsdale, NJ: Lawrence Erlbaum Associates.
• Livingstone, M.S. and Hubel, D.H. (1988) Segregation of form, colour, movement and depth: Anatomy, physiology and perception. Science 240: 740–749.
• Levelt, W. J. M. (1989) Speaking: From Intention to Articulation, Cambridge, MA: The MIT Press.
• Logan, G.D. (2004) Cumulative progress in formal theories of attention. Annual Review Of Psychology 55: 207-234.
• Lu, Z.-L., & Dosher, B.A. (1998) External noise distinguishes attention mechanisms. Vision Research 38:.
• Lu, Z.-L., & Sperling, G. (1995) The functional architecture of human visual motion perception, Vision Research 35:.
• Luce, D. R. (1959) Individual choice behavior; a theoretical analysis, New York: Wiley.
• Luce, R. D. and Raiffa, H. (1989) Games and decisions : introduction and critical survey. New York: Dover Publications
• Marr, D. (1982) Vision. San Francisco: W. H. Freeman.
• Massaro, D. W. (1998) Perceiving talking faces: from speech perception to a behavioral principle, Cambridge, MA: The MIT Press.
• McClelland, J. L. and Rumelhart, D. E. (1981) An Interactive Activation Model of Context Effects in Letter Perception: Part 1, Psychological Review 88: 375-407.
• Medlin, D. L. and Ross, B. H. (1992) Cognitive psychology. Fort Worth: Harcourt Brace Johanovich.
• Medlin, D. L. (1989) Concepts and conceptual structure. American Psychologist 44: 1469–1481.
• Miller, G.A. (1956) The magical number seven, plus or minus two. Psychological Review 63: 81–97.
• Neisser, U (1967) Cognitive psychology. New York: Appleton-Century-Crofts.
• Newell, A., and Simon, H. A. (1972) Human Problem Solving, Englewood Cliffs, NJ: Prentice-Hall.
• Newell, A., Shaw, J. C., and Simon, H. A. (1958) Elements of a Theory of Human Problem Solving. Psychological Review 23: 342-343.
• Pashler, H. E. (1999) The psychology of attention, Cambridge, MA: MIT Press,
• Pinker, S. (1994) The language instinct, New York: W. Morrow and Co.
• Posner, M.I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology 32: 3-25.
• Posner, M. I., Petersen, S. E., Fox, P. T. and Raichle, R. E. (1988) Localization of cognitive operations in the human brain, Science 240:.
• Reynolds, J.H., Chelazzi, L., & Desimone, R. (1999). Competitive mechanisms subserve attention in macaque areas V2 and V4. Journal of Neuroscience 19:.
• Rips, L. J., & Marcus, S. L. (1977). Suppositions and the analysis of conditional sentences. In M. A. Just & P. A. Carpenter (Eds.), Cognitive processes in comprehension. Hillsdale, NJ: Erlbaum.
• Roediger III, H. L. (2002) Processing approaches to cognition: The impetus from the levels-of-processing framework. Memory 10: 319-332.
• Russo, J. E. & Dosher, B. A. (1983) Strategies for multiattribute binary choice. J Exp Psychology Learning, Memory & Cognition 9: 676-696.
• Sanjana, N. E. & Tenenbaum, J. B. (2003) Bayesian models of inductive generalization. Advances in Neural Information Processing Systems 15: 59-66.
• Schank, R. C. & Abelson, R. P. (1977) Scripts, plans, goals, and understanding : an inquiry into human knowledge structures, Hillsdale, NJ: L. Erlbaum Associates.
• Searle, J. R. (1990) Is the brain a digital computer APA Presidential Address.
• Seidenberg, M. S. (1997) Language Acquisition and Use: Learning and Applying Probabilistic Constraints. Science 275:.
• Shiffrin, R. M., & Steyvers, M. (1997). A model for recognition memory: REM – retrieving effectively from memory. Psychonomic Bulletin & Review 4: 145-166.
• Skinner, B. F. (1950) Are theories of learning necessary? Psychological Review 57: 193-216.
• Sperling, G. (1960). The information available in brief visual presentations. Psychological Monographs, 74 1-29.
• Squire, L. R., Zola, S. M. (1996) Structure and function of declarative and non-declarative memory systems. Proceedings of the National Academy of Sciences 93:.
• Thompson, R. F. (1986) The neurobiology of learning and memory. Science 29: 941 – 947.
• Thorne, J., Bratley, P. & Dewar, H. (1968) The syntactic analysis of English by machine. In Michie, D. (Ed), Machine Intelligence, New York: American Elsevier.
• Tinklepaugh, O. L. (1928) An experimental study of representative factors in monkeys, Journal of Comparative Psychology 8: 197–236.
• Treisman, A. M. (1969) Strategies and models of selective attention. Psychological Review 76: 282-299.
• Tulving, E. (1962). Subjective organization in free recall of “unrelated” words. Psychological Review 69: 344-354.
• Tulving, E., Gordon Hayman, C. A. and MacDonald, C. A. (1991) Long-lasting perceptual priming and semantic learning in amnesia , A case experiment. Journal of Experimental Psychology 17: 595-617.
• Ungerleider, L.G. and Mishkin, M. (1982) In D.J. Ingle, M.A. Goodale, and R.J.W. Mansfield (Eds.), Analysis of visual behavior. Cambridge, MA: MIT Press.
• von Neumann, J. and Morgenstern, O. (1944) Theory of Games and Economic Behavior, Princeton, NJ: Princeton Univ. Press.
• Wandell, B. (1995) Foundations of vision, Sunderland, MA: Sinauer Associates.
• Watson, J.B. (1913) Psychology as the behaviorist views it, Psychological Review 20: 158-177.
• Weichselgartner, E. and G. L. U. S. Sperling (1987) Dynamics of automatic and controlled visual attention. Science 238: 778-780.
• Wickelgren, W. A. (1974) How to solve problems. New York: W. H. Freeman.
• Zhang, J. (1997) The nature of external: Representations in problem solving. Cognitive science 21: 179-217.

Internal references

• Valentino Braitenberg (2007) Brain . Scholarpedia , 2(11):2918.
• Olaf Sporns (2007) Complexity . Scholarpedia, 2(10):1623.
• James Meiss (2007) Dynamical systems . Scholarpedia, 2(2):1629.
• Paul L. Nunez and Ramesh Srinivasan (2007) Electroencephalogram . Scholarpedia, 2(2):1348.
• Robert Kurzban (2007) Evolutionary psychology . Scholarpedia, 2(8):3161.
• William D. Penny and Karl J. Friston (2007) Functional imaging . Scholarpedia, 2(5):1478.
• Seiji Ogawa and Yul-Wan Sung (2007) Functional magnetic resonance imaging . Scholarpedia, 2(10):3105.
• Mark Aronoff (2007) Language . Scholarpedia, 2(5):3175.
• John Dowling (2007) Retina . Scholarpedia, 2(12):3487.
• Wolfram Schultz (2007) Reward . Scholarpedia, 2(3):1652.

External Links

• Zhong-Lin Lu's lab website
• Zhong-Lin Lu's website
• Barbara Anne Dosher's website

Cognitive Neuropsychology , Evolutionary Psychology , Neuropsychology

• Neuroscience
• Multiple Curators

Personal tools

• View source
• View history
• Propose a new article
• Instructions for Authors
• Random article

Focal areas

• Astrophysics
• Celestial mechanics
• Computational neuroscience
• Computational intelligence
• Dynamical systems
• More topics
• Recently published articles
• Recently sponsored articles
• Recent changes
• All articles
• List all Curators
• List all users
• Scholarpedia Journal
• What links here
• Related changes
• Special pages
• Printable version
• Permanent link

• "Cognitive psychology" by Zhong-Lin Lu and Barbara Anne Dosher is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License . Permissions beyond the scope of this license are described in the Terms of Use
• Privacy policy
• About Scholarpedia
• Disclaimers

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

Analysing Complex Problem-Solving Strategies from a Cognitive Perspective: The Role of Thinking Skills

1 MTA-SZTE Digital Learning Technologies Research Group, Center for Learning and Instruction, University of Szeged, 6722 Szeged, Hungary

Gyöngyvér Molnár

2 MTA-SZTE Digital Learning Technologies Research Group, Institute of Education, University of Szeged, 6722 Szeged, Hungary; uh.degezs-u.yspde@ranlomyg

Associated Data

The data used to support the findings cannot be shared at this time as it also forms part of an ongoing study.

Complex problem solving (CPS) is considered to be one of the most important skills for successful learning. In an effort to explore the nature of CPS, this study aims to investigate the role of inductive reasoning (IR) and combinatorial reasoning (CR) in the problem-solving process of students using statistically distinguishable exploration strategies in the CPS environment. The sample was drawn from a group of university students (N = 1343). The tests were delivered via the eDia online assessment platform. Latent class analyses were employed to seek students whose problem-solving strategies showed similar patterns. Four qualitatively different class profiles were identified: (1) 84.3% of the students were proficient strategy users, (2) 6.2% were rapid learners, (3) 3.1% were non-persistent explorers, and (4) 6.5% were non-performing explorers. Better exploration strategy users showed greater development in thinking skills, and the roles of IR and CR in the CPS process were varied for each type of strategy user. To sum up, the analysis identified students’ problem-solving behaviours in respect of exploration strategy in the CPS environment and detected a number of remarkable differences in terms of the use of thinking skills between students with different exploration strategies.

1. Introduction

Problem solving is part and parcel of our daily activities, for instance, in determining what to wear in the morning, how to use our new electronic devices, how to reach a restaurant by public transport, how to arrange our schedule to achieve the greatest work efficiency and how to communicate with people in a foreign country. In most cases, it is essential to solve the problems that recur in our study, work and daily lives. These situations require problem solving. Generally, problem solving is the thinking that occurs if we want “to overcome barriers between a given state and a desired goal state by means of behavioural and/or cognitive, multistep activities” ( Frensch and Funke 1995, p. 18 ). It has also been considered as one of the most important skills for successful learning in the 21st century. This study focuses on one specific kind of problem solving, complex problem solving (CPS). (Numerous other terms are also used ( Funke et al. 2018 ), such as interactive problem solving ( Greiff et al. 2013 ; Wu and Molnár 2018 ), and creative problem solving ( OECD 2010 ), etc.).

CPS is a transversal skill ( Greiff et al. 2014 ), operating several mental activities and thinking skills (see Molnár et al. 2013 ). In order to explore the nature of CPS, some studies have focused on detecting its component skills ( Wu and Molnár 2018 ), whereas others have analysed students’ behaviour during the problem-solving process ( Greiff et al. 2018 ; Wu and Molnár 2021 ). This study aims to link these two fields by investigating the role of thinking skills in learning by examining students’ use of statistically distinguishable exploration strategies in the CPS environment.

1.1. Complex Problem Solving: Definition, Assessment and Relations to Intelligence

According to a widely accepted definition proposed by Buchner ( 1995 ), CPS is “the successful interaction with task environments that are dynamic (i.e., change as a function of users’ intervention and/or as a function of time) and in which some, if not all, of the environment’s regularities can only be revealed by successful exploration and integration of the information gained in that process” ( Buchner 1995, p. 14 ). A CPS process is split into two phases, knowledge acquisition and knowledge application. In the knowledge acquisition (KAC) phase of CPS, the problem solver understands the problem itself and stores the acquired information ( Funke 2001 ; Novick and Bassok 2005 ). In the knowledge application (KAP) phase, the problem solver applies the acquired knowledge to bring about the transition from a given state to a goal state ( Novick and Bassok 2005 ).

Problem solving, especially CPS, has frequently been compared or linked to intelligence in previous studies (e.g., Beckmann and Guthke 1995 ; Stadler et al. 2015 ; Wenke et al. 2005 ). Lotz et al. ( 2017 ) observed that “intelligence and [CPS] are two strongly overlapping constructs” (p. 98). There are many similarities and commonalities that can be detected between CPS and intelligence. For instance, CPS and intelligence share some of the same key features, such as the integration of information ( Stadler et al. 2015 ). Furthermore, Wenke et al. ( 2005 ) stated that “the ability to solve problems has featured prominently in virtually every definition of human intelligence” (p. 9); meanwhile, from the opposite perspective, intelligence has also been considered as one of the most important predictors of the ability to solve problems ( Wenke et al. 2005 ). Moreover, the relation between CPS and intelligence has also been discussed from an empirical perspective. A meta-analysis conducted by Stadler et al. ( 2015 ) selected 47 empirical studies (total sample size N = 13,740) which focused on the correlation between CPS and intelligence. The results of their analysis confirmed that a correlation between CPS and intelligence exists with a moderate effect size of M(g) = 0.43.

Due to the strong link between CPS and intelligence, assessments of these two domains have been connected and have overlapped to a certain extent. For instance, Beckmann and Guthke ( 1995 ) observed that some of the intelligence tests “capture something akin to an individual’s general ability to solve problems (e.g., Sternberg 1982 )” (p. 184). Nowadays, some widely used CPS assessment methods are related to intelligence but still constitute a distinct construct ( Schweizer et al. 2013 ), such as the MicroDYN approach ( Greiff and Funke 2009 ; Greiff et al. 2012 ; Schweizer et al. 2013 ). This approach uses the minimal complex system to simulate simplistic, artificial but still complex problems following certain construction rules ( Greiff and Funke 2009 ; Greiff et al. 2012 ).

The MicroDYN approach has been widely employed to measure problem solving in a well-defined problem context (i.e., “problems have a clear set of means for reaching a precisely described goal state”, Dörner and Funke 2017, p. 1 ). To complete a task based on the MicroDYN approach, the problem solver engages in dynamic interaction with the task to acquire relevant knowledge. It is not possible to create this kind of test environment with the traditional paper-and-pencil-based method. Therefore, it is currently only possible to conduct a MicroDYN-based CPS assessment within the computer-based assessment framework. In the context of computer-based assessment, the problem-solvers’ operations were recorded and logged by the assessment platform. Thus, except for regular achievement-focused result data, logfile data are also available for analysis. This provides the option of exploring and monitoring problem solvers’ behaviour and thinking processes, specifically, their exploration strategies, during the problem-solving process (see, e.g., Chen et al. 2019 ; Greiff et al. 2015a ; Molnár and Csapó 2018 ; Molnár et al. 2022 ; Wu and Molnár 2021 ).

Problem solving, in the context of an ill-defined problem (i.e., “problems have no clear problem definition, their goal state is not defined clearly, and the means of moving towards the (diffusely described) goal state are not clear”, Dörner and Funke 2017, p. 1), involved a different cognitive process than that in the context of a well-defined problem ( Funke 2010 ; Schraw et al. 1995 ), and it cannot be measured with the MicroDYN approach. The nature of ill-defined problem solving has been explored and discussed in numerous studies (e.g., Dörner and Funke 2017 ; Hołda et al. 2020 ; Schraw et al. 1995 ; Welter et al. 2017 ). This will not be discussed here as this study focuses on well-defined problem solving.

1.2. Inductive and Combinatorial Reasoning as Component Skills of Complex Problem Solving

Frensch and Funke ( 1995 ) constructed a theoretical framework that summarizes the basic components of CPS and the interrelations among the components. The framework contains three separate components: problem solver, task and environment. The impact of the problem solver is mainly relevant to three main categories, which are memory contents, dynamic information processing and non-cognitive variables. Some thinking skills have been reported to play an important role in dynamic information processing. We can thus describe them as component skills of CPS. Inductive reasoning (IR) and combinatorial reasoning (CR) are the two thinking skills that have been most frequently discussed as component skills of CPS.

IR is the reasoning skill that has been covered most commonly in the literature. Currently, there is no universally accepted definition. Molnár et al. ( 2013 ) described it as the cognitive process of acquiring general regularities by generalizing single and specific observations and experiences, whereas Klauer ( 1990 ) defined it as the discovery of regularities that relies upon the detection of similarities and/or dissimilarities as concerns attributes of or relations to or between objects. Sandberg and McCullough ( 2010 ) provided a general conclusion of the definitions of IR: it is the process of moving from the specific to the general.

Csapó ( 1997 ) pointed out that IR is a basic component of thinking and that it forms a central aspect of intellectual functioning. Some studies have also discussed the role of IR in a problem-solving environment. For instance, Mayer ( 1998 ) stated that IR will be applied in information processing during the process of solving general problems. Gilhooly ( 1982 ) also pointed out that IR plays a key role in some activities in the problem-solving process, such as hypothesis generation and hypothesis testing. Moreover, the influence of IR on both KAC and KAP has been analysed and demonstrated in previous studies ( Molnár et al. 2013 ).

Empirical studies have also provided evidence that IR and CPS are related. Based on the results of a large-scale assessment (N = 2769), Molnár et al. ( 2013 ) showed that IR significantly correlated with 9–17-year-old students’ domain-general problem-solving achievement (r = 0.44–0.52). Greiff et al. ( 2015b ) conducted a large-scale assessment project (N = 2021) in Finland to explore the links between fluid reasoning skills and domain-general CPS. The study measured fluid reasoning as a two-dimensional model which consisted of deductive reasoning and scientific reasoning and included inductive thinking processes ( Greiff et al. 2015b ). The results drawing on structural equation modelling indicated that fluid reasoning which was partly based on IR had significant and strong predictive effects on both KAC (β = 0.51) and KAP (β = 0.55), the two phases of problem solving. Such studies have suggested that IR is one of the component skills of CPS.

According to Adey and Csapó ’s ( 2012 ) definition, CR is the process of creating complex constructions out of a set of given elements that satisfy the conditions explicitly given in or inferred from the situation. In this process, some cognitive operations, such as combinations, arrangements, permutations, notations and formulae, will be employed ( English 2005 ). CR is one of the basic components of formal thinking ( Batanero et al. 1997 ). The relationship between CR and CPS has frequently been discussed. English ( 2005 ) demonstrated that CR has an essential meaning in several types of problem situations, such as problems requiring the systematic testing of alternative solutions. Moreover, Newell ( 1993 ) pointed out that CR is applied in some key activities of problem-solving information processing, such as strategy generation and application. Its functions include, but are not limited to, helping problem solvers to discover relationships between certain elements and concepts, promoting their fluency of thinking when they are considering different strategies ( Csapó 1999 ) and identifying all possible alternatives ( OECD 2014 ). Moreover, Wu and Molnár ’s ( 2018 ) empirical study drew on a sample (N = 187) of 11–13-year-old primary school students in China. Their study built a structural equation model between CPS, IR and CR, and the result indicated that CR showed a strong and statistically significant predictive power for CPS (β = 0.55). Thus, the results of the empirical study also support the argument that CR is one of the component skills of CPS.

1.3. Behaviours and Strategies in a Complex Problem-Solving Environment

Wüstenberg et al. ( 2012 ) stated that the creation and implementation of strategic exploration are core actions of the problem-solving task. Exploring and generating effective information are key to successfully solving a problem. Wittmann and Hattrup ( 2004 ) illustrated that “riskier strategies [create] a learning environment with greater opportunities to discover and master the rules and boundaries [of a problem]” (p. 406). Thus, when gathering information about a complex problem, there may be differences between exploration strategies in terms of efficacy. The MicroDYN scenarios, a simplification and simulation of the real-world problem-solving context, will also be influenced by the adoption and implementation of exploration strategies.

The effectiveness of the isolated variation strategy (or “Vary-One-Thing-At-A-Time” strategy—VOTAT; Vollmeyer et al. 1996 ) in a CPS environment has been hotly debated ( Chen et al. 2019 ; Greiff et al. 2018 ; Molnár and Csapó 2018 ; Molnár et al. 2022 ; Wu and Molnár 2021 ; Wüstenberg et al. 2014 ). To use the VOTAT strategy, a problem solver “systematically varies only one input variable, whereas the others remain unchanged. This way, the effect of the variable that has just been changed can be observed directly by monitoring the changes in the output variables” ( Molnár and Csapó 2018, p. 2 ). Understanding and using VOTAT effectively is the foundation for developing more complex strategies for coordinating multiple variables and the basis for some phases of scientific thinking (i.e., inquiry, analysis, inference and argument; Kuhn 2010 ; Kuhn et al. 1995 ).

Some previous studies have indicated that students who are able to apply VOTAT are more likely to achieve higher performance in a CPS assessment ( Greiff et al. 2018 ), especially if the problem is a well-defined minimal complex system (such as MicroDYN) ( Fischer et al. 2012 ; Molnár and Csapó 2018 ; Wu and Molnár 2021 ). For instance, Molnár and Csapó ( 2018 ) conducted an empirical study to explore how students’ exploration strategies influence their performance in an interactive problem-solving environment. They measured a group (N = 4371) of 3rd- to 12th-grade (aged 9–18) Hungarian students’ problem-solving achievement and modelled students’ exploration strategies. This result confirmed that students’ exploration strategies influence their problem-solving performance. For example, conscious VOTAT strategy users proved to be the best problem-solvers. Furthermore, other empirical studies (e.g., Molnár et al. 2022 ; Wu and Molnár 2021 ) achieved similar results, thus confirming the importance of VOTAT in a MicroDYN-based CPS environment.

Lotz et al. ( 2017 ) illustrated that effective use of VOTAT is associated with higher levels of intelligence. Their study also pointed out that intelligence has the potential to facilitate successful exploration behaviour. Reasoning skills are an important component of general intelligence. Based on Lotz et al. ’s ( 2017 ) statements, the roles IR and CR play in the CPS process might vary due to students’ different strategy usage patterns. However, there is still a lack of empirical studies in this regard.

2. Research Aims and Questions

Numerous studies have explored the nature of CPS, some of them discussing and analysing it from behavioural or cognitive perspectives. However, there have barely been any that have merged these two perspectives. From the cognitive perspective, this study explores the role of thinking skills (including IR and CR) in the cognition process of CPS. From the behavioural perspective, the study focuses on students’ behaviour (i.e., their exploration strategy) in the CPS assessment process. More specifically, the research aims to fill this gap and examine students’ use of statistically distinguishable exploration strategies in CPS environments and to detect the connection between the level of students’ thinking skills and their behaviour strategies in the CPS environment. The following research questions were thus formed.

• (RQ1) What exploration strategy profiles characterise the various problem-solvers at the university level?
• (RQ2) Can developmental differences in CPS, IR and CR be detected among students with different exploration strategy profiles?
• (RQ3) What are the similarities and differences in the roles IR and CR play in the CPS process as well as in the two phases of CPS (i.e., KAC and KAP) among students with different exploration strategy profiles?

3.1. Participants and Procedure

The sample was drawn from one of the largest universities in Hungary. Participation was voluntary, but students were able to earn one course credit for taking part in the assessment. The participants were students who had just started their studies there (N = 1671). 43.4% of the first-year students took part in the assessment. 50.9% of the participants were female, and 49.1% were male. We filtered the sample and excluded those who had more than 80% missing data on any of the tests. After the data were cleaned, data from 1343 students were available for analysis. The test was designed and delivered via the eDia online assessment system ( Csapó and Molnár 2019 ). The assessment was held in the university ICT room and divided into two sessions. The first session involved the CPS test, whereas the second session entailed the IR and CR tests. Each session lasted 45 min. The language of the tests was Hungarian, the mother tongue of the students.

3.2. Instruments

3.2.1. complex problem solving (cps).

The CPS assessment instrument adopted the MicroDYN approach. It contains a total of twelve scenarios, and each scenario consisted of two items (one item in the KAC phase and one item in the KAP phase in each problem scenario). Twelve KAC items and twelve KAP items were therefore delivered on the CPS test for a total of twenty-four items. Each scenario has a fictional cover story. For instance, students found a sick cat in front of their house, and they were expected to feed the cat with two different kinds of cat food to help it recover.

Each item contains up to three input and three output variables. The relations between the input and output variables were formulated with linear structural equations ( Funke 2001 ). Figure 1 shows a MicroDYN sample structure containing three input variables (A, B and C), three output variables (X, Y and Z) and a number of possible relations between the variables. The complexity of the item was defined by the number of input and output variables, and the number of relations between the variables. The test began with the item with the lowest complexity. The complexity of each item gradually increased as the test progressed.

A typical MicroDYN structure with three input variables and three output variables ( Greiff and Funke 2009 ).

The interface of each item displays the value of each variable in both numerical and figural forms (See Figure 2 ). Each of the input variables has a controller, which makes it possible to vary and set the value between +2 (+ +) and −2 (− −). To operate the system, students need to click the “+” or “−” button or use the slider directly to select the value they want to be added to or subtracted from the current value of the input variable. After clicking the “Apply” button in the interface, the input variables will add or subtract the selected value, and the output variables will show the corresponding changes. The history of the values for the input and output variables within the same problem scenario is displayed on screen. If students want to withdraw all the changes and set all the variables to their original status, they can click the “Reset” button.

Screenshot of the MicroDYN item Cat—first phase (knowledge acquisition). (The items were administered in Hungarian.)

In the first phase of the problem-solving process, the KAC phase, students are asked to interact with the system by changing the value of the input variables and observing and analysing the corresponding changes in the output variables. They are then expected to determine the relationship between the input and output variables and draw it in the form of (an) arrow(s) on the concept map at the bottom of the interface. To avoid item dependence in the second phase of the problem-solving process, the students are provided with a concept map during the KAP phase (see Figure 3 ), which shows the correct connections between the input and output variables. The students are expected to interact with the system by manipulating the input variables to make the output variables reach the given target values in four steps or less. That is, they cannot click on the “Apply” button more than four times. The first phase had a 180 s time limit, whereas the second had a 90 s time limit.

Screenshot of the MicroDYN item Cat—second phase (knowledge application). (The items were administered in Hungarian).

3.2.2. Inductive Reasoning (IR)

The IR instrument (see Figure 4 ) was originally designed and developed in Hungary ( Csapó 1997 ). In the last 25 years, the instrument has been further developed and scaled for a wide age range ( Molnár and Csapó 2011 ). In addition, figural items have been added, and the assessment method has evolved from paper-and-pencil to computer-based ( Pásztor 2016 ). Currently, the instrument is widely employed in a number of countries (see, e.g., Mousa and Molnár 2020 ; Pásztor et al. 2018 ; Wu et al. 2022 ; Wu and Molnár 2018 ). In the present study, four types of items were included after test adaptation: figural series, figural analogies, number analogies and number series. Students were expected to ascertain the correct relationship between the given figures and numbers and select a suitable figure or number as their answer. Students used the drag-and-drop operation to provide their answers. In total, 49 inductive reasoning items were delivered to the participating students.

Sample items for the IR test. (The items were administered in Hungarian.).

3.2.3. Combinatorial Reasoning (CR)

The CR instrument (see Figure 5 ) was originally designed by Csapó ( 1988 ). The instrument was first developed in paper-and-pencil format and then modified for computer use ( Pásztor and Csapó 2014 ). Each item contained figural or verbal elements and a clear requirement for combing through the elements. Students were asked to list every single combination based on a given rule they could find. For the figural items, students provided their answers using the drag-and-drop operation; for the verbal items, they were asked to type their answers in a text box provided on screen. The test consisted of eight combinatorial reasoning items in total.

Sample item for the CR test. (The items were administered in Hungarian).

3.3. Scoring

Students’ performance was automatically scored via the eDia platform. Items on the CPS and IR tests were scored dichotomously. In the first phase (KAC) of the CPS test, if a student drew all the correct relations on the concept map provided on screen within the given timeframe, his/her performance was assigned a score of 1 or otherwise a score of 0. In the second phase (KAP) of the CPS test, if the student successfully reached the given target values of the output variables by manipulating the level of the input variables within no more than four steps and the given timeframe, then his/her performance earned a score of 1 or otherwise a score of 0. On the IR test items, if a student selected the correct figure or number as his/her answer, then he or she received a score of 1; otherwise, the score was 0.

Students’ performance on the CR test items was scored according to a special J index, which was developed by Csapó ( 1988 ). The J index ranges from 0 to 1, where 1 means that the student provided all the correct combinations without any redundant combinations on the task. The formula for computing the J index is the following:

x stands for the number of correct combinations in the student’s answer,

T stands for the number of all possible correct combinations, and

y stands for the number of redundant combinations in the student’s answer.

Furthermore, according to Csapó ’s ( 1988 ) design, if y is higher than T, then the J index will be counted as 0.

3.4. Coding and Labelling the Logfile Data

Beyond concrete answer data, students’ interaction and manipulation behaviour were also logged in the assessment system. This made it possible to analyse students’ exploration behaviour in the first phase of the CPS process (KAC phase). Toward this aim, we adopted a labelling system developed by Molnár and Csapó ( 2018 ) to transfer the raw logfile data to structured data files for analysis. Based on the system, each trial (i.e., the sum of manipulations within the same problem scenario which was applied and tested by clicking the “Apply” button) was modelled as a single data entity. The sum of these trials within the same problem was defined as a strategy. In our study, we only consider the trials which were able to provide useful and new information for the problem-solvers, whereas the redundant or operations trials were excluded.

In this study, we analysed students’ trials to determine the extent to which they used the VOTAT strategy: fully, partially or not at all. This strategy is the most successful exploration strategy for such problems; it is the easiest to interpret and provides direct information about the given variable without any mediation effects ( Fischer et al. 2012 ; Greiff et al. 2018 ; Molnár and Csapó 2018 ; Wüstenberg et al. 2014 ; Wu and Molnár 2021 ). Based on the definition of VOTAT noted in Section 1.3 , we checked students’ trials to ascertain if they systematically varied one input variable while keeping the others unchanged, or applied a different, less successful strategy. We considered the following three types of trials:

• “Only one single input variable was manipulated, whose relationship to the output variables was unknown (we considered a relationship unknown if its effect cannot be known from previous settings), while the other variables were set at a neutral value like zero […]
• One single input variable was changed, whose relationship to the output variables was unknown. The others were not at zero, but at a setting used earlier. […]
• One single input variable was changed, whose relationship to the output variables was unknown, and the others were not at zero; however, the effect of the other input variable(s) was known from earlier settings. Even so, this combination was not attempted earlier” ( Molnár and Csapó 2018, p. 8 )

We used the numbers 0, 1 and 2 to distinguish the level of students’ use of the most effective exploration strategy (i.e., VOTAT). If a student applied one or more of the above trials for every input variable within the same scenario, we considered that they had used the full VOTAT strategy and labelled this behaviour 2. If a student had only employed VOTAT on some but not all of the input variables, we concluded that they had used a partial VOTAT strategy for that problem scenario and labelled it 1. If a student had used none of the trials noted above in their problem exploration, then we determined that they had not used VOTAT at all and thus gave them a label of 0.

3.5. Data Analysis Plan

We used LCA (latent class analysis) to explore students’ exploration strategy profiles. LCA is a latent variable modelling approach that can be used to identify unmeasured (latent) classes of samples with similarly observed variables. LCA has been widely used in analysing logfile data for CPS assessment and in exploring students’ behaviour patterns (see, e.g., Gnaldi et al. 2020 ; Greiff et al. 2018 ; Molnár et al. 2022 ; Molnár and Csapó 2018 ; Mustafić et al. 2019 ; Wu and Molnár 2021 ). The scores for the use of VOTAT in the KAC phase (0, 1, 2; see Section 3.4 ) were used for the LCA analysis. We used Mplus ( Muthén and Muthén 2010 ) to run the LCA analysis. Several indices were used to measure the model fit: AIC (Akaike information criterion), BIC (Bayesian information criterion) and aBIC (adjusted Bayesian information criterion). With these three indicators, lower values indicate a better model fit. Entropy (ranging from 0 to 1, with values close to 1 indicating high certainty in the classification). The Lo–Mendell–Rubin adjusted likelihood ratio was used to compare the model containing n latent classes with the model containing n − 1 latent classes, and the p value was the indicator for whether a significant difference could be detected ( Lo et al. 2001 ). The results of the Lo–Mendell–Rubin adjusted likelihood ratio analysis were used to decide the correct number of latent classes in LCA models.

ANOVA was used to analyse the performance differences for CPS, IR and CR across the students from the different class profiles. The analysis was run using SPSS. A path analysis (PA) was employed in the structural equation modelling (SEM) framework to investigate the roles of CR and IR in CPS and the similarities and differences across the students from the different exploration strategy profiles. The PA models were carried out with Mplus. The Tucker–Lewis index (TLI), the comparative fit index (CFI) and the root-mean-square error of approximation (RMSEA) were used as indicators for the model fit. A TLI and CFI larger than 0.90 paired with a RMSEA less than 0.08 are commonly considered as an acceptable model fit ( van de Schoot et al. 2012 ).

4.1. Descriptive Results

All three tests showed good reliability (Cronbach’s α: CPS: 0.89; IR: 0.87; CR: 0.79). Furthermore, the two sub-dimensions of the CPS test, KAC and KAP, also showed satisfactory reliability (Cronbach’s α: KAC: 0.86; KAP: 0.78). The tests thus proved to be reliable. The means and standard deviations of students’ performance (in percentage) on each test are provided in Table 1 .

The means and standard deviations of students’ performance on each test.

4.2. Four Qualitatively Different Exploration Strategy Profiles Can Be Distinguished in CPS

Based on the labelled logfile data for CPS, we applied latent class analyses to identify the behaviour patterns of the students in the exploration phase of the problem-solving process. The model fits for the LCA analysis are listed in Table 2 . Compared with the 2 or 3 latent class models, the 4 latent class model has a lower AIC, BIC and aBIC, and the likelihood ratio statistical test (the Lo–Mendell–Rubin adjusted likelihood ratio test) confirmed it has a significantly better model fit. The 5 and 6 latent class models did not show a better model fit than the 4 latent class model. Therefore, based on the results, four qualitatively different exploration strategy profiles can be distinguished, which covered 96% of the students.

Fit indices for latent class analyses.

The patterns for the four qualitatively different exploration strategy profiles are shown in Figure 6 . In total, 84.3% of the students were proficient exploration strategy users, who were able to use VOTAT in each problem scenario independent of its difficulty level (represented by the red line in Figure 5 ). In total, 6.2% of the students were rapid learners. They were not able to apply VOTAT at the beginning of the test on the easiest problems but managed to learn quickly, and, after a rapid learning curve by the end of the test, they reached the level of proficient exploration strategy users, even though the problems became much more complex (represented by the blue line). In total, 3.1% of the students proved to be non-persistent explorers, and they employed VOTAT on the easiest problems but did not transfer this knowledge to the more complex problems. Finally, they were no longer able to apply VOTAT when the complexity of the problems increased (represented by the green line). In total, 6.5% of the students were non-performing explorers; they barely used any VOTAT strategy during the whole test (represented by the pink line) independent of problem complexity.

Four qualitatively different exploration strategy profiles.

4.3. Better Exploration Strategy Users Showed Better Performance in Reasoning Skills

Students with different exploration strategy profiles showed different kinds of performance in each reasoning skill under investigation. Results (see Table 3 ) showed that more proficient strategy users tended to have higher achievement in all the domains assessed as well as in the two sub-dimensions in CPS (i.e., KAC and KAP; ANOVA: CPS: F(3, 1339) = 187.28, p < 0.001; KAC: F(3, 1339) = 237.15, p < 0.001; KAP: F(3, 1339) = 74.91, p < 0.001; IR: F(3, 1339) = 48.10, p < 0.001; CR: F(3, 1339) = 28.72, p < 0.001); specifically, students identified as “proficient exploration strategy users” achieved the highest level on the reasoning skills tests independent of the domains. On average, they were followed by rapid learners, non-persistent explorers and, finally, non-performing explorers. Tukey’s post hoc tests revealed more details on the performance differences of students with different exploration profiles in each of the domains being measured. Proficient strategy users proved to be significantly more skilled in each of the reasoning domains. They were followed by rapid learners, who outperformed non-persistent explorers and non-performing explorers in CPS. In the domains of IR and CR, there were no achievement differences between rapid learners and non-persistent explorers, who significantly outperformed non-performing strategy explorers.

Students’ performance on each test—grouped according to the different exploration strategy profiles.

4.4. The Roles of IR and CR in CPS and Its Processes Were Different for Each Type of Exploration Strategy User

Path analysis was used to explore the predictive power of IR and CR for CPS and its processes, knowledge acquisition and knowledge application, for each group of students with different exploration strategy profiles. That is, four path analysis models were built to indicate the predictive power of IR and CR for CPS (see Figure 7 ), and another four path analyses models were developed to monitor the predictive power of IR and CR for the two empirically distinguishable phases of CPS (i.e., KAC and KAP) (see Figure 8 ). All eight models had good model fits, the fit indices TLI and CFI were above 0.90, and RMSEA was less than 0.08.

Path analysis models (with CPS, IR and CR) for each type of strategy user; * significant at 0.05 ( p   <  0.05); ** significant at 0.01 ( p   <  0.01); N.S.: no significant effect can be found.

Path analysis models (with KAC, KAP, IR and CR) for each type of strategy user; * significant at 0.05 ( p  <  0.05); ** significant at 0.01 ( p  <  0.01); N.S.: no significant effect can be found.

Students’ level of IR significantly predicted their level of CPS in all four path analysis models independent of their exploration strategy profile ( Figure 7 ; proficient strategy users: β = 0.432, p < 0.01; rapid learners: β = 0.350, p < 0.01; non-persistent explorers: β = 0.309, p < 0.05; and non-performing explorers: β = 0.386, p < 0.01). This was not the case for CR, which only proved to have predictive power for CPS among proficient strategy users (β = 0.104, p < 0.01). IR and CR were significantly correlated in all four models.

After examining the roles of IR and CR in the CPS process, we went further to explore the roles of these two reasoning skills in the distinguishable phases of CPS. The path analysis models ( Figure 8 ) showed that the predictive power of IR and CR for KAC and KAP was varied in each group. Levels of IR and CR among non-persistent explorers and non-performing explorers failed to predict their achievement in the KAC phase of the CPS process. Moreover, rapid learners’ level of IR significantly predicted their achievement in the KAC phase (β = 0.327, p < 0.01), but their level of CR did not have the same predictive power. Furthermore, the proficient strategy users’ levels of both reasoning skills had significant predictive power for KAC (IR: β = 0.363, p < 0.01; CR: β = 0.132, p < 0.01). In addition, in the KAP phase of the CPS problems, IR played a significant role for all types of strategy users, although with different power (proficient strategy users: β = 0.408, p < 0.01; rapid learners: β = 0.339, p < 0.01; non-persistent explorers: β = 0.361, p < 0.01; and non-performing explorers: β = 0.447, p < 0.01); by contrast, CR did not have significant predictive power for the KAP phase in any of the models.

5. Discussion

The study aims to investigate the role of IR and CR in CPS and its phases among students using statistically distinguishable exploration strategies in different CPS environments. We examined 1343 Hungarian university students and assessed their CPS, IR and CR skills. Both achievement data and logfile data were used in the analysis. The traditional achievement indicators formed the foundation for analysing the students’ CPS, CR and IR performance, whereas process data extracted from logfile data were used to explore students’ exploration behaviour in various CPS environments.

Four qualitatively different exploration strategy profiles were distinguished: proficient strategy users, rapid learners, non-persistent explorers and non-performing explorers (RQ1). The four profiles were consistent with the result of another study conducted at university level (see Molnár et al. 2022 ), and the frequencies of these four profiles in these two studies were very similar. The two studies therefore corroborate and validate each other’s results. The majority of the participants were identified as proficient strategy users. More than 80% of the university students were able to employ effective exploration strategies in various CPS environments. Of the remaining students, some performed poorly in exploration strategy use in the early part of the test (rapid learners), some in the last part (non-persistent explorers) and some throughout the test (non-performing explorers). However, students with these three exploration strategy profiles only constituted small portions of the total sample (with proportions ranging from 3.1% to 6.5%). The university students therefore exhibited generally good performance in terms of exploration strategy use in a CPS environment, especially compared with previous results among younger students (e.g., primary school students, see Greiff et al. 2018 ; Wu and Molnár 2021 ; primary to secondary students, see Molnár and Csapó 2018 ).

The results have indicated that better exploration strategy users achieved higher CPS performance and had better development levels of IR and CR (RQ2). First, the results have confirmed the importance of VOTAT in a CPS environment. This finding is consistent with previous studies (e.g., Greiff et al. 2015a ; Molnár and Csapó 2018 ; Mustafić et al. 2019 ; Wu and Molnár 2021 ). Second, the results have confirmed that effective use of VOTAT is strongly tied to the level of IR and CR development. Reasoning forms an important component of human intelligence, and the level of development in reasoning was an indicator of the level of intelligence ( Klauer et al. 2002 ; Sternberg and Kaufman 2011 ). Therefore, this finding has supplemented empirical evidence for the argument that effective use of VOTAT is associated with levels of intelligence to a certain extent.

The roles of IR and CR proved to be varied for each type of exploration strategy user (RQ3). For instance, the level of CPS among the best exploration strategy users (i.e., the proficient strategy users) was predicted by both the levels of IR and CR, but this was not the case for students with other profiles. In addition, the results have indicated that IR played important roles in both the KAC and KAP phases for the students with relatively good exploration strategy profiles (i.e., proficient strategy users and rapid learners) but only in the KAP phase for the rest of the students (non-persistent explorers and non-performing explorers); moreover, the predictive power of CR can only be detected in the KAC phase of the proficient strategy users. To sum up, the results suggest a general trend of IR and CR playing more important roles in the CPS process among better exploration strategy users.

Combining the answers to RQ2 and RQ3, we can gain further insights into students’ exploration strategy use in a CPS environment. Our results have confirmed that the use of VOTAT is associated with the level of IR and CR development and that the importance of IR and CR increases with proficiency in exploration strategy use. Based on these findings, we can make a reasonable argument that IR and CR are essential skills for using VOTAT and that underdeveloped IR and CR will prevent students from using effective strategies in a CPS environment. Therefore, if we want to encourage students to become better exploration strategy users, it is important to first enhance their IR and CR skills. Previous studies have suggested that establishing explicit training in using effective strategies in a CPS environment is important for students’ CPS development ( Molnár et al. 2022 ). Our findings have identified the importance of IR and CR in exploration strategy use, which has important implications for designing training programmes.

The results have also provided a basis for further studies. Future studies have been suggested to further link the behavioural and cognitive perspectives in CPS research. For instance, IR and CR were considered as component skills of CPS (see Section 1.2 ). The results of the study have indicated the possibility of not only discussing the roles of IR and CR in the cognitive process of CPS, but also exploration behaviour in a CPS environment. The results have thus provided a new perspective for exploring the component skills of CPS.

6. Limitations

There are some limitations in the study. All the tests were low stake; therefore, students might not be sufficiently motivated to do their best. This feature might have produced the missing values detected in the sample. In addition, some students’ exploration behaviour shown in this study might theoretically be below their true level. However, considering that data cleaning was adopted in this study (see Section 3.1 ), we believe this phenomenon will not have a remarkable influence on the results. Moreover, the CPS test in this study was based on the MicroDYN approach, which is a well-established and widely used artificial model with a limited number of variables and relations. However, it does not have the power to cover all kinds of complex and dynamic problems in real life. For instance, the MicroDYN approach cannot measure ill-defined problem solving. Thus, this study can only demonstrate the influence of IR and CR on problem solving in well-defined MicroDYN-simulated problems. Furthermore, VOTAT is helpful with minimally complex problems under well-defined laboratory conditions, but it may not be that helpful with real-world, ill-defined complex problems ( Dörner and Funke 2017 ; Funke 2021 ). Therefore, the generalizability of the findings is limited.

7. Conclusions

In general, the results have shed new light on students’ problem-solving behaviours in respect of exploration strategy in a CPS environment and explored differences in terms of the use of thinking skills between students with different exploration strategies. Most studies discuss students’ problem-solving strategies from a behavioural perspective. By contrast, this paper discusses them from both behavioural and cognitive perspectives, thus expanding our understanding in this area. As for educational implications, the study contributes to designing and revising training methods for CPS by identifying the importance of IR and CR in exploration behaviour in a CPS environment. To sum up, the study has investigated the nature of CPS from a fresh angle and provided a sound basis for future studies.

Funding Statement

This study has been conducted with support provided by the National Research, Development and Innovation Fund of Hungary, financed under the OTKA K135727 funding scheme and supported by the Research Programme for Public Education Development, Hungarian Academy of Sciences (KOZOKT2021-16).

Author Contributions

Conceptualization, H.W. and G.M.; methodology, H.W. and G.M.; formal analysis, H.W.; writing—original draft preparation, H.W.; writing—review and editing, G.M.; project administration, G.M.; funding acquisition, G.M. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Ethical approval was not required for this study in accordance with the national and institutional guidelines. The assessments which provided data for this study were integrated parts of the educational processes of the participating university. The participation was voluntary.

Informed Consent Statement

All of the students in the assessment turned 18, that is, it was not required or possible to request and obtain written informed parental consent from the participants.

Data Availability Statement

Conflicts of interest.

Authors declare no conflict of interest.

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

• Adey Philip, Csapó Benő. Developing and Assessing Scientific Reasoning. In: Csapó Benő, Szabó Gábor., editors. Framework for Diagnostic Assessment of Science. Nemzeti Tankönyvkiadó; Budapest: 2012. pp. 17–53. [ Google Scholar ]
• Batanero Carmen, Navarro-Pelayo Virginia, Godino Juan D. Effect of the implicit combinatorial model on combinatorial reasoning in secondary school pupils. Educational Studies in Mathematics. 1997; 32 :181–99. doi: 10.1023/A:1002954428327. [ CrossRef ] [ Google Scholar ]
• Beckmann Jens F., Guthke Jürgen. Complex problem solving, intelligence, and learning ability. In: Frensch Peter A., Funke Joachim., editors. Complex Problem Solving: The European Perspective. Erlbaum; Hillsdale: 1995. pp. 177–200. [ Google Scholar ]
• Buchner Axel. Basic topics and approaches to the study of complex problem solving. In: Frensch Peter A., Funke Joachim., editors. Complex Problem Solving: The European Perspective. Erlbaum; Hillsdale: 1995. pp. 27–63. [ Google Scholar ]
• Chen Yunxiao, Li Xiaoou, Liu Jincheng, Ying Zhiliang. Statistical analysis of complex problem-solving process data: An event history analysis approach. Frontiers in Psychology. 2019; 10 :486. doi: 10.3389/fpsyg.2019.00486. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Csapó Benő. A kombinatív képesség struktúrája és fejlődése. Akadémiai Kiadó; Budapest: 1988. [ Google Scholar ]
• Csapó Benő. The development of inductive reasoning: Cross-sectional assessments in an educational context. International Journal of Behavioral Development. 1997; 20 :609–26. doi: 10.1080/016502597385081. [ CrossRef ] [ Google Scholar ]
• Csapó Benő. Teaching and Learning Thinking Skills. Swets & Zeitlinger; Lisse: 1999. Improving thinking through the content of teaching; pp. 37–62. [ Google Scholar ]
• Csapó Benő, Molnár Gyöngyvér. Online diagnostic assessment in support of personalized teaching and learning: The eDia System. Frontiers in Psychology. 2019; 10 :1522. doi: 10.3389/fpsyg.2019.01522. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dörner Dietrich, Funke Joachim. Complex problem solving: What it is and what it is not. Frontiers in Psychology. 2017; 8 :1153. doi: 10.3389/fpsyg.2017.01153. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• English Lyn D. Combinatorics and the development of children’s combinatorial reasoning. In: Jones Graham A., editor. Exploring Probability in School: Challenges for Teaching and Learning. Springer; New York: 2005. pp. 121–41. [ Google Scholar ]
• Fischer Andreas, Greiff Samuel, Funke Joachim. The process of solving complex problems. Journal of Problem Solving. 2012; 4 :19–42. doi: 10.7771/1932-6246.1118. [ CrossRef ] [ Google Scholar ]
• Frensch Peter A., Funke Joachim. Complex Problem Solving: The European Perspective. Psychology Press; New York: 1995. [ Google Scholar ]
• Funke Joachim. Dynamic systems as tools for analysing human judgement. Thinking and Reasoning. 2001; 7 :69–89. doi: 10.1080/13546780042000046. [ CrossRef ] [ Google Scholar ]
• Funke Joachim. Complex problem solving: A case for complex cognition? Cognitive Processing. 2010; 11 :133–42. doi: 10.1007/s10339-009-0345-0. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Funke Joachim. It Requires More Than Intelligence to Solve Consequential World Problems. Journal of Intelligence. 2021; 9 :38. doi: 10.3390/jintelligence9030038. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Funke Joachim, Fischer Andreas, Holt Daniel V. Competencies for complexity: Problem solving in the twenty-first century. In: Care Esther, Griffin Patrick, Wilson Mark., editors. Assessment and Teaching of 21st Century Skills. Springer; Dordrecht: 2018. pp. 41–53. [ Google Scholar ]
• Gilhooly Kenneth J. Thinking: Directed, Undirected and Creative. Academic Press; London: 1982. [ Google Scholar ]
• Gnaldi Michela, Bacci Silvia, Kunze Thiemo, Greiff Samuel. Students’ complex problem solving profiles. Psychometrika. 2020; 85 :469–501. doi: 10.1007/s11336-020-09709-2. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Greiff Samuel, Funke Joachim. Measuring complex problem solving-the MicroDYN approach. In: Scheuermann Friedrich, Björnsson Julius., editors. The Transition to Computer-Based Assessment. Office for Official Publications of the European Communities; Luxembourg: 2009. pp. 157–63. [ Google Scholar ]
• Greiff Samuel, Holt Daniel V., Funke Joachim. Perspectives on problem solving in educational assessment: Analytical, interactive, and collaborative problem solving. Journal of Problem Solving. 2013; 5 :71–91. doi: 10.7771/1932-6246.1153. [ CrossRef ] [ Google Scholar ]
• Greiff Samuel, Molnár Gyöngyvér, Martina Romain, Zimmermann Johannes, Csapó Benő. Students’ exploration strategies in computer-simulated complex problem environments: A latent class approach. Computers & Education. 2018; 126 :248–63. [ Google Scholar ]
• Greiff Samuel, Wüstenberg Sascha, Avvisati Francesco. Computer-generated log-file analyses as a window into students’ minds? A showcase study based on the PISA 2012 assessment of problem solving. Computers & Education. 2015a; 91 :92–105. [ Google Scholar ]
• Greiff Samuel, Wüstenberg Sascha, Funke Joachim. Dynamic problem solving: A new measurement perspective. Applied Psychological Measurement. 2012; 36 :189–213. doi: 10.1177/0146621612439620. [ CrossRef ] [ Google Scholar ]
• Greiff Samuel, Wüstenberg Sascha, Csapó Benő, Demetriou Andreas, Hautamäki Jarkko, Graesser Arthur C., Martin Romain. Domain-general problem solving skills and education in the 21st century. Educational Research Review. 2014; 13 :74–83. doi: 10.1016/j.edurev.2014.10.002. [ CrossRef ] [ Google Scholar ]
• Greiff Samuel, Wüstenberg Sascha, Goetz Thomas, Vainikainen Mari-Pauliina, Hautamäki Jarkko, Bornstein Marc H. A longitudinal study of higher-order thinking skills: Working memory and fluid reasoning in childhood enhance complex problem solving in adolescence. Frontiers in Psychology. 2015b; 6 :1060. doi: 10.3389/fpsyg.2015.01060. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hołda Małgorzata, Głodek Anna, Dankiewicz-Berger Malwina, Skrzypińska Dagna, Szmigielska Barbara. Ill-defined problem solving does not benefit from daytime napping. Frontiers in Psychology. 2020; 11 :559. doi: 10.3389/fpsyg.2020.00559. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Klauer Karl Josef. Paradigmatic teaching of inductive thinking. Learning and Instruction. 1990; 2 :23–45. [ Google Scholar ]
• Klauer Karl Josef, Willmes Klaus, Phye Gary D. Inducing inductive reasoning: Does it transfer to fluid intelligence? Contemporary Educational Psychology. 2002; 27 :1–25. doi: 10.1006/ceps.2001.1079. [ CrossRef ] [ Google Scholar ]
• Kuhn Deanna. What is scientific thinking and how does it develop? In: Goswami Usha., editor. The Wiley-Blackwell Handbook of Childhood Cognitive Development. Wiley-Blackwell; Oxford: 2010. pp. 371–93. [ Google Scholar ]
• Kuhn Deanna, Garcia-Mila Merce, Zohar Anat, Andersen Christopher, Sheldon H. White, Klahr David, Carver Sharon M. Strategies of knowledge acquisition. Monographs of the Society for Research in Child Development. 1995; 60 :1–157. doi: 10.2307/1166059. [ CrossRef ] [ Google Scholar ]
• Lo Yungtai, Mendell Nancy R., Rubin Donald B. Testing the number of components in a normal mixture. Biometrika. 2001; 88 :767–78. doi: 10.1093/biomet/88.3.767. [ CrossRef ] [ Google Scholar ]
• Lotz Christin, Scherer Ronny, Greiff Samuel, Sparfeldt Jörn R. Intelligence in action—Effective strategic behaviors while solving complex problems. Intelligence. 2017; 64 :98–112. doi: 10.1016/j.intell.2017.08.002. [ CrossRef ] [ Google Scholar ]
• Mayer Richard E. Cognitive, metacognitive, and motivational aspects of problem solving. Instructional Science. 1998; 26 :49–63. doi: 10.1023/A:1003088013286. [ CrossRef ] [ Google Scholar ]
• Molnár Gyöngyvér, Csapó Benő. Az 1–11 évfolyamot átfogó induktív gondolkodás kompetenciaskála készítése a valószínűségi tesztelmélet alkalmazásával. Magyar Pedagógia. 2011; 111 :127–40. [ Google Scholar ]
• Molnár Gyöngyvér, Csapó Benő. The efficacy and development of students’ problem-solving strategies during compulsory schooling: Logfile analyses. Frontiers in Psychology. 2018; 9 :302. doi: 10.3389/fpsyg.2018.00302. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Molnár Gyöngyvér, Alrababah Saleh Ahmad, Greiff Samuel. How we explore, interpret, and solve complex problems: A cross-national study of problem-solving processes. Heliyon. 2022; 8 :e08775. doi: 10.1016/j.heliyon.2022.e08775. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Molnár Gyöngyvér, Greiff Samuel, Csapó Benő. Inductive reasoning, domain specific and complex problem solving: Relations and development. Thinking Skills and Creativity. 2013; 9 :35–45. doi: 10.1016/j.tsc.2013.03.002. [ CrossRef ] [ Google Scholar ]
• Mousa Mojahed, Molnár Gyöngyvér. Computer-based training in math improves inductive reasoning of 9- to 11-year-old children. Thinking Skills and Creativity. 2020; 37 :100687. doi: 10.1016/j.tsc.2020.100687. [ CrossRef ] [ Google Scholar ]
• Mustafić Maida, Yu Jing, Stadler Matthias, Vainikainen Mari-Pauliina, Bornstein Marc H., Putnick Diane L., Greiff Samuel. Complex problem solving: Profiles and developmental paths revealed via latent transition analysis. Developmental Psychology. 2019; 55 :2090–101. doi: 10.1037/dev0000764. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Muthén Linda K., Muthén Bengt O. Mplus User’s Guide. Muthén & Muthén; Los Angeles: 2010. [ Google Scholar ]
• Newell Allen. Reasoning, Problem Solving, and Decision Processes: The Problem Space as a Fundamental Category. MIT Press; Boston: 1993. [ Google Scholar ]
• Novick Laura R., Bassok Miriam. Problem solving. In: Holyoak Keith James, Morrison Robert G., editors. The Cambridge Handbook of Thinking and Reasoning. Cambridge University Press; New York: 2005. pp. 321–49. [ Google Scholar ]
• OECD . PISA 2012 Field Trial Problem Solving Framework. OECD Publishing; Paris: 2010. [ Google Scholar ]
• OECD . Results: Creative Problem Solving—Students’ Skills in Tackling Real-Life Problems (Volume V) OECD Publishing; Paris: 2014. [ Google Scholar ]
• Pásztor Attila. Ph.D. thesis. Doctoral School of Education, University of Szeged; Szeged, Hungary: 2016. Technology-Based Assessment and Development of Inductive Reasoning. [ Google Scholar ]
• Pásztor Attila, Csapó Benő. Improving Combinatorial Reasoning through Inquiry-Based Science Learning; Paper presented at the Science and Mathematics Education Conference; Dublin, Ireland. June 24–25; 2014. [ Google Scholar ]
• Pásztor Attila, Kupiainen Sirkku, Hotulainen Risto, Molnár Gyöngyvér, Csapó Benő. Comparing Finnish and Hungarian Fourth Grade Students’ Inductive Reasoning Skills; Paper presented at the EARLI SIG 1 Conference; Helsinki, Finland. August 29–31; 2018. [ Google Scholar ]
• Sandberg Elisabeth Hollister, McCullough Mary Beth. The development of reasoning skills. In: Sandberg Elisabeth Hollister, Spritz Becky L., editors. A Clinician’s Guide to Normal Cognitive Development in Childhood. Routledge; New York: 2010. pp. 179–89. [ Google Scholar ]
• Schraw Gregory, Dunkle Michael E., Bendixen Lisa D. Cognitive processes in well-defined and ill-defined problem solving. Applied Cognitive Psychology. 1995; 9 :523–38. doi: 10.1002/acp.2350090605. [ CrossRef ] [ Google Scholar ]
• Schweizer Fabian, Wüstenberg Sascha, Greiff Samuel. Validity of the MicroDYN approach: Complex problem solving predicts school grades beyond working memory capacity. Learning and Individual Differences. 2013; 24 :42–52. doi: 10.1016/j.lindif.2012.12.011. [ CrossRef ] [ Google Scholar ]
• Stadler Matthias, Becker Nicolas, Gödker Markus, Leutner Detlev, Greiff Samuel. Complex problem solving and intelligence: A meta-analysis. Intelligence. 2015; 53 :92–101. doi: 10.1016/j.intell.2015.09.005. [ CrossRef ] [ Google Scholar ]
• Sternberg Robert J. Handbook of Human Intelligence. Cambridge University Press; New York: 1982. [ Google Scholar ]
• Sternberg Robert J., Kaufman Scott Barry. The Cambridge Handbook of Intelligence. Cambridge University Press; New York: 2011. [ Google Scholar ]
• van de Schoot Rens, Lugtig Peter, Hox Joop. A checklist for testing measurement invariance. European Journal of Developmental Psychology. 2012; 9 :486–92. doi: 10.1080/17405629.2012.686740. [ CrossRef ] [ Google Scholar ]
• Vollmeyer Regina, Burns Bruce D., Holyoak Keith J. The impact of goal specificity on strategy use and the acquisition of problem structure. Cognitive Science. 1996; 20 :75–100. doi: 10.1207/s15516709cog2001_3. [ CrossRef ] [ Google Scholar ]
• Welter Marisete Maria, Jaarsveld Saskia, Lachmann Thomas. Problem space matters: The development of creativity and intelligence in primary school children. Creativity Research Journal. 2017; 29 :125–32. doi: 10.1080/10400419.2017.1302769. [ CrossRef ] [ Google Scholar ]
• Wenke Dorit, Frensch Peter A., Funke Joachim. Complex Problem Solving and intelligence: Empirical relation and causal direction. In: Sternberg Robert J., Pretz Jean E., editors. Cognition and Intelligence: Identifying the Mechanisms of the Mind. Cambridge University Press; New York: 2005. pp. 160–87. [ Google Scholar ]
• Wittmann Werner W., Hattrup Keith. The relationship between performance in dynamic systems and intelligence. Systems Research and Behavioral Science. 2004; 21 :393–409. doi: 10.1002/sres.653. [ CrossRef ] [ Google Scholar ]
• Wu Hao, Molnár Gyöngyvér. Interactive problem solving: Assessment and relations to combinatorial and inductive reasoning. Journal of Psychological and Educational Research. 2018; 26 :90–105. [ Google Scholar ]
• Wu Hao, Molnár Gyöngyvér. Logfile analyses of successful and unsuccessful strategy use in complex problem-solving: A cross-national comparison study. European Journal of Psychology of Education. 2021; 36 :1009–32. doi: 10.1007/s10212-020-00516-y. [ CrossRef ] [ Google Scholar ]
• Wu Hao, Saleh Andi Rahmat, Molnár Gyöngyvér. Inductive and combinatorial reasoning in international educational context: Assessment, measurement invariance, and latent mean differences. Asia Pacific Education Review. 2022; 23 :297–310. doi: 10.1007/s12564-022-09750-z. [ CrossRef ] [ Google Scholar ]
• Wüstenberg Sascha, Greiff Samuel, Funke Joachim. Complex problem solving—More than reasoning? Intelligence. 2012; 40 :1–14. doi: 10.1016/j.intell.2011.11.003. [ CrossRef ] [ Google Scholar ]
• Wüstenberg Sascha, Greiff Samuel, Molnár Gyöngyvér, Funke Joachim. Cross-national gender differences in complex problem solving and their determinants. Learning and Individual Differences. 2014; 29 :18–29. doi: 10.1016/j.lindif.2013.10.006. [ CrossRef ] [ Google Scholar ]

Define Cognitive Psychology: Meaning and Examples

Categories Cognition

Cognitive psychology is defined as the study of internal mental processes. Such processes include thinking, decision-making, problem-solving , language, attention, and memory. The cognitive approach in psychology is often considered part of the larger field of cognitive science. This branch of psychology is also related to several other disciplines, including neuroscience, philosophy, and linguistics.

To define cognitive psychology , it is important to understand the core focus of the cognitive approach, which is to psychology is on how people acquire, process, and store information. Cognitive psychologists are interested in studying what happens inside people’s minds.

Table of Contents

How Do We Define Cognitive Psychology?

While the cognitive approach to psychology is a popular branch of psychology today, it is actually a relatively young field of study. Until the 1950s, behaviorism was the dominant school of thought in psychology.

Between 1950 and 1970, the tide began to shift against behavioral psychology to focus on topics such as attention, memory, and problem-solving.

Often referred to as the cognitive revolution, this period generated considerable research on subjects, including processing models, cognitive research methods , and the first use of the term “cognitive psychology.”

The term “cognitive psychology” was first used in 1967 by American psychologist Ulric Neisser in his book Cognitive Psychology . Neisser went on to define cognitive psychology by saying that cognition involves “all processes by which the sensory input is transformed, reduced, elaborated, stored, recovered, and used.” Neisser also suggested that given such a broad and sweeping definition, cognition was involved in anything and everything that people do.

Essentially, all psychological events are cognitive events. Today, the American Psychological Association defines cognitive psychology as the “study of higher mental processes such as attention, language use, memory, perception, problem solving, and thinking.”

Understanding How We Define Cognitive Psychology

Some factors that contributed to the rise of the cognitive approach to psychology. These include:

• Dissatisfaction with the behaviorist approach : Behaviorism largely focused on looking at external influences on behavior. What the behavioral perspective failed to account for was the internal processes that influence human behavior. The cognitive approached emerged to fill this void.
• The increased use of computers : Scientists began comparing the way the human mind works to how a computer stores information on a hard drive. The information-processing model became popular as a result.

Thanks to these influences, the cognitive approach became an increasingly important branch of psychology. Behaviorism lost its hold as a dominant perspective, and psychologists began to look more intensely at memory, learning, language, and other internal processes.

Research Methods Used in Cognitive Psychology

Psychologists who use the cognitive approach rely on rigorous scientific methods to research the human mind. In many cases, this involves using experiments to determine if changes in an independent variable result in changes in the dependent variable.

Some of the main research methods used in the cognitive approach include:

Experimental Research

This involves conducting controlled experiments to manipulate variables and observe their effects on cognitive processes. Experiments are often conducted in laboratory settings to maintain control over extraneous variables.

For example, a memory experiment might involve randomly assigning participants to take a series of memory tests to determine if a certain change in conditions led to changes in memory abilities.

By using rigorous empirical methods, psychologists can accurately determine that it is the independent variable causing the changes rather than some other factor.

Cognitive Neuropsychology

This approach studies cognitive function by examining individuals with brain injuries or neurological disorders. By observing how damage to specific brain areas affects cognitive processes, researchers can infer the functions of those areas.

Neuroimaging Techniques

Cognitive neuroscientists use techniques to examine brain activity during cognitive tasks. Some of these neuroimaging tools include:

• Functional magnetic resonance imaging (fMRI)
• Positron emission tomography (PET)
• Electroencephalography (EEG)

Eye-Tracking Studies

Eye-tracking technology is used to study visual attention and perception by recording eye movements as participants view stimuli. This method provides insights into how people process visual information and allocate attention.

Areas of Study in the Cognitive Psychology

As mentioned previously, any mental event is considered a cognitive event. There are a number of larger topics that have held the interest of cognitive psychologists over the last few decades. These include:

Information-Processing

As you might imagine, studying what’s happening in a person’s thoughts is not always the easiest thing to do.

Very early in psychology’s history, Wilhelm Wundt attempted to use a process known as introspection to study what was happening inside a person’s mind. This involved training people to focus on their internal states and write down what they were feeling, thinking, or experiencing. This approach was extremely subjective, so it did not last long as a cognitive research tool.

Cognitive psychologists have developed different models of thinking to study the human mind. One of the most popular of these is the information-processing approach .

In this approach, the mind is thought of as a computer. Thoughts and memories are broken down into smaller units of knowledge. As information enters the mind through the senses, it is manipulated by the brain, which then determines what to do with it.

Some information triggers an immediate response. Other units of information are transferred into long-term memory for future use.

Units of Knowledge

Cognitive psychologists often break down the units of knowledge into three different types: concepts, prototypes, and schemas.

A concept is basically a larger category of knowledge. A broad category exists inside your mind for these concepts where similar items are grouped together. You have concepts for things that are concrete such as a dog or cat, as well as concepts for abstract ideas such as beauty, gravity, and love.

A prototype refers to the most recognizable example of a particular concept. For example, what comes to mind when you think of a chair. If a large, comfy recliner immediately springs to mind, that is your prototype for the concept of a chair. If a bench, office chair, or bar stool pops into your mind, then that would be your prototype for that concept.

A schema is a mental framework you utilize to make sense of the world around you. Concepts are essentially the building blocks that are used to construct schemas, which are mental models for what you expect from the world around you. You have schemas for a wide variety of objects, ideas, people, and situations.

So what happens when you come across information that does not fit into one of your existing schemas? In some cases, you might even encounter things in the world that challenges or completely upend the ideas you already hold.

When this happens, you can either assimilate or accommodate the information. Assimilating the information involves broadening your current schema or even creating a new one. Accommodating the information requires changing your previously held ideas altogether. This process allows you to learn new things and develop new and more complex schemas for the world around you.

The Cognitive Approach to Attention

Attention is another major topic studied in the field of cognitive psychology. Attention is a state of focused awareness of some aspect of the environment. This ability to focus your attention allows you to take in knowledge about relevant stimuli in the world around you while at the same time filtering out things that are not particularly important.

At any given moment in time, you are taking in an immense amount of information from your visual, auditory, olfactory, tactile, and taste senses. Because the human brain has a limited capacity for handling all of this information, attention is both limited and selective.

Your attentional processes allow you to focus on the things that are relevant and essential for your survival while filtering out extraneous details.

The Cognitive Approach to Memory

How people form, recall, and retain memories is another important focus in the cognitive approach. The two major types of memory that researchers tend to look at are known as short-term memory and long-term memory.

Short-Term Memory

Short-term memories are all the things that you are actively thinking about and aware of at any given moment. This type of memory is both limited and very brief.

Estimates suggest that you can probably hold anywhere from 5 to 9 items in short-term memory for approximately 20 to 30 seconds.

Long-Term Memory

If this information is actively rehearsed and attended to, it may be transferred to what is known as long-term memory. As the name suggests, this type of memory is much more durable. While these longer-lasting memories are still susceptible to forgetting , the information retained in your long-term memory can last anywhere from days to decades.

Cognitive psychologists are interested in the various processes that influence how memories are formed, stored, and later retrieved. They also look at things that might interfere with the formation and storage of memories as well as various factors that might lead to memory errors or even false memories.

The Cognitive Approach to Intelligence

Human intelligence is also a major topic of interest within cognitive psychology, but it is also one of the most hotly debated and sometimes controversial. Not only has there been considerable questioning over how intelligence is measured (or if it can even be measured), but experts also disagree on exactly how to define intelligence itself.

One survey of psychologists found that experts provided more than 70 different definitions of what made up intelligence. While exact definitions vary, many agree that two important themes include both the ability to learn and the capacity to adapt as a result of experience.

Researchers have found that more intelligent people tend to perform better on tasks that require working memory , problem-solving, selective attention , concept formation, and decision-making. When looking at intelligence, cognitive psychologists often focus on understanding the mental processes that underlie these critical abilities.

Cognitive Development

Cognitive development refers to the changes in cognitive abilities that occur over the lifespan, from infancy through old age. Cognitive psychologists study the development of perception, attention, memory, language, and reasoning skills.

Research in cognitive development explores factors that influence cognitive growth, such as genetics, environment, and social interactions.

Language is a complex cognitive ability that enables communication through the use of symbols and grammatical rules. Cognitive psychologists study the cognitive processes involved in language comprehension, production, and acquisition.

Research in language examines topics such as syntax, semantics, pragmatics, and the neurobiological basis of language processing.

Reasons to Study Cognitive Psychology

Because cognitive psychology touches on many other disciplines, this branch of psychology is frequently studied by people in different fields. Even if you are not a psychology student, learning some of the basics of cognitive psychology can be helpful.

The following are just a few of those who may benefit from studying cognitive psychology.

• Students interested in behavioral neuroscience, linguistics, industrial-organizational psychology, artificial intelligence, and other related areas.
• Teachers, curriculum designers, instructional developers, and other educators may find it helpful to learn more about how people process, learn, and remember information.
• Engineers, scientists, artists, architects, and designers can all benefit from understanding internal mental states and processes.

Key Points to Remember About Cognitive Approach

• The cognitive approach emerged during the 1960s and 70s and has become a major force in the field of psychology.
• Cognitive psychologists are interested in mental processes, including how people take in, store, and utilize information.
• The cognitive approach to psychology often relies on an information-processing model that likens the human mind to a computer.
• Findings from the field of cognitive psychology apply in many areas, including our understanding of learning, memory, moral development, attention, decision-making, problem-solving, perceptions, and therapy approaches such as cognitive-behavior therapy and rational emotive behavior therapy.

Airenti G. (2019). The place of development in the history of psychology and cognitive science .  Frontiers in Psychology ,  10 , 895. https://doi.org/10.3389/fpsyg.2019.00895

Legg S, Hutter M.  A collection of definitions of intelligence. Frontiers in Artificial Intelligence and Applications . 2007;157:17-24.

Miller, G. A. (1956). The magical n u mber seven, plus or minus two: Some limits on our capacity for processing information .  Psychological Review, 63 (2), 81–97. https://doi.org/10.1037/h0043158

Neisser U. Cognitive Psychology . Meredith Publishing Company; 1967.

Image: Julia Freeman-Woolpert / freeimages.com

• Mental Health

What Is Cognitive Psychology?

Cognitive psychology is the branch of psychology dedicated to studying how people think. The cognitive perspective in psychology focuses on how the interactions of thinking, emotion, creativity, and problem-solving abilities affect how and why you think the way you do. Cognitive psychology attempts to measure different types of intelligence, determine how you organize your thoughts, and compare different components of cognition. 

What Does a Cognitive Psychologist Do?

Cognitive psychologists do clinical research, training, education, and clinical practice. They use the insights gained from studying how people think and process information to help people develop new ways of dealing with problem behaviors and live better lives. Cognitive psychologists have special knowledge of applied behavior analysis, behavior therapy, learning theories, and emotional processing theories. 

They know how to apply this knowledge to the human condition and use it in the treatment of: 

• Anxiety disorders
• Academic performance
• Personality disorders
• Substance abuse
• Depressive disorders
• Relationship problems
• Autism spectrum disorder
• Emotional regulation 

The History of Cognitive Psychology

Cognitive psychology gained popularity in the 1950s to 1970s as researchers became more interested in how thinking affects behavior. This period is called the "cognitive revolution" and represented a shift in thinking and focus for psychologists. Before this time, the behaviorist approach dominated psychology. The behaviorists only studied external behavior that could be measured.

Behaviorists believed it was pointless to try to study the mind because there was no way to see or objectively measure what happened in someone's thoughts. The mind was seen as a black box that couldn't be measured. 

The cognitive approach gave rise to the idea that internal mental behavior could be studied using experiments. Cognitive psychology assumes that there is an internal process that occurs between when a stimulus happens and when you respond to it.

These processes are called mediational processes and can involve memory, perception, attention, problem-solving, or other processes. Cognitive psychologists believe if you want to understand behavior, you have to understand the mediational processes that cause it.

Cognitive Psychology Examples

Some examples of studies and work in cognitive psychology include: 

Experts think differently. Beginners think literally when they try to solve a problem. They tend to focus on the surface details when they're presented with an unfamiliar situation. Experts are able to see the underlying connections and think of the problem more abstractly. 

Short-term memory. Your short-term memory is probably a lot shorter than you think. A classic study in cognitive psychology found that participants in a study could only recall 10% of random three-letter strings after 18 seconds. After 3 seconds, the participants could recall 80% of the letter strings, so there was a significant drop after 15 additional seconds. 

Mapping the brain. Some cognitive psychologists are working on the BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative. This project has been compared to the human genome project. It's an attempt to learn more about the 100 billion brain cells, including the connections between them and how they relate to behavior and health.

Cognitive Psychology Perspective in Practice

Cognitive psychology perspectives can be used to improve many areas of life, including how children learn. Researchers Pooja K. Agarwal and Henry L. Roediger III used insights from their cognitive psychology studies to develop better practices to encourage learning in the classroom. They used experiments to determine how students learn and apply their knowledge as well as disprove outdated theories. 

Experts used to believe that memory could be improved with practice, a theory that has been disproven. Another popular theory that has been debunked is that errors interfere with learning. The opposite is actually true. You learn from your mistakes, so making errors improves your ability to learn. While most educators have moved beyond those theories, there are still some unproven ones that linger, like the notion that different people have different learning styles. 

In addition to disproving theories that don't work, cognitive psychology shines a light on theories that do work. After combing through over 100 years of studies, researchers found four different practices that increased students' ability to learn: 

• Retrieval practice, which is quickly bringing the information you're learning to mind
• Getting feedback that lets you know what you don't know
• Spaced practice, which is returning to the material periodically over time
• Interleaving, which is practicing a mix of skills

Careers in Cognitive Psychology

Cognitive psychologists can work at universities doing research or teaching. They can also work in the private sector in organizational psychology, software development, or human-computer interaction. Another option for cognitive psychologists is working in a clinical setting treating patients for issues related to mental processes, like: 

• Alzheimer's disease
• Speech problems
• Memory issues
• Sensory difficulties

You can work in some entry-level jobs with a bachelor's degree in cognitive psychology, but most opportunities will be available to people with a master's or doctorate degree. Most research done by people with master's degrees is supervised by cognitive psychologists with doctorate degrees. 

Top doctors in ,

Find more top doctors on, related links.

• Mental Health Home
• Mental Health News & Features
• Mental Health Reference
• Mental Health Quizzes
• Mental Health Slideshows
• Mental Health Blogs
• Mental Health Videos
• Find a Psychiatrist
• Anxiety & Panic Disorders
• Bipolar Disorder
• Crisis Assistance
• Eating Disorders
• Health & Balance
• Personality Disorders
• Schizophrenia
• Social Media and Mental Health
• Stress Management
• Substance Abuse & Addiction
• More Related Topics

• Earth Science
• Physical Science

Social Science

• Medical Science
• Mathematics
• Paleontology

Cognitive Psychology Problem Solving Cognitive Psychologists

Cognitive psychologists define problem solving as the process that people use when they are confronted with unfamiliar tasks. Simply stated, a problem is any question or matter involving doubt, uncertainty or difficulty.Problem solving is a higher-level cognitive process that includes a variety of mental activities such attention, perception, memory, language and reasoning. It is a conscious, controlled process.

Research has shown that problem solving is a cycle that includes the following phases: 1. Recognize or identify the problem. 2. Define the problem and determine its limits. 3. Develop a solution strategy. 4. Organize knowledge about the problem. 5. Allocate and use the mental and physical resources needed to solve the problem. 6. Monitor progress toward the solution. 7. Evaluate the solution for accuracy.

This problem-solving cycle is a model only. Typically, this is how people work through a problem. Depending on the nature and complexity of the problem, some steps may be skipped or combined.

Types of Problems

Problems are classified as well-defined or ill-defined. A well-defined problem is one that has a clear goal, a specific path to the solution and clearly visible obstacles based on the information given. For example, calculating the sales tax and total cost of an item for purchase is a simple, clearly defined process:

Price x Tax Rate (Percent) = Sales Tax + Price = Total Cost

Well-defined problems can be solved using a formula or algorithm; a step-by- step process that will always produce the correct result.

Ill-defined problems are not clear-cut. There is no obvious path to the solution. These problems require investigation to define, understand and solve. For example, building a child’s tree house involves many problems that must be solved, such as:

• How big is the tree? • Will the tree house be a platform or an enclosed space? • What kind of wood will be best for the tree house? • How will the child get into the tree house?

No simple formula can be used to solve an ill-defined problem. The problem solver must gather and analyze information in order to find a solution. But ill-defined problems may include sub-problems that are well-defined. So the overall solution may require a combination of strategies.

Problem Solving Strategies

Many strategies and complex methodologies are available for problem solving. The strategies used are determined by the nature of the problem and what level the problem-solver is in the aforementioned seven-step cycle. For example, researcher John Malouff identified more than 50 problem solving strategies. Here are some common strategies:

• Analogy: Use what has been leaned with similar problems. • Brainstorm: List all options without evaluation, then go back, analyze and select one. • Break down (simplify): Break a large complex problem into smaller, simpler     problems. • Hypothesis testing (scientific method): Develop a hypothesis about the cause of the problem, collect information and test the hypothesis. • Means/ends analysis: Choose and take an action at each phase of the problem solving cycle to move closer to the goal. • Research: Use existing ideas and adapt them to use for similar problems. • Trial and Error: Test solutions until the right one is found.

Related posts: