the study of human problem solving strategies

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

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12 Problem Solving

Stephen K. Reed, Department of Psychology, San Diego State University

• Published: 05 December 2014
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Solving a problem results in obtaining a desired goal through the use of higher mental functions, including reasoning and planning. Problems—such as those requiring arrangement, transformation, and inducing structure—can be classified based on the cognitive skills that are required to solve them. Although general heuristics are sufficient for solving knowledge-lean problems, organized knowledge structures (schemas) are needed to solve knowledge-rich problems. Using analogous solutions is often helpful for both types of problems. Mappings across concepts, problem states, and operations relate the structure of analogous problems and of different solutions to the same problem. EUREKA, CLARION, and ACT are examples of cognitive architectures that apply to problem solving. Underinvestigated topics include problems with insufficient information, estimated answers, complex problem solving, and collaborative problem solving.

The APA Dictionary of Psychology ( VandenBoss, 2006 ) defines problem solving as:

The process by which individuals attempt to overcome difficulties, achieve plans that move them from a starting situation to a desired goal, or reach conclusions through the use of higher mental functions such as reasoning and creative thinking.

Reviewing problem-solving research and theories is a challenge because this definition is so inclusive. Our task is made easier, however, because of previous reviews. In particular, I have built on problem-solving chapters by Bassok and Novick (2012) and by VanLehn (1989) . The Bassok and Novick chapter appears in the Oxford Handbook of Thinking and Reasoning and emphasizes research by cognitive psychologists. The VanLehn chapter appears in Foundations of Cognitive Science and includes a computational approach to problem solving. My objective here is to present both research findings and computational models while extending the contributions made in these previous chapters.

There are many different kinds of problems that fit the definition of problem solving in the first paragraph. The first section of this chapter therefore includes a taxonomy that partitions problems into categories based on the skills required to solve them. This section also describes major historical approaches. The second section discusses the role of organized knowledge structures, labeled schemas , in supporting the development of expertise. The third section explores relations between different problems and between different solutions to the same problem. The fourth section illustrates how cognitive architectures have enhanced our understanding through embedding problem solving within broad theoretical frameworks. The final section proposes future directions by identifying underresearched and developing topics.

Kinds of Problems

To make a review of problem solving more manageable, Greeno (1978) divided problems into three categories based on the cognitive skills required to solve them. He labeled the categories arrangement problems, transformation problems, and inducing structure problems. Arrangement problems require rearranging parts to satisfy some criterion, such as creating a word from the letters ARAGMAN. Transformation problems require transforming an initial state into a goal state, such as moving four rings from peg A to peg C under the constraint that a larger ring can not be placed on a smaller ring. The goal state is known in transformation problems: the stack of rings on peg C varies from the largest on the bottom to the smallest on the top. Inducing structure problems require identifying relations among the parts of a problem and then using that structure to produce the solution. Examples include series completion problems such as producing the next four letters in the series r s c d s t d e t u e f . Discovering the relations among parts is crucial for solving both arrangement and inducing structure problems, but parts cannot be rearranged in inducing structure problems.

I begin by discussing problems that fall into each of these three categories because it provides an opportunity to write about two important movements during the 20th-century work on problem solving. Gestalt psychologists focused on arrangement problems during the earlier half of the century because these problems fit into their theoretical framework that a pattern is more than the sum of its parts. Stimulated by applications of computer science to human problem solving ( Newell, Shaw, & Simon, 1958 ), transformation problems began to play an important role in the second half of the century. Inducing structure has a more eclectic history, with psychometric, artificial intelligence, and information processing approaches all making significant contributions.

The classification of problems into one of the three categories should not imply that all problems fit into a single category. Greeno (1978) admitted that complex problems require multiple skills. For instance, playing chess requires arranging chess pieces to accomplish the goal of winning the game, moving (transforming) pieces toward particular arrangements for executing a plan, and inducing structure to analyze the opponent’s plan.

Arrangement Problems and Gestalt Psychology

Gestalt psychologists were primarily interested in problems that required arranging the parts to find new relations that achieved a goal. Kohler (1925) described an early example in his book The Mentality of Apes . A cage of a chimpanzee contained fruit hanging from the top and sticks and crates on the floor. The chimp could obtain the fruit by using a stick after stacking and climbing on the crates. Solving the problem, according to the Gestalt analysis, depended on reorganizing the objects into a new structure.

Another famous example is Duncker’s (1945) radiation problem. A medical procedure required using radiation to destroy a tumor without destroying the healthy tissue that surrounds it. A solution is to divide the radiation into multiple rays that converge on the location of the tumor. Intense radiation occurs only at the point of convergence so does not harm healthy tissue.

Gestalt psychologists used the term insight to describe the sudden discovery of a correct arrangement of parts following a succession of incorrect arrangements ( Kohler, 1947 ). Metcalfe and Wiebe (1987) empirically evaluated this concept by giving students nonroutine problems such as planting four trees exactly the same distance from the others. Every 15 seconds, the participants had to indicate on a 7-point scale how close they believed they were to solving the problem. Although the highest rating was the most frequent rating at the solution, the lowest rating was the most frequent rating 15 seconds before the solution. The findings support the construct of insight in which solutions occur very suddenly following a perceived lack of progress.

One interpretation of such findings is that insight occurs when solvers remove self-imposed constraints ( Knoblich, Ohlsson, Haider, & Rhenius, 1999 ). For example, people typically attempt to solve the four-trees problem in two dimensions although this constraint is not mentioned in the problem. The solution requires a three-dimensional arrangement.

Knoblich and his coauthors evaluated their theory of constraint relaxation by asking participants to rearrange matchsticks, including the ones shown in Figure 12.1 . The objective is to move a single stick to turn an incorrect arithmetic statement into a correct one. The stick cannot be discarded but must occupy a new position in the equation. The findings confirmed the predictions based on constraint relaxation. Type a problems are solved by modifying the numerals (changing IV to VI) and were the easiest. Type b problems are solved by modifying arithmetic operations (moving a match stick from the equals sign to the minus sign). Type c problems are solved by creating more than one equals sign and were the most difficult. The three types of problems became equally easy after participants realized that they could modify and create equal signs.

Matchstick problems.

A question regarding the restructuring that leads to insight is whether restructuring involves controlled search processes or whether it involves an automatic redistribution of activation in long-term memory. Ash and Wiley (2006) investigated this question by determining whether individual differences in working memory span predicted performance on the initial search and restructuring phases. Working memory span did predict success on problems that required both initial search and restructuring but did not predict success on problems that isolated the restructuring phase. The findings are consistent with the interpretation that restructuring involves an automatic redistribution of activation.

Although the Gestalt approach emphasized problem representations, and Newell and Simon (1972) emphasized searching for a solution, both search and representation are important in solving problems ( Bassok & Novick, 2012 ). The next section focuses on the search process.

Transformation Problems and Search

The study of transformation problems—the transformation of an initial problem state into a goal state—became an important area of research as computers became a source of symbol manipulation. Newell, Shaw, and Simon (1958) made the connection between computers and human problem solving in their Psychological Review article “Elements of a Theory of Human Problem Solving.” Their theory proposed (1) a control system consisting of a number of memories containing symbols interconnected by various relations, (2) primitive information processes that operate on information in the memories, and (3) a set of rules for combining these processes into whole programs.

A program constitutes a theory that can make precise predictions. As stated by Newell, Shaw, and Simon:

The ability to specify programs precisely, and to infer accurately the behavior they produce, drives from the use of high-speed digital computers. Each specific theory—each program of information processes that purports to describe some human behavior—is coded for a computer. That is, each primitive information process is coded to be a separate computer routine, and a “master” routine is written that allows these primitive processes to be assembled into any system we wish to specify. Once this has been done, we can find out exactly what behavior the purported theory predicts by having the computer “simulate” the system. (pp. 152–153)

The authors designed the programs to simulate human problem solving by comparing the behavior predicted by the program with actual behavior observed in experimental settings. The promise of the digital computer was that it provided a device for determining what behavior is implied by a program and for subsequently modifying the program if the predictions failed. Programming required a detailed specification of the operations, which enabled theorists to evaluate whether the operations were sufficient to produce the behavior. It thereby avoided the vagueness that limited other theories of higher mental processes ( Newell et al., 1958 ).

The interest in writing simulation programs was accompanied by an interest in writing artificial intelligence (AI) programs to enable computers to produce, rather than simulate, intelligent behavior. Early examples of these activities are provided in the book GPS: A Case Study in Generality and Problem Solving ( Ernst & Newell, 1969 ). The General Problem Solver (GPS) had the objective of using generic principles to solve a variety of problems including Tower of Hanoi, Missionaries and Cannibals, integration, and logical proofs. The GPS was not completely general because it solved only transformation problems by using the means-end analysis heuristic. Means-end analysis attempts to successively eliminate differences between the initial state and the goal state until the program arrives at the goal state. This heuristic is often successful on transformation problems because these problems have a well-defined goal state: all rings are moved from peg A to peg C or all missionaries and cannibals are moved across the river. In contrast, producing the goal state is typically required in arrangement problems such as solving an anagram or Dunker’s radiation problem.

Solution of a logic problem using means-end analysis.

These initial AI programs provided possible theories for how people solve problems. One example is the construction of logical proofs, a task that Newell and Simon (1972) extensively analyzed in their classic book Human Problem Solving . The problem solver was given 12 rules for manipulating letters connected by dots •, wedges v, horseshoes ⊃, and tildes ~. These connectives are used to represent and, or, implies , and no t in logic but were not interpreted for the participants. The 12 rules enable problem solvers to modify logical expressions until they have constructed a proof by transforming the initial state into the goal state. For instance, the initial state could be A ⊃ B and the goal state could be ~ B ⊃ ~ A .

Means-end analysis was implemented in the GPS for logic problems by including a table of connections in the program that showed which of six differences could be modified by each of the 12 rules. Figure 12.2 shows a simplified table of connections consisting of three rules and three differences for solving the A ⊃ B problem.

Transforming the initial state A ⊃ B into the goal state ~ B ⊃ ~ A requires eliminating differences in both the sign and position of the letters. The table of connections reveals that both Rules 2 and 3 can change a sign, but Rule 2 cannot be applied because it has different connectives than the initial state. Application of Rule 3 to the initial state changes the sign of A to negation. However, the resulting problem state, ~ A v B , now differs from the goal state in the sign of B , position of the letters, and connective. The application of Rule 1 to Line 2 produces an expression that can be changed to the goal state through the reapplication of Rule 3. Newell and Simon (1972) asked their participants to verbalize their thoughts as they worked on the problems. Many aspects of their thinking corresponded to means-end analysis used in the GPS.

A difference between arrangement and transformation problems is that solvers of transformation problems should realize that they are making progress as they gradually reduce differences between the current problem state and the goal state. Metcalfe and Wiebe (1987) confirmed this difference by finding higher ratings of approaching the solution as their participants continued to work on the transformation problems. Both arrangement and transformation problems received a high rating at the solution, but only transformation problems received a high rating 15 seconds before the solution.

An important theoretical component of Newell and Simon’s (1972) theory of problem solving is the problem space . The search space specifies the permissible actions (legal moves) at each problem state. Figure 12.3 shows the problem space for the five missionaries-cannibals problem ( Simon & Reed, 1976 ):

Five missionaries and five cannibals who have to cross a river find a boat, but the boat is so small that it can hold no more than three persons. If the cannibals outnumber the missionaries on either bank of the river or in the boat at any time, the missionaries will be eaten. Find the simplest schedule of crossings that will allow everyone to cross safely. At least one person must be in the boat at each crossing.

Problem space for the five Missionaries and Cannibals problems.

Each oval in Figure 12.3 is a problem state of the form MC/MC* in which the first MC is the number of missionaries (M) and cannibals (C) on the initial bank, and the second MC is the number of missionaries and cannibals across the river. The asterisk shows the location of the boat, and the links show the number of missionaries and cannibals in the boat. Solving the problem requires transforming the initial state A into the goal state Z . The problem space reveals a number of important characteristics of the problem, such as there are four legal moves at the initial state, state J is the end of a blind alley that requires reversing the two previous moves, and the minimal solution requires 11 moves.

The search space differs from the problem space because it reveals which moves are considered by the problem solver ( Newell & Simon, 1972 ). For instance, undergraduates required an average of 30 moves to solve the problem without a hint and 20 moves to solve the problem when given a subgoal that at some point there will be 3 cannibals and 0 missionaries across the river without the boat (state L ). Simon and Reed (1976) proposed a strategy-shift model to predict the average number of times students in each group would visit each of the problem states in Figure 12.3 . The model assumes that students begin with a balance strategy in which they attempt to equalize the number of missionaries and cannibals across the river, as in state D . They then switch to a means-end strategy in which they attempt to take as many people across the river as possible (3) and bring back as few as possible (1). The probability of switching strategies is higher for the subgoal group, which helps them avoid the blind alley ending in state J . The strategy-shift model is consistent with the “unbalanced” subgoal—3 cannibals and 0 missionaries across the river.

Inducing Structure and Reasoning

The sections on arrangement and transformation tasks contained research that is typically included in problem-solving chapters. In contrast, tasks that require inducing structure might appear in reasoning chapters. An inclusive definition of problem solving, such as the one at the beginning of this chapter, includes reasoning, but there is a distinction between reasoning and problem solving. Holyoak and Morrison (2012) state that reasoning places an emphasis on drawing inferences (conclusions) from some initial information (premises) and has a foundation in logic. Problem solving involves a course of action to achieve a goal.

I focus on a particular reasoning task (the four-card selection problem) in this section for three reasons. First, the task has been one of the most widely studied tasks in the reasoning literature. Second, it illustrates how inducing structure differs from arranging and transforming components. Inducing structure is similar to arrangement problems because it is necessary to discover the relations among the components of the problem ( Greeno, 1978 ). However, unlike arrangement problems, these components are static and cannot be rearranged. Third, research on this task illustrates the challenge of identifying the extent to which reasoning depends on general knowledge. The arrangement and transformation problems in the previous two sections consisted primarily of puzzles that did require extensive knowledge about a particular domain. The four-card selection problem illustrates how our familiarity with the content of information in rules influences our ability to evaluate those rules.

The four-card selection problem ( Wason & Johnson-Laird, 1972 ) requires deciding which one of four cards needs to be turned over to evaluate a conditional rule; for example, if there is a D on one side of the card, then there is a 3 on the other side. The four cards in this example either display the letter D , the letter K , the number 3 , or the number 7 . The experimenter informs participants that each of the cards contains a letter on one side and a number on the other side. The answer is that it is necessary to turn over the D card and the 7 card but only 5 of 128 participants turned over only the two correct cards ( Wason & Shapiro, 1971 ).

Wason and Shapiro (1971) hypothesized that performance would dramatically improve if the conditional rules had realistic rather than abstract content, a prediction that was confirmed in a letter-sorting task ( Johnson-Laird, Legrenzi, & Legrenzi, 1972 ). The task consisted of four envelopes. Two were face up, revealing either a 50 lira or a 40 lira stamp. Two were face down, revealing either a sealed or an unsealed envelope. Participants were told to imagine that they worked in a post office and had to enforce the rule “If a letter is sealed then it has a 50-lira stamp on it.” Most participants (17 of 24) accurately selected the two envelopes required to enforce the rule.

Although Wason and Shapiro (1971) argued that conditional reasoning is vastly improved with realistic content, Griggs and Cox (1982) questioned whether the letter task required conditional reasoning. Their memory-retrieval explanation proposed that the British participants did the task by recalling their experience in placing more postage on sealed envelopes. Griggs and Cox therefore predicted that their American students, who lacked such experience, would do poorly on the task. As predicted, American students did poorly on the unfamiliar letter task but excelled in evaluating a familiar drinking-age rule “If a person is drinking beer then the person must be over 19 years of age.”

Griggs and Cox’s findings are discouraging because they support the conclusion that people are very limited in evaluating conditional rules unless the rules contain familiar content, in which case reasoning is not required. A more optimistic view of reasoning is that people do well at conditional reasoning if the content is familiar at a general, schematic level. For instance, pragmatic reasoning schemata are organized knowledge structures that enable us to evaluate practical situations such as seeking permission or fulfilling an obligation ( Cheng, Holyoak, Nisbett, & Oliver, 1986 ).

Imagine that you are hired to enforce the rule “If a passenger wishes to enter the country, then he or she must have an inoculation against cholera.” Four cards identify a passenger who wishes to enter, a passenger who does not wish to enter, a passenger who has been inoculated, and a passenger who has not been inoculated. The pragmatic reasoning hypothesis predicts that you can use your schematic knowledge about seeking permission to evaluate this rule even if you have no experience with this particular task. More information is required for the passenger who wishes to enter and for the passenger who has not been inoculated. Research supports the hypothesis that people do much better in evaluating conditional statements involving permission or obligation than in evaluating conditional statements involving arbitrary relations ( Cheng et al., 1986 ).

In summary, the evolution of research on the four-card selection problem reveals the relative influence of concrete and familiar experiences on reasoning. People did very poorly in evaluating the implications of conditional rules involving arbitrary relations between letters and numbers. Performance dramatically improved on concrete versions of the rules but raised the question of whether retrieving experiences from memory removed the need to reason. An intermediate level of abstractness is provided by schemas that generalize the commonality among individual experiences, such as seeking permission or fulfilling an obligation. People can effectively reason about unfamiliar experiences if those experiences can be linked to a familiar schema. Schemas also play an important role in problem solving, as discussed in the next section.

Much of the research on problem solving during the 1970s was influenced by Newell and Simon’s (1972) book in which general strategies (heuristics) such as using means-end analysis or forming subgoals guided the search process. VanLehn (1989) refers to problems such as Missionaries and Cannibals or the Tower of Hanoi as knowledge-lean tasks because they can be solved without prior experience. In contrast, research in the 1980s began to focus on problems from algebra, physics, geometry, and computer programming. These are knowledge-rich tasks that require many hours of instruction ( VanLehn, 1989 ).

Schemas as a Theoretical Construct

Organized knowledge structures called schemas are an effective method for organizing this knowledge. Brewer and Nakamura (1984) described the characteristics of schemas by contrasting them with learning based on stimulus-response (S-R) associations.

S-R learning is based on small units of knowledge. A schema is a larger unit in which knowledge is combined into clusters.

S-R learning requires learning an association between a stimulus and a response. A schema provides a knowledge structure for interpreting and encoding aspects of particular experiences.

S-R learning involves a particular stimulus and response. A schema is more general and represents a variety of experiences.

The association between a stimulus and a response can be learned in a passive manner. Invoking a schema is a more active process in which a particular experience is matched to the schema that best fits the experience.

In her book, Marshall (1995) began by reviewing the historic development of schemas as a theoretical construct by tracing the ideas of Plato, Aristotle, Kant, Bartlett, and Piaget. In her working definition, a schema is a memory organization that can (1) recognize similar experiences; (2) access a general framework that contains essential elements of those experiences; (3) use the framework to draw inferences, create goals, and develop plans; and (4) provide skills and procedures for solving problems in which the framework is relevant.

Marshall then described her research that built on the analysis of addition and subtraction problems. Riley, Greeno, and Heller (1983) had analyzed elementary word problems into change, combine, and compare problems. Kintsch and Greeno (1985) further developed these distinctions as a set schema in which the slots consisted of objects <noun>; quantity <number>; specification <owner>, <location>, <time>; and role <start, transfer, result, superset, subset, largeset, smallset, difference>. Marshall added two additional schematic situations (labeled restate and vary ) and constructed a computer tutor to help students learn to solve multistep arithmetic word problems.

Learning these schematic components is important because they form the building blocks of more complex problems, as in physics ( Sherin, 2001 ) and algebra word problems ( Reed et al., 2012 ). Research shows that algebra word problems are difficult for university students, not only because of algebra, but because students have not adequately learned the change, combine, and compare schema that are the components of both arithmetic and algebra word problems ( Reed et al., 2012 ). Learning these elementary and more advanced schemas supports the development of expertise.

Schemas in Experts

The transition from the study of domain-lean problems in the 1970s to domain-rich problems in the 1980s resulted in investigations of how domain knowledge influenced problem solving. Silver (1981) asked good, average, and poor problem solvers to sort arithmetic word problems into groups based on common solution procedures. The better problem solvers excelled at this assignment, but the weaker problem solvers sorted by story content. For example, they placed problems about hens and rabbits into the same category although the problems required different solutions.

Silver’s finding has been confirmed for many domains and for many levels of expertise. Chi, Glaser, and Reese (1982) asked eight undergraduates and eight advanced physics doctoral students to sort 24 physics problems into categories based on similar solutions. Novices tended to classify problems on the basis of common objects such as inclined planes and springs. Experts tended to classify problems based on physics principles such as the conservation of energy or Newton’s second law (F = MA).

Although such expert-defined schemas are usually very helpful, they can occasionally constrain innovative solutions. Dane (2010) defines cognitive entrenchment as a high level of stability in domain schemas that can cause experts to be inflexible in their thinking. Cognitive entrenchment increases the likelihood of problem-solving fixation and blocks the generation of novel ideas. However, Dane proposes two factors that can reduce cognitive entrenchment. The first is working in a dynamic environment in which one must remain open to a wide range of possibilities and options. The second is focusing attention on outside-domain tasks in which counterexamples and exceptions can increase the flexibility of one’s beliefs.

Schema Abstraction

The ability to see structural commonalities in situations that appear quite different can be very helpful, as illustrated by the use of pragmatic reasoning schema to reason about conditional rules ( Cheng et al., 1986 ); the use of change, combine, and compare schema to classify arithmetic word problems ( Silver, 1981 ); and the use of physics principles to classify physics problems ( Chi et al., 1982 ). All of these situations can be aided by schema abstraction , in which problem solvers focus on the structural relations among the objects (inoculation, cholera, hens, rabbits, springs, inclined planes) rather than on the objects.

A challenge is to encourage noticing these structural relations through schema abstraction—a challenge that was met in a classic study by Gick and Holyoak (1983) . Three years earlier, they published research that demonstrated the difficulty of spontaneously noticing analogous solutions ( Gick & Holyoak, 1980 ). Their goal in this earlier research was to increase the number of convergence solutions to Duncker’s (1945) radiation problem. Participants read an analogous problem in which a general wanted to capture a fortress but could not attack along one road because it was mined. The general therefore divided his army into small groups that simultaneously converged on the fortress from different roads. Very few participants, however, used the analogy unless they were given a hint that the military problem would help them solve the radiation problem.

To spontaneously notice an analogy, people need to think about analogous solutions at a more abstract level so that differences in the objects, such as a fortress and a tumor, would not be a hindrance. Gick and Holyoak (1983) therefore asked participants to compare the similarities between two stories, the military problem and a story about Red Adair whose crew put out fires in oil derricks by using multiple hoses that converged on the site of the fire. Comparing two stories helped participants spontaneously notice the analogy to the radiation problem by creating the more abstract convergence schema shown in Table 12.1 . Simply reading the two stories was insufficient; abstraction depended on the comparison ( Catrambone & Holyoak, 1989 ).

A productive application of this finding occurred in a negotiation training program for management consultants who had approximately 15 years of work experience ( Gentner, Lowenstein, Thompson, & Forbus, 2009 ). The consultants studied two cases of a contingent contract that depended on the outcome of some future event. One group studied the two cases separately, and another group compared the similarities of the two cases. As found in laboratory studies ( Catrambone & Holyoak, 1989 ), the comparison aided schema abstraction. The comparison group was more successful in describing the principles of a contingent contract and in recalling examples of contingent contracts from their own experiences.

Mapping Across Problems and Solutions

Using the solution of the military problem to find a solution to the radiation problem requires finding corresponding objects and relations in the two solutions. As shown in Table 12.1 , the fortress in the military problem corresponds to the tumor in the radiation problem, the large army corresponds to powerful rays, the inability to use a single road corresponds to the inability to use a single pathway, and dividing the army corresponds to dividing the radiation. Establishing these correspondences requires mapping the objects and relations in the military problem to objects and relations in the radiation problem.

Illustration of one-to-one, one-to-many, and partial mappings across knowledge states.

There have been a number of detailed computational models of analogical mappings, including one by Hummel and Holyoak (1997) . Mappings in their model are guided by three constraints:

Structural consistency implies a one-to-one mapping between an element in the source and an element in the target.

Semantic similarity implies that elements with prior semantic similarity (such as joint membership in a taxonomic category) should tend to map to each other.

Pragmatic centrality implies that mappings should give preference to elements that are important for goal attainment.

Structural consistency in the fortress-tumor analogy is illustrated by the one-to-one mapping between objects in the two problems, semantic similarity is illustrated by similar actions (dividing the army and the tumor), and pragmatic centrality is illustrated by the principle of converging forces in both solutions.

Reed (2012) has extended this one-to-one mapping across problems to construct a taxonomy consisting of three types of mappings (one-to-one, partial, and one-to-many, as illustrated in Figure 12.4 ) and four types of situations (problems, solutions, representations, and sociocultural contexts). Mappings across problems and mappings across solutions—different solutions to the same problem—illustrate parts of the taxonomy.

Mapping Across Problems

Most computational models of transfer, including the one proposed by Hummel and Holyoak (1997) , have emphasized one-to-one mappings across isomorphic problems. In contrast, Reed, Ernst, and Banerji (1974) investigated transfer between two problems in which the problem states and moves in one problem had a one-to-many mapping to the problem states and moves in the other problem. One of the problems was the Missionaries and Cannibals (MC) problem, in which three missionaries and three cannibals cross a river using a boat that can hold two people under the constraint that cannibals can never outnumber missionaries. The other problem was the Jealous Husbands (JH) problem:

Three jealous husbands, and their wives, having to cross a river, find a boat. However, the boat is so small that it can hold no more than two persons. Find the simplest schedule of crossings that will permit all six persons to cross the river so that no woman is left in the company of any other woman’s husband unless her own husband is present.

We anticipated, based on our perceived similarity of the two problems, that there would be substantial transfer from one problem to the other. Our first experiment found no transfer, but our second experiment found some transfer when students were informed about the mapping between the two problems: husbands correspond to missionaries and wives correspond to cannibals. However, even with this hint, there was evidence of transfer only from the JH problem to the MC problem. The asymmetrical transfer is consistent with a one-to-many mapping from the MC to the JH problem because moving a missionary does not specify which husband to move and moving a cannibal does not specify which wife to move. Although all missionaries and cannibals are equivalent, all husbands and wives are not because they are paired with each other. For example, the three circles in Figure 12.2 representing one-to-many mappings might represent moving husbands A and B, B and C , and A and C . Each of these moves maps onto moving two missionaries, but moving two missionaries does not specify which two husbands to move. It should therefore be more difficult to map moves from the MC problem to the JH problem because this mapping does not specify a unique move.

Partial mappings are similar to isomorphic mappings because both specify one-to-one mappings between the source and the target. The difference is that isomorphic mappings are sufficient for solving the target problem, whereas partial mappings are not. It may therefore be helpful to use more than one analogy ( Gentner & Gentner, 1983 ).

The Garden Border Problem. From Greeno & van de Sande (2007) .

The Gentners identified two analogies (flowing waters and teeming crowds) for helping students understand electric circuits. They predicted that students who used the flowing waters analogy (pressure of water, flow in a pipe) should do well on questions about voltage and current because serial and parallel reservoirs combine in the same manner as serial and parallel batteries. In contrast, students with the moving crowd model should do better on resistors because of the analogy to gates. The results supported their predictions in the first experiment. In the second experiment, the analogy to flowing waters was not as helpful as expected because students lacked knowledge in this area.

Spiro, Feltovich, Coulson, and Anderson (1989) discuss practical implications of partial mappings. They propose that simple analogies help beginners gain a preliminary understanding of complex concepts but can later block fuller understanding if learners never progress beyond the simple analogy. One consequence is that instructors need to pay closer attention to how analogies can fail. The authors discuss eight possible failures of simple analogies including misleading properties, missing properties, a focus on surfaces descriptions, and wrong grain size. Their remedy is to use multiple analogies to convey the complexity of difficult ideas.

Mapping Across Solutions

Most teachers and researchers are delighted if problem solvers find one solution to a problem. However, Alan Schoenfeld is more demanding. After students in his math classes at Berkeley solve the problem, he asks them to find another solution. Then a third. The reason is that any one of these solutions might prove helpful in solving future problems ( Schoenfeld, 1985 ).

Studying mapping across solutions attempts to establish how one solution to a problem is related to an alternative solution ( Reed, 2012 ). Rittle-Johnson, Star, and Durkin (2009) discovered that asking seventh- and eighth-grade students to compare two solutions for solving the same problem was helpful when they had the appropriate prior knowledge. One of the solutions showed a short-cut method. In the example given here, the first method requires multiplication, subtraction, and division. The second method requires only division and subtraction:

Students who were familiar with one of the two methods typically noticed that one method required fewer steps or was more efficient than the other. Comparing solutions for these students produced flexible knowledge of procedures. In contrast, students who were not familiar with either method benefited more from the sequential presentation of the solutions.

Comparing alternative solutions can be particularly rewarding when the solutions are generated by different people. Greeno and van de Sande’s (2007) analysis of the Garden Border problem in Figure 12.5 illustrates how a shift in a teacher’s perspective helped her understand that a student’s different approach to the problem could provide an alternative solution. The key difference between the two solutions was how the teacher and student used the phrase “an even border of flowers.” The teacher represented the width of this border by the unknown variable (such as w ) and constructed an equation to represent the area of the inner rectangle by multiplying the length of this rectangle by its width:

This equation followed a previous calculation that the area of the inner rectangle is 1,680 square feet. Because the border is even, 2 w can be subtracted from both the length and width of the outer rectangle.

However, this one-to-one mapping from the text to a variable did not occur to a student who represented the border’s width by two variables: w 1 = ( 72 − y ) / 2 with respect to the length of the outer rectangle and w 2 = ( 40 − x ) / 2 with respect to the width of the outer rectangle. Another student understood how this representation could work by using two equations. The teacher then encouraged the class to figure out the values of x and y by generating the two equations. This second solution was not as efficient because it requires two equations to solve for the two unknown variables. However, the teacher not only encouraged the students in their attempts to generate an alternative solution but recognized that the alternative solution provided a learning opportunity for the class to practice solving for two unknown values.

In summary, one-to-one, one-to-many, and partial mappings across knowledge states provides a basis for analyzing both analogical transfer across problems and the relations between different solutions to the same problem.

Cognitive Architectures

Computational models have contributed to our theoretical understanding of topics such as exploring a problem space ( Simon & Reed, 1976 ) and using analogous solutions ( Hummel & Holyoak, 1997 ). Embedding computational models within cognitive architectures increases their generality by modeling a greater range of activities. EUREKA ( Jones & Langley, 2005 ), CLARION ( Heile & Son, 2010 ; Sun & Zhang, 2006 ), and ACT ( Anderson, Byrne, Douglass, Lebeire, & Qin, 2004 ) illustrate how cognitive architectures have been used to model problem solving.

VanLehn (1989) identified 10 robust findings in his problem-solving chapter that provided a test bed for the design and evaluation of a problem-solving architecture called EUREKA ( Jones & Langley, 2005 ). EUREKA attempts to solve all problems by using analogical reasoning. By incorporating human memory constraints, EUREKA strives to qualitatively replicate VanLehn’s (1989) reported findings. To find solutions to problems, analogies are created to map the operators used in a previous problem to the new problem. The degree of mapping can differ, depending on the degree of the match of the two situations and the level of activation of relevant retrieval patterns.

EUREKA uses means-ends analysis ( Newell & Simon, 1972 ) to divide problem solution into two tasks. The transform task transforms the current state into a desired state. The apply task satisfies preconditions of operators. If the current state satisfies the operators’ preconditions, then the operators are applied to generate a desired state. Otherwise, another transform task is necessary to change the current state into a new state that satisfies the preconditions.

The transformations and applications are stored in EUREKA’s long-term memory in the form of a semantic network of concepts and relations. To make helpful retrievals, Jones and Langley (2005) use a spreading activation framework similar to Anderson’s (1983) early ACT models. EUREKA activates links of concepts in proportions to the trace strengths attached to the links. By increasing or decreasing the trace strengths, the retrieval patterns are strengthened or weakened, respectively.

Table 12.2 lists the 10 psychological findings identified in VanLehn’s (1989) literature review. The first three describe practice effects. Item 1 refers to how people automate the problem-solving process with practice. The rate of learning is fastest at the beginning but slows with more practice, as described in items 2–3. To evaluate EUREKA’s practice effects, Jones and Langley (2005) presented the system with Towers of Hanoi and Blocks World problems. Similar to humans, graphs of EUREKA’s performance showed a rapid decrease on several measures (number of attempts, total search effort, and productive search effort) after the first and second trials, and it remained fairly constant for the remaining trials. The data indicate that EUREKA had difficulty solving the problems on the first trial but quickly improved after the first trial. Once productive trace links are strengthened, the problem-solving process becomes more automatic.

Item 4 describes differences in improvement across intradomain problems that vary in complexity. Assuming that difficult problems are a composite of simple problems, transfer can occur from simple problems to difficult problems, but not vice versa. To test this prediction, EUREKA again was given Towers of Hanoi and Blocks World problems that became increasingly difficult to solve. In the control condition, each trial was run separately. In the test condition, trials were run continuously, allowing EUREKA to store information from previous trials. EUREKA struggled to solve the problems as they became more difficult in the control condition. However, for the test condition, the system was able to solve even the most difficult problems by using analogy to previous solutions.

Based on VanLehn (1989) .

Negative transfer, in which previous learning makes new learning more difficult, rarely occurs, as stated in item 5 of Table 12.2 . An exception, however, is the set effect or Einstellung (item 6) demonstrated in Luchins’s (1942) water jug task that requires obtaining a specified amount of water by filling and emptying jugs of varying sizes. Luchins found that when people solved practice problems with complex solutions, they failed to discover simpler solutions for the test problems. Similar to the human subjects, EUREKA failed to find simpler solutions after the system solved more complex problems. The results can be explained by the fact that EUREKA continues to use operators that have been successful.

Analogy can also be used to solve isomorphic problems from different domains. To evaluate how EUREKA represents interdomain transfer, Jones and Langley (2005) gave it Holyoak and Koh’s (1987) radiation and broken-light problems. The test group was presented with Duncker’s (1945) radiation problem in which a patient with a tumor must be saved by using X-rays. The transfer problem consisted of a broken light bulb that had to be repaired by using laser beams. The test group was able to solve the transfer problem more successfully than a control group that did not receive the light bulb problem.

In EUREKA’s simulation, the light bulb problem was given first, and the radiation problem was used as the analogous problem. The results showed that EUREKA successfully solved the radiation problem 50% of the time in the control condition and 80% of the time in the test condition. This improvement suggests the use of analogical reasoning to transfer solutions, similar to intradomain transfer.

Intradomain and interdomain transfer differ in that concepts and relations in the current problem and the analogous problem are semantically further apart for interdomain transfer. Therefore, for interdomain transfer, retrieval may be more difficult because activation of abstract nodes may be necessary to find solutions. This assumption explains why semantically similar isomorphic problems, such as the tumor and light bulb problems, are easier to solve than semantically dissimilar isomorphic problems (items 7 and 8). The more semantically similar the problems are, the more likely the “correct” activation will occur.

Items 9 and 10 in Table 12.2 state that spontaneous retrieval of solutions is rare and usually only occurs when people use analogies based on surface similarities. Although spontaneous noticing of analogies is uncommon, it can occur with hints. To simulate the effect of hints, the semantic network nodes describing the broken bulb were activated before the system attempted to solve the radiation problem. Compared to the previous trials in which the hint was not given, the activation of relevant nodes greatly reduced the number of attempts and search efforts. The strengthened activation of relevant nodes helped improve the system’s problem-solving performance, as stated in items 9 and 10.

CLARION is an integrative cognitive architecture that consists of a top-level explicit representation and a bottom-level implicit representation ( Sun & Zhang, 2006 ). Explicit knowledge is represented by easily interpretable symbols that have clear conceptual meaning. Implicit knowledge is represented by a subsymbolic distributed representation within a back-propagation network. In contrast to an explicit memory that encodes rules as all or none, implicit memory supports a more gradual accumulation of knowledge

Heile and Sun (2010) subsequently developed the explicit-implicit interaction (EII) theory based on CLARION to analyze the four stages of problem solving proposed in Wallas’s (1926) influential book The Art of Thought . Preparation is the initial search for a solution, incubation is a period of inactivity following an impasse, illumination (or insight) is a sudden discovery of a possible solution, and verification is a determination of whether the discovered solution is valid.

The EII theory distinguishes between explicit processing based on well-defined rules and implicit processing based on associations. Most problems elicit both implicit and explicit processing. The integration of conclusions from both types of processing influences an internal confidence level that measures the probability of finding the solution.

The theory postulates that the initial preparation phase is predominately rule-based processing as people respond to verbal instructions, form representation of the problem, and establish goals. In contrast, the second incubation state is predominately implicit processing in which people may not consciously think about the problem. The third stage, insight, occurs when the internal confidence level crosses a threshold that makes the output available for verbal report. The final verification stage, like the initial stage, requires primarily explicit processing to evaluate the potential of the discovered solution.

The importance of implicit processes in solving insight problems is illustrated by the success of solving the following problem from Schooler, Ohlsson, and Brooks (1993) :

A dealer in antique coins got an offer to buy a beautiful bronze coin. The coin had an emperor’s head on one side and the date 544 B.C. stamped on the other. The dealer examined the coin, but instead of buying it, he called the police. Why?

After working on the problem for 2 minutes in Schooler’s experiment, half of the participants verbalized their strategies while the remainder worked on an unrelated task. After returning to the problem, 36% of the former group and 46% of the latter group solved the problem. CLARION simulates these findings by assuming that the explicit process of verbalizing strategies disrupts the implicit process that can result in insight.

The goal of CLARION and EUREKA is to propose computational models that can provide theoretical explanations of research on human problem solving. In contrast, an early objective in the evaluation of ACT was to evaluate its theoretical assumptions by designing cognitive tutors to improve instruction ( Anderson, Boyle, & Reiser, 1985 ). An extensive ongoing project at Carnegie Mellon University has continued to design intelligent tutoring systems for teaching topics such as algebra, high school geometry, genetics, and computer programming ( Koedinger & Corbett, 2006 ).

ACT consists of a set of assumptions about both declarative and procedural knowledge. The assumptions about declarative knowledge emphasize the representation and organization of factual information. The assumptions about procedural knowledge emphasize how people use this knowledge to carry out various tasks. This part of the theory consists of production rules that specify which action should be performed under a particular set of conditions and have the form IF <condition> THEN <action>. The condition typically states a goal, and the action specifies a potential way to achieve the goal. Production rules were formulated by Newell and used in his cognitive architecture SOAR ( Laird, Newell, & Rosenbloom, 1987 ). The goal of the production rules in ACT, however, is to model human cognition.

One of the initial cognitive tutors helped students learn the programming language LISP. The major theoretical assumptions underlying the construction of the LISP tutor include the following ( Anderson, 1990 ):

Production rules . A skill such as programming can be decomposed into a set of production rules.

Skill complexity . Hundreds of production rules are required to learn a complex skill. This assumption is consistent with the domain-specific view of knowledge.

Hierarchical goal organization . All productions are organized by a hierarchical goal structure in which subgoals are helpful in accomplishing goals.

Declarative origins of knowledge . All knowledge begins in some declarative representation, typically acquired from instruction or example. Before people practice solving problems, they are instructed in how to solve problems.

Compilation of procedural knowledge . Solving problems requires more than being told about how to solve problems. Problem solvers have to convert this declarative knowledge into efficient procedures for solving specific problems.

The LISP tutor consisted of 1,200 production rules that model student performances on programming problems. It covered all the basic concepts of LISP during a full-semester, self-paced course at Carnegie Mellon University. Students who worked on problems with the LISP tutor generally received one letter grade higher on exams than did students who had not worked with the tutor.

Both ACT theory ( Anderson, 2007 ) and cognitive tutors have continued to evolve. The most extensive application of the cognitive tutors has been to mathematics classes, and, by 2007, data had been collected from more than 7,000 students in pre-algebra classes ( Ritter, Anderson, Koedinger, & Corbett, 2007 ). The curriculum includes both a textbook and software so students can divide their time between the classroom (typically 3 days a week) and a computer lab (typically 2 days a week).

The primary source of declarative knowledge is worked examples that show problem solutions ( Anderson & Fincham, 1994 ). Although the presentation of worked examples has typically occurred in the classroom rather than in the computer lab, interweaving worked examples with practice problems has been particularly effective ( Pashler et al., 2007 ). This can be achieved by adding worked examples to the cognitive tutor and requiring that students solve a practice problem on the cognitive tutor after studying each worked example ( Reed, Corbett, Hoffman, Wagner, & MacLaren, 2013 ).

Other recent work to improve the cognitive tutor provides support for seeking help. Students can request hints but occasionally either do not take advantage of this feature or exploit it by requesting so many hints that the tutor does most of the problem solving. Ideally, learners should develop strong metacognitive skills in which they become proficient at requesting the appropriate amount of help. Such training is provided by the help tutor , which has been integrated into the geometry cognitive tutor ( Roll, Aleven, McLaren, & Koedinger, 2011 ). Results showed that this additional assistance not only improved help-seeking skills for solving geometry problems but transferred to a different topic a month later ( Roll et al., 2011 ).

Future Directions

The typical research paradigm for studying problem solving requires individuals to find a single solution. We still have much to learn by using this paradigm, but our knowledge of problem solving would be broadened by investigating a greater variety of topics such as the value of multiple solutions to a problem ( Reed, 2012 ; Rittle-Johnson et al., 2009 ; Schoenfeld, 1985 ), including understanding an alternative solution ( Greeno & van de Sande, 2007 ). Alternative solutions to a problem reveal the problem space of possible solutions. Other underinvestigated topics include (1) problems with insufficient information, (2) estimated answers, (3) complex problem solving, and (4) collaborative problem solving.

A useful skill outside the classroom is the ability to identify problems that have missing information required for a solution. Perhaps because students do not expect to be assigned such problems, they require a hint to identify them. The hint helped high math ability students discover the missing information, but students with moderate ability required familiar cover stories ( Rehder, 1999 ). Other problems provide only enough information to constrain correct answers:

Morita has five friends and Takeda has seven friends. They decide to throw a party together and invite all their friends. All friends are present. How many friends are there at the party?

An instructional example was moderately helpful in reducing single answers and increasing the number of two answers or, more appropriately, a range of possible answers ( Kinda, 2012 ).

Some problems provide enough information for an estimate:

An athlete’s best time to run a mile is 4 minutes and 7 seconds. About how long would it take him to run 3 miles? ( Greer, 1993 )

Research on these kinds of problems has focused on the incorrect application of proportional reasoning ( Verschaffel, Greer, & de Corte, 2000 ), but we need more information on how people use proportional reasoning as a first step toward making reasonable estimates. Estimated answers are important because people often base their decisions on estimates rather than on precise calculations. Estimates are also helpful in evaluating whether a calculated answer is correct. Checking a calculation is recommended when the answer appears unreasonable.

We also need more information on how to help people improve their estimates. The animation tutor provides simulations of people’s estimates so they can improve their estimates of the time to fill a tank, paint a fence, or complete a round trip ( Reed, 2005 ). Although both the American Association for the Advancement of Science ( AAAS, 1993 ) and the National Council of Teachers of Mathematics ( NCTM, 2000 ) have stressed the importance of estimated answers, their recommendations have had a limited impact on instruction.

The topic of complex problem solving is slowly becoming integrated with the more mainstream research and theory discussed in this chapter. Complex problem solving emerged approximately 30 years ago in Europe as a new topic of investigation ( Funke, 2010 ). The problems are formulated in computer-simulated microworlds (MicroDYN) that require discovering causal relations between input and output variables. In one application labeled Handball Training, the input variables were three different training procedures, and the output variables were motivation, power of the throw, and exhaustion ( Wustenberg, Greiff, & Funke, 2012 ). Participants attempted to reach specified target goals in the output variables by adjusting the values of the three training procedures. Performance on this task explained variance in grade point average beyond reasoning ability as measured by scores on Raven’s Advanced Progressive Matrices ( Wustenberg et al., 2012 ).

Another topic that is receiving increased attention is collaborative problem solving, in part assisted by the 2006 launch of the International Journal of Computer-supported Collaborative Learning ( Stahl & Hesse, 2006 ). An example article from this journal is the Engelmann and Hesse (2010) study in which three group members, working at separate computers, had to determine which pesticide and fertilizer to use to rescue a spruce forest. Each member of the group was given both relevant and irrelevant information to construct a concept map of shared information. Groups who initially had access to the knowledge of other group members started significantly earlier in discussing the problems and solved the fertilizer problem significantly sooner.

In conclusion, although research on the individual solutions of individual problem solvers will continue to be a major focus, research on multiple solutions, problems with insufficient information, estimated answers, complex problem solving, and collaborative problem solving will expand and enrich our knowledge.

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Overview of the Problem-Solving Mental Process

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

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

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

Frequently Asked Questions

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

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

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

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

The following steps include developing strategies and organizing knowledge.

1. Identifying the Problem

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

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

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

2. Defining the Problem

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

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

3. Forming a Strategy

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

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

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

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

4. Organizing Information

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

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

5. Allocating Resources

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

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

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

6. Monitoring Progress

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

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

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

7. Evaluating the Results

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

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

A Word From Verywell​

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

Get Advice From The Verywell Mind Podcast

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

Follow Now : Apple Podcasts / Spotify / Google Podcasts

You can become a better problem solving by:

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

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

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

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

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

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

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

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Ch 8: Thinking and Language

Thinking and language.

Why is it so difficult to break habits—like reaching for your ringing phone even when you shouldn’t, such as when you’re driving? Why is it hard to pay attention to a conversation when typing out a text message? How does a person who has never seen or touched snow in real life develop an understanding of the concept of snow? How do young children acquire the ability to learn language with no formal instruction? Psychologists who study thinking explore questions like these.

As a part of this discussion, we will consider thinking, and briefly explore the development and use of language. We will also discuss problem solving and creativity. After finishing this chapter, you will have a greater appreciation of the higher-level cognitive processes that contribute to our distinctiveness as a species.

Learning Objectives

• Understand why selective attention is important and how it can be studied.
• Learn about different models of when and how selection can occur.
• Understand how divided attention or multitasking is studied, and implications of multitasking in situations such as distracted driving.

Thinking and Problem-Solving

Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet, you don’t notice most of your brain’s activity as you move throughout your daily routine. This is only one facet of the complex processes involved in cognition. Simply put, cognition is thinking, and it encompasses the processes associated with perception, knowledge, problem solving, judgment, language, and memory. Scientists who study cognition are searching for ways to understand how we integrate, organize, and utilize our conscious cognitive experiences without being aware of all of the unconscious work that our brains are doing (for example, Kahneman, 2011).

• Distinguish between concepts and prototypes
• Explain the difference between natural and artificial concepts
• Describe problem solving strategies, including algorithms and heuristics
• Explain some common roadblocks to effective problem solving

What is Cognition?

Categories and concepts, concepts and prototypes.

The human nervous system is capable of handling endless streams of information. The senses serve as the interface between the mind and the external environment, receiving stimuli and translating it into nerve impulses that are transmitted to the brain. The brain then processes this information and uses the relevant pieces to create thoughts, which can then be expressed through language or stored in memory for future use. To make this process more complex, the brain does not gather information from external environments only. When thoughts are formed, the brain also pulls information from emotions and memories (Figure 9). Emotion and memory are powerful influences on both our thoughts and behaviors.

In order to organize this staggering amount of information, the brain has developed a file cabinet of sorts in the mind. The different files stored in the file cabinet are called concepts. Concepts  are categories or groupings of linguistic information, images, ideas, or memories, such as life experiences. Concepts are, in many ways, big ideas that are generated by observing details, and categorizing and combining these details into cognitive structures. You use concepts to see the relationships among the different elements of your experiences and to keep the information in your mind organized and accessible.

Concepts are informed by our semantic memory (you will learn more about this concept when you study memory) and are present in every aspect of our lives; however, one of the easiest places to notice concepts is inside a classroom, where they are discussed explicitly. When you study United States history, for example, you learn about more than just individual events that have happened in America’s past. You absorb a large quantity of information by listening to and participating in discussions, examining maps, and reading first-hand accounts of people’s lives. Your brain analyzes these details and develops an overall understanding of American history. In the process, your brain gathers details that inform and refine your understanding of related concepts like democracy, power, and freedom.

Concepts can be complex and abstract, like justice, or more concrete, like types of birds. In psychology, for example, Piaget’s stages of development are abstract concepts. Some concepts, like tolerance, are agreed upon by many people because they have been used in various ways over many years. Other concepts, like the characteristics of your ideal friend or your family’s birthday traditions, are personal and individualized. In this way, concepts touch every aspect of our lives, from our many daily routines to the guiding principles behind the way governments function.

Concepts are at the core of intelligent behavior. We expect people to be able to know what to do in new situations and when confronting new objects. If you go into a new classroom and see chairs, a blackboard, a projector, and a screen, you know what these things are and how they will be used. You’ll sit on one of the chairs and expect the instructor to write on the blackboard or project something onto the screen. You do this even if you have never seen any of these particular objects before , because you have concepts of classrooms, chairs, projectors, and so forth, that tell you what they are and what you’re supposed to do with them. Furthermore, if someone tells you a new fact about the projector—for example, that it has a halogen bulb—you are likely to extend this fact to other projectors you encounter. In short, concepts allow you to extend what you have learned about a limited number of objects to a potentially infinite set of entities.

Another technique used by your brain to organize information is the identification of prototypes for the concepts you have developed. A prototype  is the best example or representation of a concept. For example, for the category of civil disobedience, your prototype could be Rosa Parks. Her peaceful resistance to segregation on a city bus in Montgomery, Alabama, is a recognizable example of civil disobedience. Or your prototype could be Mohandas Gandhi, sometimes called Mahatma Gandhi (“Mahatma” is an honorific title) (Figure 10).

Mohandas Gandhi served as a nonviolent force for independence for India while simultaneously demanding that Buddhist, Hindu, Muslim, and Christian leaders—both Indian and British—collaborate peacefully. Although he was not always successful in preventing violence around him, his life provides a steadfast example of the civil disobedience prototype (Constitutional Rights Foundation, 2013). Just as concepts can be abstract or concrete, we can make a distinction between concepts that are functions of our direct experience with the world and those that are more artificial in nature.

Link to Learning

Natural and artificial concepts.

In psychology, concepts can be divided into two categories, natural and artificial. Natural concepts  are created “naturally” through your experiences and can be developed from either direct or indirect experiences. For example, if you live in Essex Junction, Vermont, you have probably had a lot of direct experience with snow. You’ve watched it fall from the sky, you’ve seen lightly falling snow that barely covers the windshield of your car, and you’ve shoveled out 18 inches of fluffy white snow as you’ve thought, “This is perfect for skiing.” You’ve thrown snowballs at your best friend and gone sledding down the steepest hill in town. In short, you know snow. You know what it looks like, smells like, tastes like, and feels like. If, however, you’ve lived your whole life on the island of Saint Vincent in the Caribbean, you may never have actually seen snow, much less tasted, smelled, or touched it. You know snow from the indirect experience of seeing pictures of falling snow—or from watching films that feature snow as part of the setting. Either way, snow is a natural concept because you can construct an understanding of it through direct observations or experiences of snow (Figure 11).

An artificial concept  on the other hand, is a concept that is defined by a specific set of characteristics. Various properties of geometric shapes, like squares and triangles, serve as useful examples of artificial concepts. A triangle always has three angles and three sides. A square always has four equal sides and four right angles. Mathematical formulas, like the equation for area (length × width) are artificial concepts defined by specific sets of characteristics that are always the same. Artificial concepts can enhance the understanding of a topic by building on one another. For example, before learning the concept of “area of a square” (and the formula to find it), you must understand what a square is. Once the concept of “area of a square” is understood, an understanding of area for other geometric shapes can be built upon the original understanding of area. The use of artificial concepts to define an idea is crucial to communicating with others and engaging in complex thought. According to Goldstone and Kersten (2003), concepts act as building blocks and can be connected in countless combinations to create complex thoughts.

A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.

There are several types of schemata. A role schema makes assumptions about how individuals in certain roles will behave (Callero, 1994). For example, imagine you meet someone who introduces himself as a firefighter. When this happens, your brain automatically activates the “firefighter schema” and begins making assumptions that this person is brave, selfless, and community-oriented. Despite not knowing this person, already you have unknowingly made judgments about him. Schemata also help you fill in gaps in the information you receive from the world around you. While schemata allow for more efficient information processing, there can be problems with schemata, regardless of whether they are accurate: Perhaps this particular firefighter is not brave, he just works as a firefighter to pay the bills while studying to become a children’s librarian.

An event schema , also known as a cognitive script , is a set of behaviors that can feel like a routine. Think about what you do when you walk into an elevator (Figure 12). First, the doors open and you wait to let exiting passengers leave the elevator car. Then, you step into the elevator and turn around to face the doors, looking for the correct button to push. You never face the back of the elevator, do you? And when you’re riding in a crowded elevator and you can’t face the front, it feels uncomfortable, doesn’t it? Interestingly, event schemata can vary widely among different cultures and countries. For example, while it is quite common for people to greet one another with a handshake in the United States, in Tibet, you greet someone by sticking your tongue out at them, and in Belize, you bump fists (Cairns Regional Council, n.d.)

Because event schemata are automatic, they can be difficult to change. Imagine that you are driving home from work or school. This event schema involves getting in the car, shutting the door, and buckling your seatbelt before putting the key in the ignition. You might perform this script two or three times each day. As you drive home, you hear your phone’s ring tone. Typically, the event schema that occurs when you hear your phone ringing involves locating the phone and answering it or responding to your latest text message. So without thinking, you reach for your phone, which could be in your pocket, in your bag, or on the passenger seat of the car. This powerful event schema is informed by your pattern of behavior and the pleasurable stimulation that a phone call or text message gives your brain. Because it is a schema, it is extremely challenging for us to stop reaching for the phone, even though we know that we endanger our own lives and the lives of others while we do it (Neyfakh, 2013) (Figure 13).

Remember the elevator? It feels almost impossible to walk in and not face the door. Our powerful event schema dictates our behavior in the elevator, and it is no different with our phones. Current research suggests that it is the habit, or event schema, of checking our phones in many different situations that makes refraining from checking them while driving especially difficult (Bayer & Campbell, 2012). Because texting and driving has become a dangerous epidemic in recent years, psychologists are looking at ways to help people interrupt the “phone schema” while driving. Event schemata like these are the reason why many habits are difficult to break once they have been acquired. As we continue to examine thinking, keep in mind how powerful the forces of concepts and schemata are to our understanding of the world.

Watch this CrashCourse video to see more examples of concepts and prototypes. You’ll also get a preview on other key topics in cognition, including problem-solving strategies like algorithms and heuristics.

You can view the transcript for “Cognition – How Your Mind Can Amaze and Betray You: Crash Course Psychology #15” here (opens in new window) .

Think It Over

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

Problem-Solving Strategies

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

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

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

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

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

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

What problem-solving method could you use to solve Einstein’s famous riddle?

You can view the transcript for “Can you solve “Einstein’s Riddle”? – Dan Van der Vieren” here (opens in new window) .

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

Everyday Connections: Solving Puzzles

Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below (Figure 14) 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.

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

Take a look at the “Puzzling Scales” logic puzzle below (Figure 16). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

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

Pitfalls to Problem Solving

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

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

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

Watch this video from the Big Think to learn more about the confirmation bias.

You can view the transcript for “Confirmation Bias: Your Brain is So Judgmental” here (opens in new window) .

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

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

Watch the following video for an example of the availability heuristic.

You can view the transcript for “Availability Heuristic: Are Planes More Dangerous Than Cars?” here (opens in new window) .

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

Learn more about heuristics and common biases through the article, “ 8 Common Thinking Mistakes Our Brains Make Every Day and How to Prevent Them ” by Belle Beth Cooper.

You can also watch this clever music video explaining these and other cognitive biases.

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?

• Understand how the use of language develops
• Explain the relationship between language and thinking

Language Development

Language is a communication system that involves using words and systematic rules to organize those words to transmit information from one individual to another. While language is a form of communication, not all communication is language. Many species communicate with one another through their postures, movements, odors, or vocalizations. This communication is crucial for species that need to interact and develop social relationships with their conspecifics. However, many people have asserted that it is language that makes humans unique among all of the animal species (Corballis & Suddendorf, 2007; Tomasello & Rakoczy, 2003). This section will focus on what distinguishes language as a special form of communication, how the use of language develops, and how language affects the way we think.

Components of Language

Language , be it spoken, signed, or written, has specific components: a lexicon and grammar. Lexicon refers to the words of a given language. Thus, lexicon is a language’s vocabulary. Grammar  refers to the set of rules that are used to convey meaning through the use of the lexicon (Fernández & Cairns, 2011). For instance, English grammar dictates that most verbs receive an “-ed” at the end to indicate past tense.

Words are formed by combining the various phonemes that make up the language. A phoneme  (e.g., the sounds “ah” vs. “eh”) is a basic sound unit of a given language, and different languages have different sets of phonemes. Phonemes are combined to form morphemes , which are the smallest units of language that convey some type of meaning (e.g., “I” is both a phoneme and a morpheme).  Further, a morpheme is not the same as a word. The main difference is that a morpheme sometimes does not stand alone, but a word, by definition, always stands alone.

We use semantics and syntax to construct language. Semantics and syntax are part of a language’s grammar. Semantics refers to the process by which we derive meaning from morphemes and words. Syntax  refers to the way words are organized into sentences (Chomsky, 1965; Fernández & Cairns, 2011).

We apply the rules of grammar to organize the lexicon in novel and creative ways, which allow us to communicate information about both concrete and abstract concepts. We can talk about our immediate and observable surroundings as well as the surface of unseen planets. We can share our innermost thoughts, our plans for the future, and debate the value of a college education. We can provide detailed instructions for cooking a meal, fixing a car, or building a fire. The flexibility that language provides to relay vastly different types of information is a property that makes language so distinct as a mode of communication among humans.

Given the remarkable complexity of a language, one might expect that mastering a language would be an especially arduous task; indeed, for those of us trying to learn a second language as adults, this might seem to be true. However, young children master language very quickly with relative ease. B. F. Skinner (1957) proposed that language is learned through reinforcement. Noam Chomsky (1965) criticized this behaviorist approach, asserting instead that the mechanisms underlying language acquisition are biologically determined. The use of language develops in the absence of formal instruction and appears to follow a very similar pattern in children from vastly different cultures and backgrounds. It would seem, therefore, that we are born with a biological predisposition to acquire a language (Chomsky, 1965; Fernández & Cairns, 2011). Moreover, it appears that there is a critical period for language acquisition, such that this proficiency at acquiring language is maximal early in life; generally, as people age, the ease with which they acquire and master new languages diminishes (Johnson & Newport, 1989; Lenneberg, 1967; Singleton, 1995).

Children begin to learn about language from a very early age (Table 1). In fact, it appears that this is occurring even before we are born. Newborns show preference for their mother’s voice and appear to be able to discriminate between the language spoken by their mother and other languages. Babies are also attuned to the languages being used around them and show preferences for videos of faces that are moving in synchrony with the audio of spoken language versus videos that do not synchronize with the audio (Blossom & Morgan, 2006; Pickens, 1994; Spelke & Cortelyou, 1981).

Dig Deeper: The Case of Genie

In the fall of 1970, a social worker in the Los Angeles area found a 13-year-old girl who was being raised in extremely neglectful and abusive conditions. The girl, who came to be known as Genie, had lived most of her life tied to a potty chair or confined to a crib in a small room that was kept closed with the curtains drawn. For a little over a decade, Genie had virtually no social interaction and no access to the outside world. As a result of these conditions, Genie was unable to stand up, chew solid food, or speak (Fromkin, Krashen, Curtiss, Rigler, & Rigler, 1974; Rymer, 1993). The police took Genie into protective custody.

Genie’s abilities improved dramatically following her removal from her abusive environment, and early on, it appeared she was acquiring language—much later than would be predicted by critical period hypotheses that had been posited at the time (Fromkin et al., 1974). Genie managed to amass an impressive vocabulary in a relatively short amount of time. However, she never developed a mastery of the grammatical aspects of language (Curtiss, 1981). Perhaps being deprived of the opportunity to learn language during a critical period impeded Genie’s ability to fully acquire and use language.

You may recall that each language has its own set of phonemes that are used to generate morphemes, words, and so on. Babies can discriminate among the sounds that make up a language (for example, they can tell the difference between the “s” in vision and the “ss” in fission); early on, they can differentiate between the sounds of all human languages, even those that do not occur in the languages that are used in their environments. However, by the time that they are about 1 year old, they can only discriminate among those phonemes that are used in the language or languages in their environments (Jensen, 2011; Werker & Lalonde, 1988; Werker & Tees, 1984).

After the first few months of life, babies enter what is known as the babbling stage, during which time they tend to produce single syllables that are repeated over and over. As time passes, more variations appear in the syllables that they produce. During this time, it is unlikely that the babies are trying to communicate; they are just as likely to babble when they are alone as when they are with their caregivers (Fernández & Cairns, 2011). Interestingly, babies who are raised in environments in which sign language is used will also begin to show babbling in the gestures of their hands during this stage (Petitto, Holowka, Sergio, Levy, & Ostry, 2004).

Generally, a child’s first word is uttered sometime between the ages of 1 year to 18 months, and for the next few months, the child will remain in the “one word” stage of language development. During this time, children know a number of words, but they only produce one-word utterances. The child’s early vocabulary is limited to familiar objects or events, often nouns. Although children in this stage only make one-word utterances, these words often carry larger meaning (Fernández & Cairns, 2011). So, for example, a child saying “cookie” could be identifying a cookie or asking for a cookie.

As a child’s lexicon grows, she begins to utter simple sentences and to acquire new vocabulary at a very rapid pace. In addition, children begin to demonstrate a clear understanding of the specific rules that apply to their language(s). Even the mistakes that children sometimes make provide evidence of just how much they understand about those rules. This is sometimes seen in the form of overgeneralization . In this context, overgeneralization refers to an extension of a language rule to an exception to the rule. For example, in English, it is usually the case that an “s” is added to the end of a word to indicate plurality. For example, we speak of one dog versus two dogs. Young children will overgeneralize this rule to cases that are exceptions to the “add an s to the end of the word” rule and say things like “those two gooses” or “three mouses.” Clearly, the rules of the language are understood, even if the exceptions to the rules are still being learned (Moskowitz, 1978).

Language and Thinking

Think about it:  the meaning of language.

Think about what you know of other languages; perhaps you even speak multiple languages. Imagine for a moment that your closest friend fluently speaks more than one language. Do you think that friend thinks differently, depending on which language is being spoken? You may know a few words that are not translatable from their original language into English. For example, the Portuguese word saudade originated during the 15th century, when Portuguese sailors left home to explore the seas and travel to Africa or Asia. Those left behind described the emptiness and fondness they felt as saudade (Figure 20) . The word came to express many meanings, including loss, nostalgia, yearning, warm memories, and hope. There is no single word in English that includes all of those emotions in a single description. Do words such as saudade indicate that different languages produce different patterns of thought in people? What do you think??

Language may indeed influence the way that we think, an idea known as linguistic determinism. One recent demonstration of this phenomenon involved differences in the way that English and Mandarin Chinese speakers talk and think about time. English speakers tend to talk about time using terms that describe changes along a horizontal dimension, for example, saying something like “I’m running behind schedule” or “Don’t get ahead of yourself.” While Mandarin Chinese speakers also describe time in horizontal terms, it is not uncommon to also use terms associated with a vertical arrangement. For example, the past might be described as being “up” and the future as being “down.” It turns out that these differences in language translate into differences in performance on cognitive tests designed to measure how quickly an individual can recognize temporal relationships. Specifically, when given a series of tasks with vertical priming, Mandarin Chinese speakers were faster at recognizing temporal relationships between months. Indeed, Boroditsky (2001) sees these results as suggesting that “habits in language encourage habits in thought” (p. 12).

Language does not completely determine our thoughts—our thoughts are far too flexible for that—but habitual uses of language can influence our habit of thought and action. For instance, some linguistic practice seems to be associated even with cultural values and social institution. Pronoun drop is the case in point. Pronouns such as “I” and “you” are used to represent the speaker and listener of a speech in English. In an English sentence, these pronouns cannot be dropped if they are used as the subject of a sentence. So, for instance, “I went to the movie last night” is fine, but “Went to the movie last night” is not in standard English. However, in other languages such as Japanese, pronouns can be, and in fact often are, dropped from sentences. It turned out that people living in those countries where pronoun drop languages are spoken tend to have more collectivistic values (e.g., employees having greater loyalty toward their employers) than those who use non–pronoun drop languages such as English (Kashima & Kashima, 1998). It was argued that the explicit reference to “you” and “I” may remind speakers the distinction between the self and other, and the differentiation between individuals. Such a linguistic practice may act as a constant reminder of the cultural value, which, in turn, may encourage people to perform the linguistic practice.

One group of researchers who wanted to investigate how language influences thought compared how English speakers and the Dani people of Papua New Guinea think and speak about color. The Dani have two words for color: one word for light and one word for dark . In contrast, the English language has 11 color words. Researchers hypothesized that the number of color terms could limit the ways that the Dani people conceptualized color. However, the Dani were able to distinguish colors with the same ability as English speakers, despite having fewer words at their disposal (Berlin & Kay, 1969). A recent review of research aimed at determining how language might affect something like color perception suggests that language can influence perceptual phenomena, especially in the left hemisphere of the brain. You may recall from earlier chapters that the left hemisphere is associated with language for most people. However, the right (less linguistic hemisphere) of the brain is less affected by linguistic influences on perception (Regier & Kay, 2009)

Learn more about language, language acquisition, and especially the connection between language and thought in the following CrashCourse video:

You can view the transcript for “Language: Crash Course Psychology #16” here (opens in new window) .

In this chapter, you learned to

• describe attention
• describe cognition and problem-solving strategies
• describe language acquisition and the role language plays in communication and thought

You learned about non-memory cognitive processes in this chapter. Because each of you reading this is using language in some shape or form, we will end with a quick summary and a video on this topic. Language is a communication system that has both a lexicon and a system of grammar. Language acquisition occurs naturally and effortlessly during the early stages of life, and this acquisition occurs in a predictable sequence for individuals around the world. Language has a strong influence on thought, and the concept of how language may influence cognition remains an area of study and debate in psychology.

In this TED talk, Lera Boroditsky summarizes unique ways that language and culture intersect with some basic cognitive processes. How was your language shaped your thinking?

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thinking, including perception, learning, problem solving, judgment, and memory

field of psychology dedicated to studying every aspect of how people think

a set of objects that can be treated as equivalent in some way

category or grouping of linguistic information, objects, ideas, or life experiences

best representation of a concept

mental groupings that are created “naturally” through your experiences

concept that is defined by a very specific set of characteristics

(plural = schemata) mental construct consisting of a cluster or collection of related concepts

set of expectations that define the behaviors of a person occupying a particular role

set of behaviors that are performed the same way each time; also referred to as a cognitive script

set of behaviors that are performed the same way each time; also referred to as an event schema

method for solving problems

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

problem-solving strategy characterized by a specific set of instructions

mental shortcut that saves time when solving a problem

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

continually using an old solution to a problem without results

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

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

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

subset of the population that accurately represents the general population

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

communication system that involves using words to transmit information from one individual to another

Words and expressions.

set of rules that are used to convey meaning through the use of a lexicon

basic sound unit of a given language

smallest unit of language that conveys some type of meaning

process by which we derive meaning from morphemes and words

manner by which words are organized into sentences

extension of a rule that exists in a given language to an exception to the rule

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Do You Understand the Problem You’re Trying to Solve?

To solve tough problems at work, first ask these questions.

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Problem solving skills are invaluable in any job. But all too often, we jump to find solutions to a problem without taking time to really understand the dilemma we face, according to Thomas Wedell-Wedellsborg , an expert in innovation and the author of the book, What’s Your Problem?: To Solve Your Toughest Problems, Change the Problems You Solve .

In this episode, you’ll learn how to reframe tough problems by asking questions that reveal all the factors and assumptions that contribute to the situation. You’ll also learn why searching for just one root cause can be misleading.

Key episode topics include: leadership, decision making and problem solving, power and influence, business management.

HBR On Leadership curates the best case studies and conversations with the world’s top business and management experts, to help you unlock the best in those around you. New episodes every week.

• Listen to the original HBR IdeaCast episode: The Secret to Better Problem Solving (2016)
• Find more episodes of HBR IdeaCast
• Discover 100 years of Harvard Business Review articles, case studies, podcasts, and more at HBR.org .

HANNAH BATES: Welcome to HBR on Leadership , case studies and conversations with the world’s top business and management experts, hand-selected to help you unlock the best in those around you.

Problem solving skills are invaluable in any job. But even the most experienced among us can fall into the trap of solving the wrong problem.

Thomas Wedell-Wedellsborg says that all too often, we jump to find solutions to a problem – without taking time to really understand what we’re facing.

He’s an expert in innovation, and he’s the author of the book, What’s Your Problem?: To Solve Your Toughest Problems, Change the Problems You Solve .

  In this episode, you’ll learn how to reframe tough problems, by asking questions that reveal all the factors and assumptions that contribute to the situation. You’ll also learn why searching for one root cause can be misleading. And you’ll learn how to use experimentation and rapid prototyping as problem-solving tools.

This episode originally aired on HBR IdeaCast in December 2016. Here it is.

SARAH GREEN CARMICHAEL: Welcome to the HBR IdeaCast from Harvard Business Review. I’m Sarah Green Carmichael.

Problem solving is popular. People put it on their resumes. Managers believe they excel at it. Companies count it as a key proficiency. We solve customers’ problems.

The problem is we often solve the wrong problems. Albert Einstein and Peter Drucker alike have discussed the difficulty of effective diagnosis. There are great frameworks for getting teams to attack true problems, but they’re often hard to do daily and on the fly. That’s where our guest comes in.

Thomas Wedell-Wedellsborg is a consultant who helps companies and managers reframe their problems so they can come up with an effective solution faster. He asks the question “Are You Solving The Right Problems?” in the January-February 2017 issue of Harvard Business Review. Thomas, thank you so much for coming on the HBR IdeaCast .

THOMAS WEDELL-WEDELLSBORG: Thanks for inviting me.

SARAH GREEN CARMICHAEL: So, I thought maybe we could start by talking about the problem of talking about problem reframing. What is that exactly?

THOMAS WEDELL-WEDELLSBORG: Basically, when people face a problem, they tend to jump into solution mode to rapidly, and very often that means that they don’t really understand, necessarily, the problem they’re trying to solve. And so, reframing is really a– at heart, it’s a method that helps you avoid that by taking a second to go in and ask two questions, basically saying, first of all, wait. What is the problem we’re trying to solve? And then crucially asking, is there a different way to think about what the problem actually is?

SARAH GREEN CARMICHAEL: So, I feel like so often when this comes up in meetings, you know, someone says that, and maybe they throw out the Einstein quote about you spend an hour of problem solving, you spend 55 minutes to find the problem. And then everyone else in the room kind of gets irritated. So, maybe just give us an example of maybe how this would work in practice in a way that would not, sort of, set people’s teeth on edge, like oh, here Sarah goes again, reframing the whole problem instead of just solving it.

THOMAS WEDELL-WEDELLSBORG: I mean, you’re bringing up something that’s, I think is crucial, which is to create legitimacy for the method. So, one of the reasons why I put out the article is to give people a tool to say actually, this thing is still important, and we need to do it. But I think the really critical thing in order to make this work in a meeting is actually to learn how to do it fast, because if you have the idea that you need to spend 30 minutes in a meeting delving deeply into the problem, I mean, that’s going to be uphill for most problems. So, the critical thing here is really to try to make it a practice you can implement very, very rapidly.

There’s an example that I would suggest memorizing. This is the example that I use to explain very rapidly what it is. And it’s basically, I call it the slow elevator problem. You imagine that you are the owner of an office building, and that your tenants are complaining that the elevator’s slow.

Now, if you take that problem framing for granted, you’re going to start thinking creatively around how do we make the elevator faster. Do we install a new motor? Do we have to buy a new lift somewhere?

The thing is, though, if you ask people who actually work with facilities management, well, they’re going to have a different solution for you, which is put up a mirror next to the elevator. That’s what happens is, of course, that people go oh, I’m busy. I’m busy. I’m– oh, a mirror. Oh, that’s beautiful.

And then they forget time. What’s interesting about that example is that the idea with a mirror is actually a solution to a different problem than the one you first proposed. And so, the whole idea here is once you get good at using reframing, you can quickly identify other aspects of the problem that might be much better to try to solve than the original one you found. It’s not necessarily that the first one is wrong. It’s just that there might be better problems out there to attack that we can, means we can do things much faster, cheaper, or better.

SARAH GREEN CARMICHAEL: So, in that example, I can understand how A, it’s probably expensive to make the elevator faster, so it’s much cheaper just to put up a mirror. And B, maybe the real problem people are actually feeling, even though they’re not articulating it right, is like, I hate waiting for the elevator. But if you let them sort of fix their hair or check their teeth, they’re suddenly distracted and don’t notice.

But if you have, this is sort of a pedestrian example, but say you have a roommate or a spouse who doesn’t clean up the kitchen. Facing that problem and not having your elegant solution already there to highlight the contrast between the perceived problem and the real problem, how would you take a problem like that and attack it using this method so that you can see what some of the other options might be?

THOMAS WEDELL-WEDELLSBORG: Right. So, I mean, let’s say it’s you who have that problem. I would go in and say, first of all, what would you say the problem is? Like, if you were to describe your view of the problem, what would that be?

SARAH GREEN CARMICHAEL: I hate cleaning the kitchen, and I want someone else to clean it up.

THOMAS WEDELL-WEDELLSBORG: OK. So, my first observation, you know, that somebody else might not necessarily be your spouse. So, already there, there’s an inbuilt assumption in your question around oh, it has to be my husband who does the cleaning. So, it might actually be worth, already there to say, is that really the only problem you have? That you hate cleaning the kitchen, and you want to avoid it? Or might there be something around, as well, getting a better relationship in terms of how you solve problems in general or establishing a better way to handle small problems when dealing with your spouse?

SARAH GREEN CARMICHAEL: Or maybe, now that I’m thinking that, maybe the problem is that you just can’t find the stuff in the kitchen when you need to find it.

THOMAS WEDELL-WEDELLSBORG: Right, and so that’s an example of a reframing, that actually why is it a problem that the kitchen is not clean? Is it only because you hate the act of cleaning, or does it actually mean that it just takes you a lot longer and gets a lot messier to actually use the kitchen, which is a different problem. The way you describe this problem now, is there anything that’s missing from that description?

SARAH GREEN CARMICHAEL: That is a really good question.

THOMAS WEDELL-WEDELLSBORG: Other, basically asking other factors that we are not talking about right now, and I say those because people tend to, when given a problem, they tend to delve deeper into the detail. What often is missing is actually an element outside of the initial description of the problem that might be really relevant to what’s going on. Like, why does the kitchen get messy in the first place? Is it something about the way you use it or your cooking habits? Is it because the neighbor’s kids, kind of, use it all the time?

There might, very often, there might be issues that you’re not really thinking about when you first describe the problem that actually has a big effect on it.

SARAH GREEN CARMICHAEL: I think at this point it would be helpful to maybe get another business example, and I’m wondering if you could tell us the story of the dog adoption problem.

THOMAS WEDELL-WEDELLSBORG: Yeah. This is a big problem in the US. If you work in the shelter industry, basically because dogs are so popular, more than 3 million dogs every year enter a shelter, and currently only about half of those actually find a new home and get adopted. And so, this is a problem that has persisted. It’s been, like, a structural problem for decades in this space. In the last three years, where people found new ways to address it.

So a woman called Lori Weise who runs a rescue organization in South LA, and she actually went in and challenged the very idea of what we were trying to do. She said, no, no. The problem we’re trying to solve is not about how to get more people to adopt dogs. It is about keeping the dogs with their first family so they never enter the shelter system in the first place.

In 2013, she started what’s called a Shelter Intervention Program that basically works like this. If a family comes and wants to hand over their dog, these are called owner surrenders. It’s about 30% of all dogs that come into a shelter. All they would do is go up and ask, if you could, would you like to keep your animal? And if they said yes, they would try to fix whatever helped them fix the problem, but that made them turn over this.

And sometimes that might be that they moved into a new building. The landlord required a deposit, and they simply didn’t have the money to put down a deposit. Or the dog might need a $10 rabies shot, but they didn’t know how to get access to a vet.

And so, by instigating that program, just in the first year, she took her, basically the amount of dollars they spent per animal they helped went from something like $85 down to around $60. Just an immediate impact, and her program now is being rolled out, is being supported by the ASPCA, which is one of the big animal welfare stations, and it’s being rolled out to various other places.

And I think what really struck me with that example was this was not dependent on having the internet. This was not, oh, we needed to have everybody mobile before we could come up with this. This, conceivably, we could have done 20 years ago. Only, it only happened when somebody, like in this case Lori, went in and actually rethought what the problem they were trying to solve was in the first place.

SARAH GREEN CARMICHAEL: So, what I also think is so interesting about that example is that when you talk about it, it doesn’t sound like the kind of thing that would have been thought of through other kinds of problem solving methods. There wasn’t necessarily an After Action Review or a 5 Whys exercise or a Six Sigma type intervention. I don’t want to throw those other methods under the bus, but how can you get such powerful results with such a very simple way of thinking about something?

THOMAS WEDELL-WEDELLSBORG: That was something that struck me as well. This, in a way, reframing and the idea of the problem diagnosis is important is something we’ve known for a long, long time. And we’ve actually have built some tools to help out. If you worked with us professionally, you are familiar with, like, Six Sigma, TRIZ, and so on. You mentioned 5 Whys. A root cause analysis is another one that a lot of people are familiar with.

Those are our good tools, and they’re definitely better than nothing. But what I notice when I work with the companies applying those was those tools tend to make you dig deeper into the first understanding of the problem we have. If it’s the elevator example, people start asking, well, is that the cable strength, or is the capacity of the elevator? That they kind of get caught by the details.

That, in a way, is a bad way to work on problems because it really assumes that there’s like a, you can almost hear it, a root cause. That you have to dig down and find the one true problem, and everything else was just symptoms. That’s a bad way to think about problems because problems tend to be multicausal.

There tend to be lots of causes or levers you can potentially press to address a problem. And if you think there’s only one, if that’s the right problem, that’s actually a dangerous way. And so I think that’s why, that this is a method I’ve worked with over the last five years, trying to basically refine how to make people better at this, and the key tends to be this thing about shifting out and saying, is there a totally different way of thinking about the problem versus getting too caught up in the mechanistic details of what happens.

SARAH GREEN CARMICHAEL: What about experimentation? Because that’s another method that’s become really popular with the rise of Lean Startup and lots of other innovation methodologies. Why wouldn’t it have worked to, say, experiment with many different types of fixing the dog adoption problem, and then just pick the one that works the best?

THOMAS WEDELL-WEDELLSBORG: You could say in the dog space, that’s what’s been going on. I mean, there is, in this industry and a lot of, it’s largely volunteer driven. People have experimented, and they found different ways of trying to cope. And that has definitely made the problem better. So, I wouldn’t say that experimentation is bad, quite the contrary. Rapid prototyping, quickly putting something out into the world and learning from it, that’s a fantastic way to learn more and to move forward.

My point is, though, that I feel we’ve come to rely too much on that. There’s like, if you look at the start up space, the wisdom is now just to put something quickly into the market, and then if it doesn’t work, pivot and just do more stuff. What reframing really is, I think of it as the cognitive counterpoint to prototyping. So, this is really a way of seeing very quickly, like not just working on the solution, but also working on our understanding of the problem and trying to see is there a different way to think about that.

If you only stick with experimentation, again, you tend to sometimes stay too much in the same space trying minute variations of something instead of taking a step back and saying, wait a minute. What is this telling us about what the real issue is?

SARAH GREEN CARMICHAEL: So, to go back to something that we touched on earlier, when we were talking about the completely hypothetical example of a spouse who does not clean the kitchen–

THOMAS WEDELL-WEDELLSBORG: Completely, completely hypothetical.

SARAH GREEN CARMICHAEL: Yes. For the record, my husband is a great kitchen cleaner.

You started asking me some questions that I could see immediately were helping me rethink that problem. Is that kind of the key, just having a checklist of questions to ask yourself? How do you really start to put this into practice?

THOMAS WEDELL-WEDELLSBORG: I think there are two steps in that. The first one is just to make yourself better at the method. Yes, you should kind of work with a checklist. In the article, I kind of outlined seven practices that you can use to do this.

But importantly, I would say you have to consider that as, basically, a set of training wheels. I think there’s a big, big danger in getting caught in a checklist. This is something I work with.

My co-author Paddy Miller, it’s one of his insights. That if you start giving people a checklist for things like this, they start following it. And that’s actually a problem, because what you really want them to do is start challenging their thinking.

So the way to handle this is to get some practice using it. Do use the checklist initially, but then try to step away from it and try to see if you can organically make– it’s almost a habit of mind. When you run into a colleague in the hallway and she has a problem and you have five minutes, like, delving in and just starting asking some of those questions and using your intuition to say, wait, how is she talking about this problem? And is there a question or two I can ask her about the problem that can help her rethink it?

SARAH GREEN CARMICHAEL: Well, that is also just a very different approach, because I think in that situation, most of us can’t go 30 seconds without jumping in and offering solutions.

THOMAS WEDELL-WEDELLSBORG: Very true. The drive toward solutions is very strong. And to be clear, I mean, there’s nothing wrong with that if the solutions work. So, many problems are just solved by oh, you know, oh, here’s the way to do that. Great.

But this is really a powerful method for those problems where either it’s something we’ve been banging our heads against tons of times without making progress, or when you need to come up with a really creative solution. When you’re facing a competitor with a much bigger budget, and you know, if you solve the same problem later, you’re not going to win. So, that basic idea of taking that approach to problems can often help you move forward in a different way than just like, oh, I have a solution.

I would say there’s also, there’s some interesting psychological stuff going on, right? Where you may have tried this, but if somebody tries to serve up a solution to a problem I have, I’m often resistant towards them. Kind if like, no, no, no, no, no, no. That solution is not going to work in my world. Whereas if you get them to discuss and analyze what the problem really is, you might actually dig something up.

Let’s go back to the kitchen example. One powerful question is just to say, what’s your own part in creating this problem? It’s very often, like, people, they describe problems as if it’s something that’s inflicted upon them from the external world, and they are innocent bystanders in that.

SARAH GREEN CARMICHAEL: Right, or crazy customers with unreasonable demands.

THOMAS WEDELL-WEDELLSBORG: Exactly, right. I don’t think I’ve ever met an agency or consultancy that didn’t, like, gossip about their customers. Oh, my god, they’re horrible. That, you know, classic thing, why don’t they want to take more risk? Well, risk is bad.

It’s their business that’s on the line, not the consultancy’s, right? So, absolutely, that’s one of the things when you step into a different mindset and kind of, wait. Oh yeah, maybe I actually am part of creating this problem in a sense, as well. That tends to open some new doors for you to move forward, in a way, with stuff that you may have been struggling with for years.

SARAH GREEN CARMICHAEL: So, we’ve surfaced a couple of questions that are useful. I’m curious to know, what are some of the other questions that you find yourself asking in these situations, given that you have made this sort of mental habit that you do? What are the questions that people seem to find really useful?

THOMAS WEDELL-WEDELLSBORG: One easy one is just to ask if there are any positive exceptions to the problem. So, was there day where your kitchen was actually spotlessly clean? And then asking, what was different about that day? Like, what happened there that didn’t happen the other days? That can very often point people towards a factor that they hadn’t considered previously.

SARAH GREEN CARMICHAEL: We got take-out.

THOMAS WEDELL-WEDELLSBORG: S,o that is your solution. Take-out from [INAUDIBLE]. That might have other problems.

Another good question, and this is a little bit more high level. It’s actually more making an observation about labeling how that person thinks about the problem. And what I mean with that is, we have problem categories in our head. So, if I say, let’s say that you describe a problem to me and say, well, we have a really great product and are, it’s much better than our previous product, but people aren’t buying it. I think we need to put more marketing dollars into this.

Now you can go in and say, that’s interesting. This sounds like you’re thinking of this as a communications problem. Is there a different way of thinking about that? Because you can almost tell how, when the second you say communications, there are some ideas about how do you solve a communications problem. Typically with more communication.

And what you might do is go in and suggest, well, have you considered that it might be, say, an incentive problem? Are there incentives on behalf of the purchasing manager at your clients that are obstructing you? Might there be incentive issues with your own sales force that makes them want to sell the old product instead of the new one?

So literally, just identifying what type of problem does this person think about, and is there different potential way of thinking about it? Might it be an emotional problem, a timing problem, an expectations management problem? Thinking about what label of what type of problem that person is kind of thinking as it of.

SARAH GREEN CARMICHAEL: That’s really interesting, too, because I think so many of us get requests for advice that we’re really not qualified to give. So, maybe the next time that happens, instead of muddying my way through, I will just ask some of those questions that we talked about instead.

THOMAS WEDELL-WEDELLSBORG: That sounds like a good idea.

SARAH GREEN CARMICHAEL: So, Thomas, this has really helped me reframe the way I think about a couple of problems in my own life, and I’m just wondering. I know you do this professionally, but is there a problem in your life that thinking this way has helped you solve?

THOMAS WEDELL-WEDELLSBORG: I’ve, of course, I’ve been swallowing my own medicine on this, too, and I think I have, well, maybe two different examples, and in one case somebody else did the reframing for me. But in one case, when I was younger, I often kind of struggled a little bit. I mean, this is my teenage years, kind of hanging out with my parents. I thought they were pretty annoying people. That’s not really fair, because they’re quite wonderful, but that’s what life is when you’re a teenager.

And one of the things that struck me, suddenly, and this was kind of the positive exception was, there was actually an evening where we really had a good time, and there wasn’t a conflict. And the core thing was, I wasn’t just seeing them in their old house where I grew up. It was, actually, we were at a restaurant. And it suddenly struck me that so much of the sometimes, kind of, a little bit, you love them but they’re annoying kind of dynamic, is tied to the place, is tied to the setting you are in.

And of course, if– you know, I live abroad now, if I visit my parents and I stay in my old bedroom, you know, my mother comes in and wants to wake me up in the morning. Stuff like that, right? And it just struck me so, so clearly that it’s– when I change this setting, if I go out and have dinner with them at a different place, that the dynamic, just that dynamic disappears.

SARAH GREEN CARMICHAEL: Well, Thomas, this has been really, really helpful. Thank you for talking with me today.

THOMAS WEDELL-WEDELLSBORG: Thank you, Sarah.  

HANNAH BATES: That was Thomas Wedell-Wedellsborg in conversation with Sarah Green Carmichael on the HBR IdeaCast. He’s an expert in problem solving and innovation, and he’s the author of the book, What’s Your Problem?: To Solve Your Toughest Problems, Change the Problems You Solve .

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

We’re a production of Harvard Business Review. If you want more podcasts, articles, case studies, books, and videos like this, find it all at HBR dot org.

This episode was produced by Anne Saini, and me, Hannah Bates. Ian Fox is our editor. Music by Coma Media. Special thanks to Maureen Hoch, Adi Ignatius, Karen Player, Ramsey Khabbaz, Nicole Smith, Anne Bartholomew, and you – our listener.

See you next week.

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Study: Monkeys are much smarter than we thought they were

I n a groundbreaking study published today in the journal Nature Neuroscience , researchers have discovered that monkeys, much like humans , are capable of complex deliberation and careful decision-making. 

This new finding challenges the long-held belief that humans alone possess the ability to think deeply about a problem and consider multiple factors such as costs, consequences, and constraints in order to arrive at optimal outcomes.

"Humans are not the only animals capable of slow and thoughtful deliberation," said study senior author Dr. William Stauffer from the University of Pittsburgh School of Medicine. "Our work shows that monkeys have a rich mental state that renders them capable of intelligent thinking. It's a new paradigm for studying the neurophysiological basis for deliberative thought."

The study raises important questions about the nature of thought processes and decision-making in animals, and whether other species are also capable of engaging in the same level of complexity as humans. It also helps to shed light on the cognitive processes at work when we, as humans, make decisions about various aspects of our lives, such as who to spend time with or what to study in school.

Several decades ago, Dr. Daniel Kahneman, a Nobel Prize laureate, revolutionized the field of behavioral economics with his Prospect Theory. In his seminal book, "Thinking Fast and Slow," Dr. Kahneman posited that humans employ two distinct systems of thinking: one nearly instantaneous and automatic, and the other much slower and reliant on conscious logical reasoning that requires greater mental effort.

How the study was done

Dr. Kahneman referred to the first type of thinking as "slow" and the second as "fast." Slow, effortful thinking enables us to engage in complex activities such as writing music, developing scientific hypotheses, and balancing our checkbooks. Until now, it was believed that slow thinking was a uniquely human trait.

However, this latest research turns that notion on its head. By presenting monkeys with combinatorial optimization problems, which the researchers dubbed the "knapsack task," and rewarding the animals based on the value of their solutions, the study demonstrated that monkeys employed sophisticated mathematical reasoning and used efficient computational algorithms to tackle complex problems.

The scientists found that the animals' performance and speed of deliberation were dependent on the task's complexity, and that their solutions closely mirrored those generated by efficient computer algorithms specifically designed to solve the optimization problem.

"Results from this work will contribute neurophysiological evidence to enlighten centuries of discussions about dual process theories of the mind, the structure of thoughts, and the neurobiological basis of intuition and reasoning," wrote Stauffer in an accompanying research briefing.

Tao Hong of Carnegie Mellon University is the lead author of the paper. The study's findings not only provide valuable insights into the cognitive abilities of monkeys but also pave the way for a new paradigm in studying the neurophysiological basis for deliberative thought, with potential implications for better understanding the complex nature of decision-making across various species.

More about monkeys

Monkeys are a diverse group of primates that belong to the infraorder Simiiformes. They are divided into two major groups: New World monkeys, native to Central and South America, and Old World monkeys, native to Africa and Asia. Monkeys are known for their intelligence, social behavior, and adaptability to different environments.

Physical characteristics

Monkeys vary greatly in size and appearance, ranging from the tiny pygmy marmoset, which measures just 4.6-6.2 inches (12-16 cm) in length, to the large mandrill, which can reach up to 37 inches (94 cm) in length. 

Monkeys typically have forward-facing eyes, flat faces, and dexterous hands with opposable thumbs. Some species also have prehensile tails, which they use to grasp and manipulate objects or to hang from branches.

Most monkeys are omnivores, eating a diverse diet that includes fruits, leaves, seeds, insects, and small animals. Some species, like the howler monkey, primarily consume leaves, while others, like the capuchin monkey, have a more varied diet.

Social behavior

Monkeys are highly social animals that usually live in groups called troops. These troops can range in size from just a few individuals to hundreds of members. Social hierarchies are common in monkey troops, with dominant individuals enjoying benefits like better access to food and mating opportunities. Monkeys communicate through vocalizations, body language, and facial expressions, and they often engage in grooming behaviors to maintain social bonds.

Intelligence and tool use

Monkeys are known for their cognitive abilities, problem-solving skills, and in some cases, their use of tools. Capuchin monkeys, for example, have been observed using rocks to crack open nuts, while some macaques have been seen using sticks to extract insects from tree bark. 

Research has also shown that monkeys are capable of understanding basic arithmetic and recognizing themselves in mirrors, which is considered a sign of self-awareness.

Conservation

Many monkey species are threatened by habitat loss, hunting, and the illegal pet trade. Conservation efforts are underway to protect these primates and their habitats, including the establishment of protected areas, reintroduction programs, and education campaigns to raise awareness about the importance of monkey conservation.

In conclusion, monkeys are fascinating and intelligent creatures with complex social structures and diverse behaviors. As we continue to study these primates, we gain a greater understanding of their cognitive abilities and the evolutionary links between humans and other primates.

Other animals that demonstrate problem-solving ability

Yes, numerous animals demonstrate problem-solving abilities, indicating the presence of intelligence and cognitive skills across various species. Some examples of animals with notable problem-solving capabilities include:

Crows and other corvids

These birds are known for their exceptional problem-solving skills and have been observed using tools to access food. For instance, they can use sticks to extract insects from tree bark or crevices and even bend wires to create hooks for retrieving food from hard-to-reach places.

Elephants are highly intelligent animals capable of complex problem-solving. They have been observed using sticks and branches to swat flies or scratch hard-to-reach areas and can also recognize themselves in mirrors, suggesting self-awareness. Elephants have displayed the ability to cooperate and work together to solve problems, such as pulling a rope simultaneously to access food.

Dolphins are known for their intelligence and problem-solving abilities. They have been observed using tools like sponges to protect their snouts while foraging on the ocean floor. Dolphins can also learn and understand complex commands and have been shown to recognize themselves in mirrors, indicating self-awareness.

These highly intelligent invertebrates have demonstrated remarkable problem-solving skills. Octopuses have been observed opening jars, navigating mazes, and escaping from enclosures by manipulating objects and their environment. Their impressive learning and memory capabilities make them formidable problem solvers.

Domesticated dogs have evolved alongside humans and have developed a range of problem-solving skills. They can learn commands, understand gestures, and follow human cues to solve problems, such as locating hidden objects or navigating obstacles. Some breeds, like border collies and poodles, are especially known for their intelligence and problem-solving abilities.

Chimpanzees

As our closest living relatives, chimpanzees share many cognitive traits with humans. They have been observed using tools, such as sticks to extract termites from their mounds, and leaves as sponges to collect water. Chimpanzees also display complex social behaviors, such as cooperation and deception, which require problem-solving skills.

Rats are intelligent rodents that have shown the ability to solve problems and learn from their experiences. They can navigate complex mazes, recognize patterns, and demonstrate a rudimentary understanding of cause and effect. Rats have also been observed using tools and adapting their behavior based on previous experiences.

These examples illustrate that problem-solving abilities are not exclusive to humans and can be found across various animal species. Studying these animals and their cognitive skills can provide valuable insights into the evolution of intelligence and the diversity of problem-solving strategies in the animal kingdom.

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• Open access
• Published: 17 February 2022

Effectiveness of problem-based learning methodology in undergraduate medical education: a scoping review

• Joan Carles Trullàs   ORCID: orcid.org/0000-0002-7380-3475 1 , 2 , 3 ,
• Carles Blay   ORCID: orcid.org/0000-0003-3962-5887 1 , 4 ,
• Elisabet Sarri   ORCID: orcid.org/0000-0002-2435-399X 3 &
• Ramon Pujol   ORCID: orcid.org/0000-0003-2527-385X 1  

BMC Medical Education volume  22 , Article number:  104 ( 2022 ) Cite this article

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Problem-based learning (PBL) is a pedagogical approach that shifts the role of the teacher to the student (student-centered) and is based on self-directed learning. Although PBL has been adopted in undergraduate and postgraduate medical education, the effectiveness of the method is still under discussion. The author’s purpose was to appraise available international evidence concerning to the effectiveness and usefulness of PBL methodology in undergraduate medical teaching programs.

The authors applied the Arksey and O’Malley framework to undertake a scoping review. The search was carried out in February 2021 in PubMed and Web of Science including all publications in English and Spanish with no limits on publication date, study design or country of origin.

The literature search identified one hundred and twenty-four publications eligible for this review. Despite the fact that this review included many studies, their design was heterogeneous and only a few provided a high scientific evidence methodology (randomized design and/or systematic reviews with meta-analysis). Furthermore, most were single-center experiences with small sample size and there were no large multi-center studies. PBL methodology obtained a high level of satisfaction, especially among students. It was more effective than other more traditional (or lecture-based methods) at improving social and communication skills, problem-solving and self-learning skills. Knowledge retention and academic performance weren’t worse (and in many studies were better) than with traditional methods. PBL was not universally widespread, probably because requires greater human resources and continuous training for its implementation.

PBL is an effective and satisfactory methodology for medical education. It is likely that through PBL medical students will not only acquire knowledge but also other competencies that are needed in medical professionalism.

Peer Review reports

There has always been enormous interest in identifying the best learning methods. In the mid-twentieth century, US educator Edgar Dale proposed which actions would lead to deeper learning than others and published the well-known (and at the same time controversial) “Cone of Experience or Cone of Dale”. At the apex of the cone are oral representations (verbal descriptions, written descriptions, etc.) and at the base is direct experience (based on a person carrying out the activity that they aim to learn), which represents the greatest depth of our learning. In other words, each level of the cone corresponds to various learning methods. At the base are the most effective, participative methods (what we do and what we say) and at the apex are the least effective, abstract methods (what we read and what we hear) [ 1 ]. In 1990, psychologist George Miller proposed a framework pyramid to assess clinical competence. At the lowest level of the pyramid is knowledge (knows), followed by the competence (knows how), execution (shows how) and finally the action (does) [ 2 ]. Both Miller’s pyramid and Dale’s cone propose a very efficient way of training and, at the same time, of evaluation. Miller suggested that the learning curve passes through various levels, from the acquisition of theoretical knowledge to knowing how to put this knowledge into practice and demonstrate it. Dale stated that to remember a high percentage of the acquired knowledge, a theatrical representation should be carried out or real experiences should be simulated. It is difficult to situate methodologies such as problem-based learning (PBL), case-based learning (CBL) and team-based learning (TBL) in the context of these learning frameworks.

In the last 50 years, various university education models have emerged and have attempted to reconcile teaching with learning, according to the principle that students should lead their own learning process. Perhaps one of the most successful models is PBL that came out of the English-speaking environment. There are many descriptions of PBL in the literature, but in practice there is great variability in what people understand by this methodology. The original conception of PBL as an educational strategy in medicine was initiated at McMaster University (Canada) in 1969, leaving aside the traditional methodology (which is often based on lectures) and introducing student-centered learning. The new formulation of medical education proposed by McMaster did not separate the basic sciences from the clinical sciences, and partially abandoned theoretical classes, which were taught after the presentation of the problem. In its original version, PBL is a methodology in which the starting point is a problem or a problematic situation. The situation enables students to develop a hypothesis and identify learning needs so that they can better understand the problem and meet the established learning objectives [ 3 , 4 ]. PBL is taught using small groups (usually around 8–10 students) with a tutor. The aim of the group sessions is to identify a problem or scenario, define the key concepts identified, brainstorm ideas and discuss key learning objectives, research these and share this information with each other at subsequent sessions. Tutors are used to guide students, so they stay on track with the learning objectives of the task. Contemporary medical education also employs other small group learning methods including CBL and TBL. Characteristics common to the pedagogy of both CBL and TBL include the use of an authentic clinical case, active small-group learning, activation of existing knowledge and application of newly acquired knowledge. In CBL students are encouraged to engage in peer learning and apply new knowledge to these authentic clinical problems under the guidance of a facilitator. CBL encourages a structured and critical approach to clinical problem-solving, and, in contrast to PBL, is designed to allow the facilitator to correct and redirect students [ 5 ]. On the other hand, TBL offers a student-centered, instructional approach for large classes of students who are divided into small teams of typically five to seven students to solve clinically relevant problems. The overall similarities between PBL and TBL relate to the use of professionally relevant problems and small group learning, while the main difference relates to one teacher facilitating interactions between multiple self-managed teams in TBL, whereas each small group in PBL is facilitated by one teacher. Further differences are related to mandatory pre-reading assignments in TBL, testing of prior knowledge in TBL and activating prior knowledge in PBL, teacher-initiated clarifying of concepts that students struggled with in TBL versus students-generated issues that need further study in PBL, inter-team discussions in TBL and structured feedback and problems with related questions in TBL [ 6 ].

In the present study we have focused on PBL methodology, and, as attractive as the method may seem, we should consider whether it is really useful and effective as a learning method. Although PBL has been adopted in undergraduate and postgraduate medical education, the effectiveness (in terms of academic performance and/or skill improvement) of the method is still under discussion. This is due partly to the methodological difficulty in comparing PBL with traditional curricula based on lectures. To our knowledge, there is no systematic scoping review in the literature that has analyzed these aspects.

The main motivation for carrying out this research and writing this article was scientific but also professional interest. We believe that reviewing the state of the art of this methodology once it was already underway in our young Faculty of Medicine, could allow us to know if we were on the right track and if we should implement changes in the training of future doctors.

The primary goal of this study was to appraise available international evidence concerning to the effectiveness and usefulness of PBL methodology in undergraduate medical teaching programs. As the intention was to synthesize the scattered evidence available, the option was to conduct a scoping review. A scoping study tends to address broader topics where many different study designs might be applicable. Scoping studies may be particularly relevant to disciplines, such as medical education, in which the paucity of randomized controlled trials makes it difficult for researchers to undertake systematic reviews [ 7 , 8 ]. Even though the scoping review methodology is not widely used in medical education, it is well established for synthesizing heterogeneous research evidence [ 9 ].

The specific aims were: 1) to determine the effectiveness of PBL in academic performance (learning and retention of knowledge) in medical education; 2) to determine the effectiveness of PBL in other skills (social and communication skills, problem solving or self-learning) in medical education; 3) to know the level of satisfaction perceived by the medical students (and/or tutors) when they are taught with the PBL methodology (or when they teach in case of tutors).

This review was guided by Arksey and O’Malley’s methodological framework for conducting scoping reviews. The five main stages of the framework are: (1) identifying the research question; (2) ascertaining relevant studies; (3) determining study selection; (4) charting the data; and (5) collating, summarizing and reporting the results [ 7 ]. We reported our process according to the PRISMA Extension for Scoping Reviews [ 10 ].

Stage 1: Identifying the research question

With the goals of the study established, the four members of the research team established the research questions. The primary research question was “What is the effectiveness of PBL methodology for learning in undergraduate medicine?” and the secondary question “What is the perception and satisfaction of medical students and tutors in relation to PBL methodology?”.

Stage 2: Identifying relevant studies

After the research questions and a search strategy were defined, the searches were conducted in PubMed and Web of Science using the MeSH terms “problem-based learning” and “Medicine” (the Boolean operator “AND” was applied to the search terms). No limits were set on language, publication date, study design or country of origin. The search was carried out on 14th February 2021. Citations were uploaded to the reference manager software Mendeley Desktop (version 1.19.8) for title and abstract screening, and data characterization.

Stage 3: Study selection

The searching strategy in our scoping study generated a total of 2399 references. The literature search and screening of title, abstract and full text for suitability was performed independently by one author (JCT) based on predetermined inclusion criteria. The inclusion criteria were: 1) PBL methodology was the major research topic; 2) participants were undergraduate medical students or tutors; 3) the main outcome was academic performance (learning and knowledge retention); 4) the secondary outcomes were one of the following: social and communication skills, problem solving or self-learning and/or student/tutor satisfaction; 5) all types of studies were included including descriptive papers, qualitative, quantitative and mixed studies methods, perspectives, opinion, commentary pieces and editorials. Exclusion criteria were studies including other types of participants such as postgraduate medical students, residents and other health non-medical specialties such as pharmacy, veterinary, dentistry or nursing. Studies published in languages other than Spanish and English were also excluded. Situations in which uncertainty arose, all authors (CB, ES, RP) discussed the publication together to reach a final consensus. The outcomes of the search results and screening are presented in Fig.  1 . One-hundred and twenty-four articles met the inclusion criteria and were included in the final analysis.

Study flow PRISMA diagram. Details the review process through the different stages of the review; includes the number of records identified, included and excluded

Stage 4: Charting the data

A data extraction table was developed by the research team. Data extracted from each of the 124 publications included general publication details (year, author, and country), sample size, study population, design/methodology, main and secondary outcomes and relevant results and/or conclusions. We compiled all data into a single spreadsheet in Microsoft Excel for coding and analysis. The characteristics and the study subject of the 124 articles included in this review are summarized in Tables 1 and 2 . The detailed results of the Microsoft Excel file is also available in Additional file 1 .

Stage 5: Collating, summarizing and reporting the results

As indicated in the search strategy (Fig.  1 ) this review resulted in the inclusion of 124 publications. Publication years of the final sample ranged from 1990 to 2020, the majority of the publications (51, 41%) were identified for the years 2010–2020 and the years in which there were more publications were 2001, 2009 and 2015. Countries from the six continents were represented in this review. Most of the publications were from Asia (especially China and Saudi Arabia) and North America followed by Europe, and few studies were from Africa, Oceania and South America. The country with more publications was the United States of America ( n  = 27). The most frequent designs of the selected studies were surveys or questionnaires ( n  = 45) and comparative studies ( n  = 48, only 16 were randomized) with traditional or lecture-based learning methodologies (in two studies the comparison was with simulation) and the most frequently measured outcomes were academic performance followed by student satisfaction (48 studies measured more than one outcome). The few studies with the highest level of scientific evidence (systematic review and meta-analysis and randomized studies) were conducted mostly in Asian countries (Tables  1 and 2 ). The study subject was specified in 81 publications finding a high variability but at the same time great representability of almost all disciplines of the medical studies.

The sample size was available in 99 publications and the median [range] of the participants was 132 [14–2061]. According to study population, there were more participants in the students’ focused studies (median 134 and range 16–2061) in comparison with the tutors’ studies (median 53 and range 14–494).

Finally, after reviewing in detail the measured outcomes (main and secondary) according to the study design (Table 2 and Additional file 1 ) we present a narrative overview and a synthesis of the main findings.

Main outcome: academic performance (learning and knowledge retention)

Seventy-one of the 124 publications had learning and/or knowledge retention as a measured outcome, most of them ( n  = 45) were comparative studies with traditional or lecture-based learning and 16 were randomized. These studies were varied in their methodology, were performed in different geographic zones, and normally analyzed the experience of just one education center. Most studies ( n  = 49) reported superiority of PBL in learning and knowledge acquisition [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ] but there was no difference between traditional and PBL curriculums in another 19 studies [ 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 ]. Only three studies reported that PBL was less effective [ 79 , 80 , 81 ], two of them were randomized (in one case favoring simulation-based learning [ 80 ] and another favoring lectures [ 81 ]) and the remaining study was based on tutors’ opinion rather than real academic performance [ 79 ]. It is noteworthy that the four systematic reviews and meta-analysis included in this scoping review, all carried out in China, found that PBL was more effective than lecture-based learning in improving knowledge and other skills (clinical, problem-solving, self-learning and collaborative) [ 40 , 51 , 53 , 58 ]. Another relevant example of the superiority of the PBL method over the traditional method is the experience reported by Hoffman et al. from the University of Missouri-Columbia. The authors analyzed the impact of implementing the PBL methodology in its Faculty of Medicine and revealed an improvement in the academic results that lasted for over a decade [ 31 ].

Secondary outcomes

Social and communication skills.

We found five studies in this scoping review that focused on these outcomes and all of them described that a curriculum centered on PBL seems to instill more confidence in social and communication skills among students. Students perceived PBL positively for teamwork, communication skills and interpersonal relations [ 44 , 45 , 67 , 75 , 82 ].

Student satisfaction

Sixty publications analyzed student satisfaction with PBL methodology. The most frequent methodology were surveys or questionnaires (30 studies) followed by comparative studies with traditional or lecture-based methodology (19 studies, 7 of them were randomized). Almost all the studies (51) have shown that PBL is generally well-received [ 11 , 13 , 18 , 19 , 20 , 21 , 22 , 26 , 29 , 34 , 37 , 39 , 41 , 42 , 46 , 50 , 56 , 58 , 63 , 64 , 66 , 78 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 ] but in 9 studies the overall satisfaction scores for the PBL program were neutral [ 76 , 111 , 112 , 113 , 114 , 115 , 116 ] or negative [ 117 , 118 ]. Some factors that have been identified as key components for PBL to be successful include: a small group size, the use of scenarios of realistic cases and good management of group dynamics. Despite a mostly positive assessment of the PBL methodology by the students, there were some negative aspects that could be criticized or improved. These include unclear communication of the learning methodology, objectives and assessment method; bad management and organization of the sessions; tutors having little experience of the method; and a lack of standardization in the implementation of the method by the tutors.

Tutor satisfaction

There are only 15 publications that analyze the satisfaction of tutors, most of them surveys or questionnaires [ 85 , 88 , 92 , 98 , 108 , 110 , 119 ]. In comparison with the satisfaction of the students, here the results are more neutral [ 112 , 113 , 115 , 120 , 121 ] and even unfavorable to the PBL methodology in two publications [ 117 , 122 ]. PBL teaching was favored by tutors when the institutions train them in the subject, when there was administrative support and adequate infrastructure and coordination [ 123 ]. In some experiences, the PBL modules created an unacceptable toll of anxiety, unhappiness and strained relations.

Other skills (problem solving and self-learning)

The effectiveness of the PBL methodology has also been explored in other outcomes such as the ability to solve problems and to self-directed learning. All studies have shown that PBL is more effective than lecture-based learning in problem-solving and self-learning skills [ 18 , 24 , 40 , 48 , 67 , 75 , 93 , 104 , 124 ]. One single study found a poor accuracy of the students’ self-assessment when compared to their own performance [ 125 ]. In addition, there are studies that support PBL methodology for integration between basic and clinical sciences [ 126 ].

Finally, other publications have reported the experience of some faculties in the implementation of the PBL methodology. Different experiences have demonstrated that it is both possible and feasible to shift from a traditional curriculum to a PBL program, recognizing that PBL methodology is complex to plan and structure, needs a large number of human and material resources, requiring an immense teacher effort [ 28 , 31 , 94 , 127 , 128 , 129 , 130 , 131 , 132 , 133 ]. In addition, and despite its cost implication, a PBL curriculum can be successfully implemented in resource-constrained settings [ 134 , 135 ].

We conducted this scoping review to explore the effectiveness and satisfaction of PBL methodology for teaching in undergraduate medicine and, to our knowledge, it is the only study of its kind (systematic scoping review) that has been carried out in the last years. Similarly, Vernon et al. conducted a meta-analysis of articles published between 1970 and 1992 and their results generally supported the superiority of the PBL approach over more traditional methods of medical education [ 136 ]. PBL methodology is implemented in medical studies on the six continents but there is more experience (or at least more publications) from Asian countries and North America. Despite its apparent difficulties on implementation, a PBL curriculum can be successfully implemented in resource-constrained settings [ 134 , 135 ]. Although it is true that the few studies with the highest level of scientific evidence (randomized studies and meta-analysis) were carried out mainly in Asian countries (and some in North America and Europe), there were no significant differences in the main results according to geographical origin.

In this scoping review we have included a large number of publications that, despite their heterogeneity, tend to show favorable results for the usefulness of the PBL methodology in teaching and learning medicine. The results tend to be especially favorable to PBL methodology when it is compared with traditional or lecture-based teaching methods, but when compared with simulation it is not so clear. There are two studies that show neutral [ 71 ] or superior [ 80 ] results to simulation for the acquisition of specific clinical skills. It seems important to highlight that the four meta-analysis included in this review, which included a high number of participants, show results that are clearly favorable to the PBL methodology in terms of knowledge, clinical skills, problem-solving, self-learning and satisfaction [ 40 , 51 , 53 , 58 ].

Regarding the level of satisfaction described in the surveys or questionnaires, the overall satisfaction rate was higher in the PBL students when compared with traditional learning students. Students work in small groups, allowing and promoting teamwork and facilitating social and communication skills. As sessions are more attractive and dynamic than traditional classes, this could lead to a greater degree of motivation for learning.

These satisfaction results are not so favorable when tutors are asked and this may be due to different reasons; first, some studies are from the 90s, when the methodology was not yet fully implemented; second, the number of tutors included in these studies is low; and third, and perhaps most importantly, the complaints are not usually due to the methodology itself, but rather due to lack of administrative support, and/or work overload. PBL methodology implies more human and material resources. The lack of experience in guided self-learning by lecturers requires more training. Some teachers may not feel comfortable with the method and therefore do not apply it correctly.

Despite how effective and/or attractive the PBL methodology may seem, some (not many) authors are clearly detractors and have published opinion articles with fierce criticism to this methodology. Some of the arguments against are as follows: clinical problem solving is the wrong task for preclinical medical students, self-directed learning interpreted as self-teaching is not appropriate in undergraduate medical education, relegation to the role of facilitators is a misuse of the faculty, small-group experience is inherently variable and sometimes dysfunctional, etc. [ 137 ].

In light of the results found in our study, we believe that PBL is an adequate methodology for the training of future doctors and reinforces the idea that the PBL should have an important weight in the curriculum of our medical school. It is likely that training through PBL, the doctors of the future will not only have great knowledge but may also acquire greater capacity for communication, problem solving and self-learning, all of which are characteristics that are required in medical professionalism. For this purpose, Koh et al. analyzed the effect that PBL during medical school had on physician competencies after graduation, finding a positive effect mainly in social and cognitive dimensions [ 138 ].

Despite its defects and limitations, we must not abandon this methodology and, in any case, perhaps PBL should evolve, adapt, and improve to enhance its strengths and improve its weaknesses. It is likely that the new generations, trained in schools using new technologies and methodologies far from lectures, will feel more comfortable (either as students or as tutors) with methodologies more like PBL (small groups and work focused on problems or projects). It would be interesting to examine the implementation of technologies and even social media into PBL sessions, an issue that has been poorly explorer [ 139 ].

Limitations

Scoping reviews are not without limitations. Our review includes 124 articles from the 2399 initially identified and despite our efforts to be as comprehensive as possible, we may have missed some (probably few) articles. Even though this review includes many studies, their design is very heterogeneous, only a few include a large sample size and high scientific evidence methodology. Furthermore, most are single-center experiences and there are no large multi-center studies. Finally, the frequency of the PBL sessions (from once or twice a year to the whole curriculum) was not considered, in part, because most of the revised studies did not specify this information. This factor could affect the efficiency of PBL and the perceptions of students and tutors about PBL. However, the adoption of a scoping review methodology was effective in terms of summarizing the research findings, identifying limitations in studies’ methodologies and findings and provided a more rigorous vision of the international state of the art.

Conclusions

This systematic scoping review provides a broad overview of the efficacy of PBL methodology in undergraduate medicine teaching from different countries and institutions. PBL is not a new teaching method given that it has already been 50 years since it was implemented in medicine courses. It is a method that shifts the leading role from teachers to students and is based on guided self-learning. If it is applied properly, the degree of satisfaction is high, especially for students. PBL is more effective than traditional methods (based mainly on lectures) at improving social and communication skills, problem-solving and self-learning skills, and has no worse results (and in many studies better results) in relation to academic performance. Despite that, its use is not universally widespread, probably because it requires greater human resources and continuous training for its implementation. In any case, more comparative and randomized studies and/or other systematic reviews and meta-analysis are required to determine which educational strategies could be most suitable for the training of future doctors.

Abbreviations

• Problem-based learning

Case-based learning

Team-based learning

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Trullàs, J.C., Blay, C., Sarri, E. et al. Effectiveness of problem-based learning methodology in undergraduate medical education: a scoping review. BMC Med Educ 22 , 104 (2022). https://doi.org/10.1186/s12909-022-03154-8

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Published on 2.4.2024 in Vol 11 (2024)

Evaluating the Usability of an mHealth App for Empowering Cancer Survivors With Disabilities: Heuristic Evaluation and Usability Testing

Authors of this article:

Original Paper

• Rachel F Adler 1, 2 , PhD   ; 
• Kevin Baez 1 , BS   ; 
• Paulina Morales 1 , BS   ; 
• Jocelyn Sotelo 1 , BS   ; 
• David Victorson 3 , PhD   ; 
• Susan Magasi 4 , PhD  

1 Department of Computer Science, Northeastern Illinois University, Chicago, IL, United States

2 School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL, United States

3 Department of Medical Social Sciences, Northwestern University, Evanston, IL, United States

4 Department of Occupational Therapy, University of Illinois Chicago, Chicago, IL, United States

Corresponding Author:

Rachel F Adler, PhD

School of Information Sciences

University of Illinois Urbana-Champaign

501 E. Daniel St.

Champaign, IL, 61820

United States

Phone: 1 217 244 4192

Email: [email protected]

Background: More than 18 million cancer survivors are living in the United States. The effects of cancer and its treatments can have cognitive, psychological, physical, and social consequences that many survivors find incredibly disabling. Posttreatment support is often unavailable or underused, especially for survivors living with disabilities. This leaves them to deal with new obstacles and struggles on their own, oftentimes feeling lost during this transition. Mobile health (mHealth) interventions have been shown to effectively aid cancer survivors in dealing with many of the aftereffects of cancer and its treatments; these interventions hold immense potential for survivors living with disabilities. We developed a prototype for WeCanManage, an mHealth-delivered self-management intervention to empower cancer survivors living with disabilities through problem-solving, mindfulness, and self-advocacy training.

Objective: Our study conducted a heuristic evaluation of the WeCanManage high-fidelity prototype and assessed its usability among cancer survivors with known disabilities.

Methods: We evaluated the prototype using Nielsen’s 10 principles of heuristic evaluation with 22 human-computer interaction university students. On the basis of the heuristic evaluation findings, we modified the prototype and conducted usability testing on 10 cancer survivors with a variety of known disabilities, examining effectiveness, efficiency, usability, and satisfaction, including a completion of the modified System Usability Scale (SUS).

Results: The findings from the heuristic evaluation were mostly favorable, highlighting the need for a help guide, addressing accessibility concerns, and enhancing the navigation experience. After usability testing, the average SUS score was 81, indicating a good-excellent design. The participants in the usability testing sample expressed positive reactions toward the app’s design, educational content and videos, and the available means of connecting with others. They identified areas for improvement, such as improving accessibility, simplifying navigation within the community forums, and providing a more convenient method to access the help guide.

Conclusions: Overall, usability testing showed positive results for the design of WeCanManage. The course content and features helped participants feel heard, understood, and less alone.

Introduction

There are an estimated 18.1 million cancer survivors in the United States, and the number is projected to increase to 22.5 million by 2032 [ 1 ]. Approximately 40% of cancer survivors experience long-term physical, cognitive, psychological, and social consequences of cancer and its treatment, which can lead to significant disability [ 2 ]. These effects can include physical challenges, including but not limited to pain, fatigue, decreased functional mobility, limb loss, lymphedema, speech and swallowing difficulties, emotional challenges (as cancer survivors may experience anxiety or depression), and cognitive challenges (such as “chemo brain”) [ 3 - 5 ]. These aftereffects can lead to activity limitations and participation restrictions, which according to contemporary frameworks and legal definitions may be considered as disabilities [ 6 , 7 ]. Yet, even with significant functional impairments, not all cancer survivors self-identify as disabled [ 8 , 9 ]. Regardless of the terminology used, the aftereffects of cancer and their related functional impacts can have a significant negative impact on well-being and health-related quality of life [ 10 ]. Survivorship plans and rehabilitation programs, which play a crucial role in restoring survivors’ physical and emotional well-being, are frequently underused by cancer survivors [ 11 ]. This can be due to obstacles like time, financial constraints, and transportation issues [ 12 ], which hinder their accessibility. Mobile health (mHealth) apps can help make rehabilitation services accessible and put them in the hands of those who need them.

mHealth Apps

Mobile technologies—smartphones, tablets, and smartwatches—are increasingly ubiquitous in today’s society and can be used almost anywhere [ 13 ]. The Pew Research Center reports that 85% of American adults own smartphones, and the ownership is relatively consistent across genders; racial groups; and urban, suburban, and rural users [ 14 ]. This leads to an increase in the development of mHealth apps. The COVID-19 pandemic has led to mHealth strategies becoming even more important in cancer care. According to the recommendations of Curigliano et al [ 15 ], patients with cancer should be offered mHealth strategies to support symptom management and adoption of healthy behaviors. The number of mHealth apps has increased throughout the years, with around 325,000 apps available in 2017 [ 16 ]. Charbonneau et al [ 17 ] identified 123 mHealth apps for cancer survivors available in the 2 most important marketplaces (ie, Apple iTunes and Google Play). Typical areas of usage in cancer are disease management support (eg, symptom monitoring, management of side effects, medication reminder and dosing, and access to health information), support of healthy behavior (eg, healthy diet and increased physical activity), or the connection with other patients (eg, social support through peers) [ 18 - 20 ].

Evaluating the Usability of mHealth Apps

It is important to gather qualitative and quantitative data on mHealth apps to determine how satisfied users would be with the product at hand. According to one scoping review, of 133 different eHealth articles that conducted usability testing, 105 used questionnaires, 57 used task completion, 45 used “think aloud,” 37 conducted interviews, 18 performed heuristic evaluation, and 13 used focus groups [ 21 ]. The System Usability Scale (SUS) was the most frequently used questionnaire with a total of 44 studies. A combination of methods was used in 88 of the studies. Further, cancer was tied as the second most frequently evaluated health condition (n=10), with only mental health being evaluated more often (n=12).

Usability testing is a common effective method for evaluating the usability of mHealth apps. Studies have shown that usability testing is an effective method for examining mHealth apps for diabetes [ 22 , 23 ], depression [ 22 , 24 ], and youth at risk for developing psychosis [ 25 ], as well as managing pain [ 26 ], heart failure [ 27 ], and cancer symptoms [ 28 ]. Common questionnaires often included variations on the Mobile Application Rating Scale [ 25 , 27 ] or the SUS [ 22 , 24 , 26 ]. Additional techniques often employed in usability testing include measuring time per task [ 26 ] and using think aloud techniques [ 29 ]. In addition to evaluating fully implemented mobile apps, studies have conducted usability testing on prototypes of mHealth apps for supporting mental health [ 30 ], chronic kidney disease [ 29 ], fall risk detection system for older users [ 31 ], HIV [ 32 ], and cancer survivors [ 33 - 35 ]. Many studies have conducted heuristic evaluation before usability testing on an mHealth prototype to fix usability issues before bringing it to users [ 28 , 29 , 32 , 33 ]. While Nielsen’s 10-point usability heuristics [ 36 ] are geared toward computer-based applications, most of these are also applicable in mobile app design. The SUS questionnaire was also commonly used in usability testing studies for examining mHealth prototypes [ 29 , 31 , 37 ].

WeCanManage App

We designed a high-fidelity prototype for WeCanManage, an evidence-informed mHealth self-management intervention, aimed at empowering individuals with tools to effectively manage cancer as a chronic condition. Users are asked to log into the app daily for 5-10 minutes to complete mobile microlearning modules of self-management content. The intervention content is based on extensive literature review and formative interviews with cancer survivors with known disabilities (n=30) and supportive cancer care professionals including social workers, psychologists, occupational and physical therapists, and a physiatrist specializing in cancer rehabilitation (n=5) [ 9 ]. A team of survivor scientists, people with lived experiences of cancer and disability, further informed intervention content and focus. Intervention content is presented sequentially as information is scaffolded on itself to promote depth of learning, retention, and application. The content is divided into 4 broad sections: WeCanRelate (fosters a sense of validating and normalizing the survivorship experience), WeCanAdapt (teaches goal direction self-management strategies), WeCanBe (emphasizes mindfulness-based practices), and WeCanSpeakUp (addresses self-advocacy and disability rights). In addition to the instructional content, WeCanManage provides users with 3 circles of support, including one-on-one connections with other users (Connect to Peers [C2P]), community forums (to discuss intervention content and shared experiences with the entire user community), and a library with evidence-informed educational content [ 38 ]. We conducted a thorough evaluation of the usability of the high-fidelity prototype for cancer survivors with disabilities, employing both heuristic evaluation and usability testing to assess its effectiveness in addressing the unique needs and challenges of this user group.

WeCanManage High-Fidelity Prototype

The high-fidelity prototype was created on Marvel [ 39 ], a web-based collaborative design platform that provides tools for creating wireframes, designs, and prototypes of interactive applications. We aimed to design WeCanManage specifically for smartphone usage. The prototype of WeCanManage allows users to navigate between the Home, Journey (Courses), C2P, Community (Community Forum), and Library (see Figure 1 ).

The Course section provides cancer survivors with an educational intervention that works with them on dealing with the long-term effects of their newly acquired disabilities through problem-solving, mindfulness, and self-advocacy. The content is designed to be a 4-week program where the user unlocks a series of microlessons divided into 4 modules (WeCanRelate, WeCanAdapt, WeCanBreathe, and WeCanSpeakUp), which educate users with different methods to deal with the effects of postcancer treatment in their daily life. To prioritize user control and accessibility, the course content is conveyed through mobile microlearning modules, presented in different formats such as readable text, clickable text-based cards, and audio ( Figure 2 ).

At the end of many of the daily sessions, there are interactive engagement activities, such as reflections that feed into the Community Forum and knowledge checks (see Figure 3 ). The engagement activities are designed to support consolidation of knowledge and application of course content to the user’s lived experiences.

The Community and C2P sections offer users a chance to engage with others, fostering networking opportunities and creating a support system with individuals undergoing similar experiences. C2P facilitates connections with others, allowing users to filter by categories like cancer type and disability, while Community features discussion forums for each of the 4 course sections and an open discussion forum. Lastly, the Library section contains additional evidence-informed resources such as articles and factsheets. The various sections of the prototypes were initially created as a low-fidelity prototype through an iterative co-design approach involving both the design teams and cancer survivors, who served as representatives of our targeted audience [ 40 ].

Because of its prototype nature, users could navigate all links, but functionalities such as real-time chat with other users and composing reflections or community posts were not operational. To overcome this, we incorporated simulated features in the prototype, triggering them automatically on user interaction. After creating the high-fidelity prototype, we evaluated it through 2 distinct methods: heuristic evaluation and usability testing.

Methodology for Heuristic Evaluation

Nielsen’s 10 principles of heuristic evaluation [ 36 ] were used for the initial testing of the prototype ( Textbox 1 ). The prototype was given to 22 undergraduate students at a Midwestern university taking a human-computer interaction course in the Spring of 2022 who were trained in conducting heuristic evaluation. No supplemental demographic data were gathered. They were given the WeCanManage prototype during a class period of 1 hour 15 minutes. During the session, students were split into 6 groups, and each group was given 5 tasks to complete using the prototype. We created 3 sets of 5 tasks, and therefore every 2 groups completed the same tasks. The tasks included going through the introduction course module, switching to text and video fields, and filtering the users by a specific disability through the C2P page. Students logged in to classroom computers and accessed Maze, an online testing platform used to monitor assessment details [ 41 ], recorded the path taken by students to complete tasks, and presented questions about their experience to help track their progress. At the end of the session, the groups documented violations of the 10 heuristic principles and rated their usability severity on a 0-4 scale, where 0 is not a usability problem and 4 is a usability catastrophe. Furthermore, the student evaluators filled out a questionnaire through Maze providing feedback and thoughts on the prototype’s design. The questionnaire covered their likes and dislikes of the design, their impressions of course modules, and the ease of changing the format of the content.

• Visibility of system status
• Match between system and the real world
• User control and freedom
• Consistency and standards
• Error prevention
• Recognition rather than recall
• Flexibility and efficiency of use
• Aesthetic and minimalist design
• Help users recognize, diagnose, and recover from errors
• Help and documentation

Methodology for Usability Testing

We modified the prototype based on the feedback from heuristic evaluation and conducted usability testing over Zoom. We used purposive sampling with targeted outreach through cancer survivorship networks, including both clinical and community. To be eligible for participation, individuals had to meet the following inclusion criteria: be 18 years or older; have a history of breast cancer, head and neck cancer, or sarcoma; have completed active treatment; self-identify as a person with a disability; and possess the ability to understand and communicate in English. Participants received a gift card for their time. Sessions lasted approximately 90 minutes. Sessions were recorded and participants shared their screens for data collection. Participants were told to connect to Zoom on a computer or laptop device. Usability testing occurred between September 2022 and February 2023. As we encountered minor issues with the Maze platform during the heuristic evaluation, including audio malfunctions, we transitioned to Ballpark, an extension of Marvel that facilitated usability testing of the prototype. Participants were given 8 tasks to complete (see Textbox 2 ). They were told that they were on day 6 of the 4-week period. Consequently, they could access content from sessions 1-6, while subsequent sessions remained locked to replicate the user’s sequential navigation experience, with new content being unlocked on a daily basis. The first 6 tasks were based on the course sessions and navigating through each course by reading the content cards and doing related engagement activities. Task 2 required participants to switch the viewing mode using the accessibility features (eye symbol) to the text-only mode, while task 6 involved watching a 1 minute 20 second–long mindfulness video, instead of the default card format. The final 2 tasks (tasks 7 and 8) focused on navigating the Community Forum and C2P sections. After each task, participants rated their satisfaction level and the time taken to complete each task using a 7-point Likert scale. On finishing all 8 tasks, participants had the opportunity to freely explore the app using a “think aloud” approach to express their thoughts and experiences.

To evaluate usability, participants completed the modified SUS, a reliable and valid 10-item questionnaire that assesses usability [ 42 , 43 ]. While the SUS has been around since 1986, it has been shown to be effective in evaluating the usability of recent health apps [ 44 ]. To calculate SUS scores, 1 is subtracted from the raw score of the odd-numbered items (those items phrased in a positive way), and the raw score of the even-numbered items (those items phrased in a negative way) is subtracted from 5. The total scores are then multiplied by 2.5 to derive the “standardized SUS score,” which ranges from 0 to 100. A SUS score of 68 is considered average usability [ 45 ], while a score above 80.3 is deemed an A grade, placing it in the top 10% of scores [ 46 ] and corresponding to a narrative rating of good-excellent [ 47 ]. In addition, we included open-ended questions to gather feedback on participants’ preferences and areas for improvement regarding the app. Examples of these questions include “How easy or difficult was it to see all the content on the screen?” and “What did you think of the design of the course modules?”

To assess the effectiveness of the app design, following a similar approach to Adler et al [ 48 ], we evaluated task completion by having 2 independent coders review each recording and code whether the participants

• Completed the task quickly on their own (C)
• Completed the task on their own though it took a little longer (L)
• Needed help to complete the task (H)

The coders achieved an agreement percentage of 87.5%. Any discrepancies were resolved through discussion. To assess efficiency, we analyzed the number of misclicks (clicks outside of clickable areas in the prototype) and the time taken to complete each task.

• Go to the Course and click on the WeCanRelate session. Read through all of the cards.
• Go to the Course and click on the Introduction session. Switch to Text view to read all the cards at once using the eye symbol on the bottom left of the first screen of the module.
• Go to the Course and click on the Celebrating & Taking Stock session. Read through all the cards and then go to the reflection. Start “typing” your reflection and post it. Do you see your post accurately reflected?
• Go to the Course and click on the Straight Talk About Symptoms session. Read through the cards and follow the link to the library and the Understanding the Cancer Rehabilitation Team Fact Sheet.
• Go to the Course and click on the Deep Breathing session. Read through the content and complete the knowledge check. Did you get the correct answer?
• Go to the Course and click on the Body Awareness session and go through to the end of the module by watching the video.
• Go to the Community Forum. Create a new post in the Open Discussion forum. Enter a title, select the community tag, enter text, and post your response.

Connect to Peers

• Find the Connect to Peers (C2P) option and filter to narrow the search to people who are deaf or hard of hearing.

Ethics Approval

We obtained institutional review board approval from the participating universities in the project (University of Illinois Chicago #2020-1067, Northeastern Illinois University #79, and Northwestern University #NUUIC21CC03). 

Results of Heuristic Evaluation

We conducted an analysis of the identified heuristic violations and their severity. The highest severity rating recorded was a 3, as illustrated in Figure 4 . The most frequent heuristic violations were related to flexibility, user control, and freedom, followed by error prevention. The issues identified were primarily navigation problems within the prototype, missing back buttons, and font size being too small. Suggestions for improvement were also raised, such as adding an FAQ page, a way to contact the creators or administrators, and including a walk-through or how-to page. Student evaluators expressed appreciation for the images and content, the knowledge check feature, the color scheme, and the layout. They found the app easy to read and navigate. The dislikes expressed included the absence of a help guide and nonfunctional back buttons. Additionally, some groups reported having difficulty finding the format button to switch the mode of learning to text-only or audio.

Modifications Based on Heuristic Evaluation

Drawing from the findings of the heuristic evaluation, we enhanced the prototype by introducing a help guide ( Figure 5 A and B) and seamlessly integrating it into the first course session. We also revised the method for switching accessibility format features ( Figure 5 C and D). Furthermore, we increased the font size on multiple screens and improved navigation by implementing additional back buttons for a smoother user experience.

Results of Usability Testing

We had 10 cancer survivors with disabilities (9 female, 1 male; 9 White or Caucasian, 1 Black or African American) who completed usability testing. The average age of the participants was 59 years. Usability scores show that participants had an overall positive reception to the design of the prototype. We had an average SUS score of 81; our prototype’s usability is therefore considered good to excellent with a grade of an A and in the top 10%.

We assessed participants’ satisfaction levels and the time taken to complete each task. The average scores for these 2 measurements are presented in Table 1 . Generally, participants exhibited high satisfaction rates; however, lower numbers were observed for task 2 (finding the eye icon to change the accessibility format), task 7 (creating a post in the Community Forum), and task 8 (using the filter in C2P).

In addition, we evaluated the effectiveness of the app design by categorizing participants’ task completion into 3 groups: completed quickly (C), completed with a little more time (L), or required assistance to complete the task (H). Overall, most participants completed their tasks without any issues, with only 17 of 80 cases (21%) needing help to complete them (see Figure 6 ). During task 1, a slight learning curve was observed as some participants had difficulty locating the correct module, leading to the need for assistance in completing the task. However, this issue was not prevalent in subsequent tasks. Task 2 revealed that some participants encountered challenges while switching the card format to text view using the eye symbol, as they had trouble locating the button. In task 4, some participants faced difficulties clicking on the correct resource within the Library as directed in the learning module. For tasks 7 and 8, several participants struggled to navigate both the Community and C2P sections because certain text and icons were too small or unclear in their function, leading to confusion on what to do.

Likewise, while analyzing efficiency based on the number of misclicks per task, tasks 7 and 8 exhibited notably higher misclick rates ( Table 2 ). The table also presents the actual time taken per task, with task 1 showing higher time than the other tasks. As mentioned earlier, task 1 had a learning curve, but it also involved reading the most cards (15 cards) as we integrated the help guide into the first course session. Therefore, this finding is expected given the additional content to review in task 1.

The prototype’s help guide received a positive response, with 8 of 10 participants (80%) rating it as very helpful or extremely helpful. Similarly, 8 of 10 participants (80%) reported finding the eye symbol (to change the course format) easily. In response to open-ended questions, participants expressed their likes and dislikes of the prototype and its design. Many participants shared positive opinions on the design and content of the modules, finding them helpful and insightful. The video located within one of the modules received positive feedback, with some expressing a desire for additional videos. The purpose of the Community section was well liked as participants enjoyed having a place to freely express themselves with other cancer survivors and appreciated the opportunity for users to support each other. The Library resources were found to be informative and useful, covering a wide range of topics.

Our findings were overwhelmingly positive, supported by quotes from participants (some written and some oral):

I want to see the whole thing work! I know that this is a prototype, but I want to see more!

Great app, it would have been very helpful to me when I was just out [of] treatment.

Even though I'm not very comfortable with technology, and that might be because of my age, … I don't think that this would be difficult for me. I think there'd be a real fast learning curve. I felt good and positive when I realized I had learned something, and I could just click on it now without having to think about it.

I do like the app. I like that I know I’m not alone feeling this way.

These participant quotes reflect their enthusiasm and positive experiences with the app, highlighting its potential benefits and ease of use.

On the basis of our session observations and participants’ feedback on areas for improvement, we identified several issues:

• Accessibility concerns, including small font sizes and icons, particularly with the navigation arrows on cards, the top navigation bar, and the eye icon.
• Some participants experienced confusion while navigating the Community page when creating new posts.
• Difficulty in locating and using the filter option within the C2P page.
• Participants expressed a desire for an easy way to return to the help guide.
• Feedback indicated a preference for changing the robotic voices used in the audio format for the modules. The prototype used Google US English from voicegenerator.io, but the intention is to have a real person’s voice in future implementations.

Addressing these areas for improvement can further enhance the app’s usability and user experience.

Modifications Based on Usability Testing

On the basis of the findings from usability testing, we made several modifications to the prototype. To enhance usability, we increased the sizes of navigation icons, the eye icon, arrows within cards, and the top navigation bar. Throughout the application, we enlarged or bolded fonts for easier reading, including the “create new post” button in the Community section. We redesigned the layout of the Community Forum, increasing text and margins to achieve a cleaner and more concise design. Additionally, we revamped the subscribe button to reduce confusion (see Figures 7 and 8 ). To improve accessibility, we enlarged the C2P filter. Finally, we added a convenient way to return to the help guide by including it in the hamburger menu icon on the main page. These changes aim to enhance user experience and address the identified issues during usability testing.

Principal Findings

Cancer and its treatments can lead to long-term disabilities, significantly impacting a survivor’s overall quality of life [ 10 ]. Unfortunately, postcancer treatment resources are often limited, further exacerbating the challenges faced by survivors [ 49 , 50 ]. To address this, we developed a high-fidelity prototype for an mHealth app called WeCanManage, aimed at empowering cancer survivors with disabilities to effectively self-manage the long-term effects of cancer treatment. Through conducting the heuristic evaluation, valuable improvements were made, including the incorporation of a helpful guide and the enhancement of accessibility formatting options, ultimately enhancing the overall user experience of the app.

In usability testing, we engaged cancer survivors with disabilities, using multiple methods such as task completion, think aloud strategies, SUS, perceived task satisfaction, and open-ended questions. These methods have been extensively used to evaluate various applications, with the SUS being one of the commonly used questionnaires [ 21 ]. The results of usability testing were overwhelmingly positive, with cancer survivors expressing appreciation for the app’s content, features, and design. The prototype achieved an impressive SUS score of 81, ranking it in the top 10% of scores and earning an A grade. Moreover, participants reported high satisfaction levels and efficiency, with average scores of 6.2 and 6.1 (out of 7), respectively. Conducting usability testing enabled us to thoroughly assess the app’s overall effectiveness, efficiency, satisfaction, and usability. We were able to identify areas for improvement, particularly in terms of accessibility. The insights gained from this testing process have allowed us to refine and enhance the app, ensuring a positive user experience for cancer survivors with disabilities.

In a study by Fuller-Tyszkiewicz et al [ 24 ], end users rated an mHealth prototype higher in usability and reported a more positive experience than clinical experts. Interestingly, users did not share the same concerns about the amount and layout of content presented as the experts had anticipated [ 24 ]. This discrepancy underscores the significance of testing potential users to tailor the app to their specific needs and preferences. While expert opinions (whether clinical or in design) are valuable, evaluating an app on actual users is ideal.

Implications for Designers and Researchers

One of our primary findings is the importance of accessibility when designing applications for cancer survivors. Our app was specifically designed for cancer survivors with disabilities, and as such, we incorporated customized options to switch the learning style. Users could choose between clicking through content cards and accessing audio or text-only views. This flexibility proved to be helpful, particularly for participants with cognitive issues like “chemo brain,” who found it easier to navigate the audio versions of the course sessions. However, during testing, we identified other accessibility concerns related to font sizes and icons. Some users found them too small to see, click on, and navigate effectively. Addressing these issues is essential to ensure an inclusive and user-friendly experience for all app users.

The importance of having a help feature was revealed during heuristic evaluation, and through usability testing, we learned that users expressed a desire for a convenient way to return to the help guide. In response to this feedback, we have now incorporated the option to access the help guide directly from our main menu.

One comment expressed by many of our participants was how lonely the experience of a cancer survivor is. Consistent with findings from other studies that highlight the significance of social features in mHealth apps [ 51 ], participants expressed their appreciation for the Community Forum and C2P sections. These features provide a valuable opportunity for them to connect with others facing similar situations, fostering a sense of community and support. Additionally, participants reported that reading the content in the course sessions made them realize that their experiences were shared by others, helping them feel less isolated and reassured that they were not alone in their journey. When asked what they liked about the app, one participant wrote the following: “The information, reliable and trustworthy, … and the realization that I am not alone.”

Limitations

Our aim was to achieve a minimum of 12 participants for usability testing, as SUS results are ideally derived from 12 or more participants [ 52 , 53 ]. However, we encountered challenges in recruitment because of technical difficulties, such as some participants lacking access to a laptop or facing issues with Zoom and screen sharing, leading to incomplete usability testing. Additionally, recruitment was hindered by our specific inclusion criteria, which focused on individuals who identified as having a disability. These challenges impacted our ability to reach the desired number of participants for the usability testing phase. Nevertheless, it is worth noting that according to Nielsen [ 54 ], 5 participants are typically adequate for identifying usability problems. Thus, we can reasonably infer that our processes have successfully identified the majority of issues, providing a level of confidence in the validity of our findings despite the lower number of participants in the usability testing phase. Additionally, it is worth mentioning that several studies evaluating mHealth prototypes have used the SUS with fewer than 12 participants [ 29 , 31 , 37 ]. We encountered instances where some participants experienced lingering effects of cancer and its treatment, but they did not self-identify as having a disability, resulting in their exclusion from usability testing. This finding has important implications for the implementation and adoption of WeCanManage, ensuring that cancer survivors experiencing disabling aftereffects can fully benefit from the tool and appreciate its relevance and value in their daily lives and experiences.

Furthermore, as this was a prototype, not all features were fully implemented (eg, the ability to create a post on the forum or direct message a user was mimicked), which may have caused some participants to encounter difficulties in the Community section of the prototype. In addition, during usability testing, participants expressed concerns regarding text and icon sizes. It is important to note that the testing was conducted over Zoom using computers (not mobile devices), and the prototype’s size (matching that of a phone) might have posed challenges during interaction, which may not be representative of the real application’s experience. Finally, it is worth noting that the age of participants and their level of comfort with technology might have influenced their overall experience [ 55 ]. Nevertheless, because these individuals constitute our target user base, it remains essential for us to maintain the app’s usability and accessibility to meet their needs.

Conclusions

When creating an mHealth app, it is crucial to evaluate it with the target users in mind, in our case, cancer survivors with disabilities. Usability testing allowed us to identify the design’s strengths and areas requiring improvement. The WeCanManage prototype achieved a SUS score of 81, placing it in the top 10% of scores. Our future work will involve feasibility testing of an implemented web-based mobile app of WeCanManage. This will enable us to further refine the application and ensure that it meets the needs and preferences of our target users, enhancing its overall usability and impact.

Acknowledgments

This research was supported by the National Cancer Institute of the National Institutes of Health (U54CA202995, U54CA202997, and U54CA203000). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Additional support was provided by Northeastern Illinois University’s Student Center for Science Engagement, Committee on Organized Research, and College of Business and Technology. The authors would also like to acknowledge contributions from the following team members: Melissa Delgado, Daniela Raudales Reyes, Elizabeth Jarvis, Sabrina Cadena, and Bruriah Horowitz.

Conflicts of Interest

None declared.

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Abbreviations

Edited by C Jacob; submitted 03.08.23; peer-reviewed by E Baker, C Baxter; comments to author 06.09.23; revised version received 07.11.23; accepted 11.11.23; published 02.04.24.

©Rachel F Adler, Kevin Baez, Paulina Morales, Jocelyn Sotelo, David Victorson, Susan Magasi. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 02.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Human Factors, is properly cited. The complete bibliographic information, a link to the original publication on https://humanfactors.jmir.org, as well as this copyright and license information must be included.

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Difficulties and Problem-Solving Strategies in Wayfinding Among Adults With Cognitive Disabilities: A Look at the Bigger Picture

Romain delgrange.

1 LAPEA, Univ. Gustave Eiffel, IFSTTAR, Versailles, France

2 Université de Paris, LAPEA, Boulogne-Billancourt, France

Jean-Marie Burkhardt

Valérie gyselinck, associated data.

The datasets generated for this study are available on request to the corresponding author.

Many people with cognitive disabilities avoid outside activities, apparently for fear of getting lost. However, little is known about the nature of the difficulties encountered and the ways in which these individuals deal with them. None of the few studies on wayfinding by people with cognitive disabilities have explored the various specific difficulties they meet in everyday life. Using both a qualitative and quantitative methodology, this study aimed at profiling the types of difficulties encountered in urban mobility and the associated problem-solving strategies. In order to provide more direct evidence from the field, we conducted semi-structured interviews using the critical incident technique ( Flanagan, 1954 ). Among the 66 participants interviewed, 44 had cognitive disabilities and 22 were matched controls. The analysis of the transcripts showed in particular an overall reduced autonomy in problem-solving strategies for people with a cognitive disability. The multiple correspondence analysis highlighted three main types of complex situations, covering a comprehensive range of complex situations that are met in everyday life by these individuals. Results also indicated that people with cognitive disabilities request assistance from another person more frequently when a complex event occurs. These situations are discussed as potential cues for improvements in navigational aids. Conclusions and perspectives are provided to improve wayfinding among people with cognitive disabilities.

Introduction

Wayfinding as a cognitive process.

Getting around the city is the first step in many of our daily activities, whether they are related to work or leisure. This activity is therefore fundamental for autonomy as well as for social integration and community access ( Doig et al., 2001 ; Sohlberg et al., 2007 ). Still, finding one’s way in the environment involves more than just movement ( Montello, 2017 ). Apart from controlled locomotion, spatial navigation relies on a set of cognitive processes referred to as “wayfinding” ( Montello, 2005 , 2017 ; Wiener et al., 2009 ). In his early work on urban architecture, Lynch (1960) coined the term wayfinding to describe the use of environmental cues in order to move toward a destination, considering the physical properties of cities that allow a traveler to find their way. During the following two decades, the scope of research expanded toward a more cognitive perspective, centered on human processes rather than on the environment. Wayfinding has been defined as the cognitive process of finding and following a path that links an origin to a destination ( Golledge, 1992 ). It is considered as a spatial problem solving that depends on the construction of mental models, consisting in internal representations of distinct scales of the environment, from a landmark, first-person perspective, to a comprehensive, “bird’s-eye” view ( Tolman, 1948 ; Siegel and White, 1975 ; Johnson-Laird, 1980 ). The use of these representations along an itinerary relies heavily on mnesic and executive functions, in order to retain spatial information and perform the adequate actions that govern movement ( Vandenberg, 2016 ; Meneghetti et al., 2017 ). It involves four main cognitive components ( Vandenberg, 2016 ): decision making, orientation, path integration, and closure. The first step, decision-making, implies that several factors have been taken into account, such as selecting the adequate path between the origin and the destination of the trip ( Gärling et al., 1986 ; Golledge, 1995 ). Decision making also takes place during the trip: while planning and moving through an itinerary, people use their internal representations of the environment to automatically choose and follow a path ( Richter, 2007 ; Brunyé et al., 2010 ). A second cognitive resource that supports the use of mental models to find one’s way is orientation, the capability of knowing where an individual finds themselves in the environment, in relation to the surroundings ( Vandenberg, 2016 ). A third process deals with updating orientation while moving through the environment, keeping track of the motion and continually acquiring information on the environment to maintain the knowledge of one’s location in space ( Gärling et al., 1986 ). Finally, the last step and fourth component is closure ( Vandenberg, 2016 ), i.e., realizing that one has reached the intended destination.

Considering the central role of cognition in wayfinding, any condition that affects either internal spatial representations or cognitive processes can result in difficulties in finding one’s way ( Postma and van der Ham, 2016 ). Depending on how challenging the environment is (e.g., noisy or dark), one’s current state of health, level of fatigue or stress, nobody is “permanently unimpaired” ( Arthur and Passini, 2002 ). This becomes even truer in the case of permanent cognitive disabilities resulting from strokes or head injuries.

What We Know About Wayfinding in People With Cognitive Disabilities

For a long time, neuropsychology has documented difficulties in spatial representations resulting from cognitive disabilities. These studies make clear the impairments as well as their neuroanatomical correlates, and classify several types of disorientation (for reviews, see Aguirre and D’Esposito, 1999 ; Claessen and van der Ham, 2017 ). Studies in cognitive psychology have also highlighted the importance of working memory in spatial representations using interference paradigms ( Gyselinck et al., 2009 ) and have shown how cognitive aging could impair these processes ( Meneghetti et al., 2012 ).

Only a few studies gathered evidence of specific difficulties in wayfinding among people with cognitive disabilities, based on both interviews and experimental settings. Results showed that people with cognitive disabilities appear to lack the ability to link landmarks and paths in a bird’s-eye view of their everyday environment ( Antonakos, 2004 ), and that they show little independence when facing a complex situation ( Lemoncello et al., 2010 ). In particular, when observing problem-solving situations, Lemoncello et al. (2010) showed that people without cognitive disabilities mostly resolve spatial problems independently by either guessing or walking a little further to look for a landmark. Conversely, people with cognitive disabilities ask the accompanying experimenter for help, or suggest potential solutions that are generally judged to be vague by the experimenter. These findings seem consistent with these individuals’ lifestyle: based on group interviews, Sohlberg et al. (2005) showed that they avoid going outside for fear of getting lost, restricting themselves mostly to routine outside trips.

Up to now, the characteristics of the difficulties encountered by people with cognitive disabilities when traveling outside in their daily activities have remained largely unexplored ( Meissonnier, 2016 ; van der Ham and Claessen, 2016 ; Nakamura and Ooie, 2017 ). While “getting lost” appears to be a major factor of avoidance of getting around the city ( Sohlberg et al., 2005 ), one can only conjecture on the nature of this problem, its causes and consequences, and the specificity these characteristics represent for the target population in comparison to the general public. Moreover, not much is known about the problem-solving strategies these people actually implement in their everyday life when they face complex situations, whether these consist in getting lost or not.

The scarcity of research data on this topic seems to be caused by a difficulty in recruiting and categorizing participants with cognitive disabilities, some matters directly discussed by most authors ( Dawson and Chipman, 1995 ; Sohlberg et al., 2005 , 2007 ; Lloyd et al., 2009 ; Cho et al., 2017 ). For the last 40 years, very few navigational aids have been designed to meet human spatial cognition needs and functioning ( Grison and Gyselinck, 2019 ). This is even truer for people with cognitive disabilities ( Sohlberg et al., 2005 , 2007 ), in part due to the lack of information on the nature of the difficulties encountered by the target population in everyday life.

Objectives of the Study

The present study was designed to explore representative everyday situations in order to develop a broader understanding of the effects that cognitive disabilities can have on all the components involved in completing an itinerary. This means not only taking the right decision at a crossroads but also coping with an unexpected delay in transport, getting along with other pedestrians or simply recognizing a building as the destination of the trip ( Vandenberg, 2016 ). We therefore collected the existing experience of complex wayfinding events among people with and without cognitive disabilities. In particular, we explored the problem-solving strategies used when a complex or an unexpected event occurs. We address the potential specificity of the difficulties met by people with cognitive disabilities by comparing them with a matched control group. Based on these results, we provide some insights that should prove helpful in designing better adapted navigational aids. Our results could also clarify the features of ecological wayfinding situations experienced as complex by people with disabilities, opening up avenues for future research.

Note that the difficulty for people with cognitive disabilities in recalling and articulating specific experiences and feelings ( Paterson and Scott-Findlay, 2002 ) usually prevents the use of semi-directed interviews. Still, the documented benefits, such as avoidance of bias and facilitation in user participation, have led some researchers to advocate such investigations, provided certain precautions are taken ( Heal and Sigelman, 1995 ; Cambridge and Forrester-Jones, 2003 ; Gilbert, 2004 ). Moreover, results have shown that interviewing the relatives of these people is not sufficiently reliable when investigating outside activities, suggesting that the target population itself should be included rather than proxies ( Cusick et al., 2000 ).

Thus, to address our research questions, individual semi-directed interviews were conducted based on the “critical incident technique” ( Flanagan, 1954 ; see section “Materials and Methods”) in order to perform a step-by-step exploration of representative, detailed everyday-life wayfinding experiences. This technique allows the problematic aspects of complex situations to be rapidly highlighted and offers a way to investigate activities that would otherwise be difficult to observe in laboratory settings. Initially developed to gather data with task experts in order to identify critical competencies for their job, the aim of the technique is to avoid the collection of general thoughts and stereotypes about a theme and to favor verbal reports on specific experienced situations recognized by participants to be significant for the theme under investigation. The features of the critical incident technique make it particularly relevant for the study of complex situations such as getting around a city, and for urban mobility in general ( Corneloup and Burkhardt, 2016 ; Grison et al., 2016 ). Furthermore, a questionnaire on orientation and spatial abilities ( Pazzaglia et al., 2000 ) was administered to characterize the participants of both groups.

As to our knowledge, this is the first research study on wayfinding to use the critical incident technique with people with cognitive disabilities, we adopted an exploratory perspective. We considered every potentially complex situation that the participants recalled, whether they concerned the action of getting lost or an unpleasant trip in a crowded subway. The aim was to determine the most frequent profiles of complex situations, and whether they were associated with a group of participants or not.

Materials and Methods

Participant recruitment.

Two groups of participants were recruited. The experimental group was formed on the basis of the following inclusion criteria: being at least 18 years old at the time of the interview (French legal majority), presenting a legally authenticated cognitive disability, and being able to travel alone in town. Participants had to be stabilized and lived autonomously. An exclusion criterion was the existence of disabilities impacting the visual or motor functions, thereby creating difficulties in mobility possibly unrelated to the cognitive disability itself. Forty-seven volunteers with a cognitive disability and meeting our inclusion criteria came forward to participate in this study. Forty-three came from four partner institutions: 10 participants came from home care services and specialized services for handicapped adults (French “SAMSAH”), and 33 were workers in centers providing care through employment to handicapped adults (French “ESAT”). One participant came from the investigator’s indirect network. Participants from the institutions were initially identified and invited by the professionals to volunteer for the study. It was made clear to them that any participant strictly meeting the inclusion criteria could volunteer, whether they had already expressed a mobility complaint or not. With the exception of aphasia that would prevent interviewing, no additional selection criteria were applied by the professionals. Volunteers were then contacted directly by the experimenter for an appointment at their home or within the institution when possible. Three participants in the experimental group were excluded since they expressed difficulties understanding the questions during the interview, and the session was therefore interrupted.

The control group was formed based on the following inclusion criteria: being at least 18 years old at the time of the study, absence of cognitive impairment and absence of daily use of a car as a driver. The latter criterion was applied because no experimental participant declared driving. Also, the partner institutions for the experimental group were located in the outskirts of the cities, and the participants themselves often lived in the suburbs. Therefore, control participants were recruited among companies based outside the city center, and had to use different types of transport (mainly trains and buses) every day, in order to match the environmental context of the experimental group.

Sample Characteristics

The experimental group included 44 participants (28 men, 16 women). The mean age was 38.91 years (Minimum = 21, Maximum = 81, SD = 13.50). Amongst them, five participants suffered from the after-effects of strokes (of whom two had had two strokes), nine from traumatic head injury, one from a brain lesion after surgical tumor removal, two were epileptic, four had developmental cognitive disabilities and 23 suffered from cognitive disabilities of unspecified etiologies.

While cognitive disabilities, like motor disabilities, can refer to a wide variety of difficulties, they cannot be specified in a “standardized” way either by a device (e.g., a wheelchair, crutches) or a functional disability (e.g., blindness). As documented in the neurological literature, there are potentially as many disabilities as lesions. More than half of the participants in our study suffered from cognitive disabilities of unspecified etiologies, suggesting pathogenesis heterogeneity. As we chose to focus on complex events in real life, we selected people who traveled autonomously for their everyday activities. Therefore, our participants were not under medical care. Furthermore, most of them did not have access to their neuropsychological and medical specifics. We therefore used the following two inclusion criteria: the stability of the disability, and the legal authentication of cognitive disabilities according to the 2005 disability policy reform. The authentication procedure consists in successive medical examinations. All partner institutions catered only for people who had strictly complied with this procedure.

The control group included 22 participants (8 men, 14 women) meeting the criteria who volunteered to participate, with a mean age of 37.45 years (Minimum = 21, Maximum = 61, SD = 14.39).

Design and Procedure

The experimenter first presented the study to the participant in accordance with the content of the information letter. When all the participant’s questions had been addressed, they were asked to sign the consent form. For the participants contacted by telephone, a first call was made to present the study; the information letter and the consent form were then sent by mail. Once the consent form had been read and signed, the interview call was made.

Semi-Structured Interview Using the Critical Incident Technique

The semi-structured interview was based on the critical incident technique ( Flanagan, 1954 ). A “critical incident” is a situation specific in time and geographically localized, resulting in either a satisfactory or an unsatisfactory experience for the participant. The critical incident technique consists in a recall of these events guided step-by-step by the investigator. Each interview was audio recorded and then transcribed. The direction of the interview led the participants to describe each step of the complex situation from the general proposition “ Think of a specific moment that you experienced as complex when you moved around the city during the last few months. It can be a moment that was either pleasant or unpleasant in the end. ” The questions asked by the experimenter to guide the direction of the interview focused successively on the step-by-step process of the event, on the feelings experienced by the participant, on the participant’s reactions and strategies, and on whether the participant had learnt anything from the situation (using the question “ If you were in the same situation today, would you do the same? ”).

When a description was incomplete, the investigator prompted the participant to elaborate using interview techniques (e.g., summarizations, repetitions of keywords, pauses, nods). To complete a description, the investigator also asked the participant if they would judge the situation overall positively or negatively. When the description of one situation was over, the experimenter asked if the participant could think of another complex situation by reiterating the initial general proposition. Then, a second event was detailed in the same way. When the participant declared that they could not remember any other complex event, the investigator summarized all the situations that had been previously described by the participant in order to make sure they had not forgotten anything. The interview was concluded when the participant stated that they did not remember any other complex event. The average duration of the interview was approximately 20 min (Minimum = 3, Maximum = 58, SD = 10).

Questionnaires

Two questions about the age and gender of the participant were asked. When a participant from the experimental group was willing and able to define their disability, the experimenter asked one optional question about their etiology (e.g., stroke, head injury). Then a broader question about the participant’s travel behavior was asked to induce them to summarize their daily journeys, recall the types of transportation (i.e., pedestrian, bus, subway, train, tramway, driver or passenger of a car, bike, others) and usual durations of these journeys. This open question served as an ice-breaker for the interview and a confirmation for the investigator that the criterion of autonomous travel was indeed met.

A 16-item questionnaire on orientation and spatial abilities was then completed ( Pazzaglia et al., 2000 ). This questionnaire returns six main scores: “general spatial orientation,” “knowledge and use of compass points,” “survey representation score,” “route representation score,” “landmark representation score,” and “preference toward survey representation over the rest.” The procedure for the completion of this questionnaire was the same for both groups. The questions were formulated orally by the experimenter, who scored the participant’s answers on the scales. Since the questionnaire had not previously been tested and adapted to people with cognitive disabilities, when a participant from the experimental group expressed a misunderstanding of certain items, the questions were exemplified or reformulated with synonyms in order to be understood by the participants. The goal of these reformulations was to stay as close as possible to the original question, while adapting to the specific cognitive disabilities of the experimental group. The average duration of this questionnaire was approximately 15 min.

Collected Data

The interviews of the experimental group took place between July and November 2017. Nine participants were met at their home, 37 were met within the institutions, and one participant recruited by the indirect network of the experimenter was contacted by phone. The interviews of the control group took place between April and June 2018. Twenty-two participants were met either at their office, at their home or by telephone depending on their availability.

The audio recordings of the 66 semi-structured interviews were entirely transcribed. Two hundred and eighteen critical incidents were obtained from the verbatim, of which 126 were from the experimental group (2.9 incidents per person in average) and 92 from the control group (4.2 incidents per person in average). Situations judged as overall positive (such as an experience deemed satisfactory or a time-saving situation) accounted for 19.9% of the cases in the control group and 5.6% of the cases in the experimental group. It is worth noting that situations triggering positive emotions and overall positive situations do not always match, as situations initially resulting in negative emotions could be evaluated as pleasant by the participants in the end.

Among the 44 questionnaires on orientation and internal spatial representations ( Pazzaglia et al., 2000 ) from the experimental participants, three contained unanswered items. Consequently, these three questionnaires were excluded from this analysis. All 22 questionnaires from the control group were fully completed.

Data Coding

The verbatim of all 218 critical incidents were coded using a grid with six variables to delineate the following dimensions of complex events: cause, type, consequence, emotion, problem-solving strategy, and learning from the situation. The first step in coding the critical incidents remained as close as possible to the story recounted by the participant. The six variables from this first step included between 16 and 48 modalities. In a second step these specific modalities (e.g., “event happened because of rush hour,” “event caused by the crowds of people”) were combined into broader ones (e.g., “event caused by a punctual environmental difficulty”) in order to allow analysis. Finally, we obtained six variables ranging from 7 to 12 modalities detailed in Table 1 . All the variables (except “consequence”) include a modality labeled “other” comprising unique situations that did not fit any other modality.

Variables and modalities determined from the analysis of the interviews, with examples from the verbatim, and overview of the contingency tables for each variable.

Among the variables, some could not be coded, resulting in 14.9% of missing data across the modalities. This missing information is labeled “N/A.”

Statistical Analyses

Univariate analyses.

For each modality of the six variables, the number of occurrences was compiled into contingency tables, as detailed in Table 1 . These frequencies were compared between the two groups using the two-sided Fisher’s exact test, as some frequencies are lower than five. Benjamini–Hochberg multiple comparison correction was applied to all data with a false discovery rate of 0.05 ( Benjamini and Hochberg, 1995 ). An effect of the gender of the participants was also controlled for each variable, and did not seem to occur across all variables.

For each contingency table, we calculated Cramer’s V 2 , an estimator of the magnitude of the association between two categorical variables ( Corroyer and Rouanet, 1994 ). Cramer’s V 2 lies between 0 and 1. We considered the association as strong when V 2 was greater than 0.16 and as weak when V 2 was less than 0.04 ( Wolff and Corroyer, 2004 ). We therefore analyzed the association when V 2 was greater than 0.04.

In the case of significant statistical difference and V 2 greater than 0.04, we calculated the association between each modality of the contingency table. Relative deviations (RDs) measure the associations and are determined on the basis of a comparison between observed and expected frequencies (i.e., those that would have been obtained if there was no association between the two variables) ( Bernard, 2003 ). There is statistical attraction between two modalities when the RD value is positive, and statistical repulsion when it is negative. By convention, only RD with absolute terms greater than 0.25 are retained. All calculated RD are detailed in Table 2 . When a modality occurred more than once but less than five times in total across the two groups, we ignored the strength of association between this modality and the groups of participants.

Overview of the significant relative deviations (RD) between each modality of the critical incident variables and each group.

Multiple Correspondence Analysis (MCA)

A multiple correspondence analysis (MCA) was performed in order to obtain a profile of the main types of existing complex situations and the group most associated with each one. We performed the MCA in accordance with the guidelines and recommendations provided by Le Roux and Rouanet (2010) . This exploratory analysis determines the most significant associations between modalities across all selected variables by determining factorial axes that contribute to the overall variance.

Consistently with our objective of exploring the relationships between features of the complex situations as well as the projection of the group factor on them, we involved all the variables describing the complex events as active variables, while the “group” variable was added as a supplementary variable. Contrary to active variables, a supplementary variable does not contribute to the construction of the axes.

As positive emotions were not mentioned by the experimental group, no correspondence could be observed between the two groups for this emotion. We therefore did not include the “emotion” variable in this analysis. Also, as learning from the event consisted in a reflection after the situation rather than a factual description of the event itself, we excluded this variable from the analysis. We therefore performed the MCA on four active variables describing the characteristics of the situations (cause, type, consequence, problem-solving strategy).

Among the 218 incidents reported by the participants, incidents containing missing values (N/A) across the four selected active variables were excluded, since a missing value cannot correspond to any modality. Incidents containing “other” modalities were also excluded, since “other” covers heterogeneous unique modalities rather than a specific one. Overall positive incidents (corresponding to pleasant experiences or time saving situations) were excluded as a result of the absence of associated problem-solving strategies. In the end, the MCA was performed on 106 critical incidents (63 out of 126 from the experimental group, 43 out of 92 from the control group). In accordance with the requirements of this analysis, previously determined modalities for each variable were combined to reduce the number of modalities per active variable. These “broader” modalities used for the MCA are detailed in Table 3 .

Combined modalities obtained from the preliminary univariate analyses and used for the Multiple Correspondence Analysis.

The contribution of a modality to a factorial axis determines its coordinate on this axis, therefore allowing for a graphical representation of the MCA. The modalities that frequently appear together in the stories of the participants are graphically close to each other.

The interpretation of an axis is permitted by selecting the categories whose contributions exceeded the “baseline criterion,” which is determined by dividing 100 by the total number of active modalities included in the MCA. As detailed in Table 3 , we included the 13 modalities of the incidents, therefore the baseline criterion we used equals 7.69%.

Univariate Analyses: Describing the Complex Situations Step by Step in the Two Groups

Spatial abilities and events recall.

The Mann-Whitney test indicated that the control group recalled significantly more complex events (Median = 4) than the experimental group (Median = 3) ( U = 247, p < 0.01). The events in the control group also appear to be more frequently judged positive than in the experimental group when comparing the two groups using a Chi-square test of independence [χ 2 (1) = 8.95, p < 0.01].

A t -test was performed on the six scores provided by the questionnaire on spatial abilities and did not indicate any significant difference between the two groups.

Differences in Event Types and Problem-Solving Strategies Between Groups

The types of events differed significantly between the two groups ( p < 0.001). As a strong association between variables was found (V 2 = 0.16), the associations between modalities (RD) were analyzed. There were four statistical attractions and three statistical repulsions in the experimental group. Compared to the control group, people with cognitive disabilities more frequently encountered situations centered on being lost, in conflict with another person, in a harmful situation or in a situation involving a transport schedule problem, either early or delayed. Less frequently than the control group, they found themselves on an unwanted or unusual route, suffered the consequences of a mistake made by another person, or met an obstacle on their route.

The problem-solving strategies implemented differed significantly between the two groups ( p < 0.05). As a moderately strong association between variables was found (V 2 = 0.09), the associations between modalities (RD) were analyzed. There was one statistical attraction and two statistical repulsions for the experimental group. Compared to the control group, people with cognitive disabilities more frequently chose to request assistance from another person, either a bystander or friend. Less frequently than the control group, they chose to change their current route for an alternative route, or stop to plan the rest of their trip. One statistical attraction for the control group was its relationship to the modality “other.”

Comparisons for the causes and consequences of the situations between the two groups returned no statistical significances. V 2 and RD are therefore not discussed here.

Differences in Emotions and Learning Between Groups

The emotions generated by the events differed significantly between the two groups ( p < 0.01). As the association between variables was moderately strong (V 2 = 0.10), the associations between modalities (RD) were analyzed. There were two statistical attractions and one statistical repulsion for the experimental group. Compared to the control group, people with cognitive disabilities more frequently experienced emotions of fear and sadness when in a complex or unexpected situation. Less frequently than the control group, they experienced joy.

A comparison of the lessons learnt from the events by the two groups was statistically significant ( p < 0.01). The association between variables was strong (V 2 = 0.13), enabling the associations between modalities (RD) to be analyzed. There were three statistical attractions and one statistical repulsion for the experimental group. Compared to the control group, people with cognitive disabilities anticipated more frequently that if they were to encounter the same situation, they would request assistance, be more attentive or give up and not attempt the journey. Less frequently than the control group, they anticipated planning during the complex situation.

Multivariate Analysis: The Main Profiles of Complex Situations

Based on the decrease in the eigenvalues of the MCA, we considered the first two factorial axes for our analysis. They account for 44.55% of the total variance (axis 1 accounting for 27.34% and axis 2 for 17.21%). The contributions of each active modality are detailed in Table 4 . The weight of the two modalities and the coordinates for the supplementary “group” variable are presented in Table 5 . The graphical representation of the MCA is depicted in Figure 1 .

Contribution of each modality of the Multiple Correspondence Analysis to each axis; the columns “left,” “right,” or “top,” “bottom” refer to their coordinates.

Supplementary “group” variable’s weight and coordinates.

Graphical representation of the Multiple Correspondence Analysis. The coordinates of each modality are determined by its contributions to both axes. The modalities that frequently appear together in the stories of the participants are graphically close to one another. The contribution of each axis to the total variance is indicated in parentheses. Axis 1 (horizontal) opposes events relative to the individuals and events that arise in specific contexts and environments, independently of the individual. Axis 2 (vertical) opposes mainly the experimental and the control groups, with events relative to the control group being dealt with in a more autonomous manner and events relative to the experimental group relying more on the help of another person. The group variable, in italics, is used as a supplementary variable and therefore does not contribute to the overall variance.

On the basis of the baseline criterion (7.69%) and the contribution of each modality, we used five modalities for the interpretation of axis 1 (“internal” cause, “mistake” and “unpleasant event” types, “discomfort” consequence, and “passivity” problem-solving strategy). Seven modalities were used for the interpretation of axis 2 (“contextual” cause, “obstruction,” “transport problem” and “unpleasant event” types, “discomfort” consequence, “autonomous action” and “asking someone for help” problem-solving strategies). Axis 1 opposes critical incidents relative to the individuals (“internal” cause, “mistake”) and critical incidents that arise independently from the individual (“unpleasant event,” “discomfort”), leaving a limited margin of maneuver to act on the situation (“passivity”). On the other hand, axis 2 opposes the critical incidents dealt with in an autonomous manner (“autonomous action”), and the critical incidents that require external intervention (“asking someone for help”).

To analyze the supplementary group variable, we used the deviation between the categories’ coordinates on the axes. A deviation between two categories greater than 0.5 is deemed notable ( Le Roux and Rouanet, 2010 ). Axis 1 does not oppose the two groups, as the deviation between their coordinates on this axis is 0.22, as shown in Table 5 . Axis 2 however opposes the two groups, as the deviation between their coordinates is 0.74. Thus, the experimental and control groups are mainly distinguished in regard to the way they deal with the complex situations, either by acting autonomously (for the control group) or asking for help (for the experimental group).

Overall, this MCA returned three main profiles of complex situations. The first profile we can identify is a complex situation resulting from an internal cause (e.g., “I did not pay attention”), which results in a mistake. In these situations, achieving the initial goal of the trip becomes uncertain. This situation is mainly encountered by people with a cognitive disability. A second situation deals with contextual problems, emerging because of particular circumstances mainly due to the action of other people (e.g., public works, transport network). This situation triggers autonomous problem-solving actions in order to resolve it. It is mainly encountered by the control group. Finally, a third type of situation is an unpleasant event, which causes discomfort and leaves the individual in a state of passivity (e.g., bad weather, crowded transportation). This situation is met mainly by the experimental group.

This study aimed to provide an overview of the difficulties actually experienced by people with cognitive disabilities regarding the complex situations they meet while getting around an urban area in their everyday lives. We used the critical incidents technique to identify the characteristics of the complex situations experienced by people with cognitive disabilities, and compared these characteristics to those of the situations encountered by matched controls. We took into account the situation itself as well as the actions implemented to solve a difficulty. Based on the semi-directed interviews, we divided a complex situation into different components, both factual (cause, event type, consequence and problem-solving strategy) and relative to an evaluation made by the participant (emotion triggered by the event, lessons learnt). We analyzed the potential differences between the two groups across these characteristics and determined a profile of the most frequently encountered complex situations. Based on our results, we propose recommendations for future navigational aids and further research on this matter.

Complex Situations Experienced by People With Cognitive Disabilities Are Specific

While the causes of the encountered events appear to share similarities between the two groups, the types of situations encountered differ between people with and without cognitive disabilities. The complex situations met by control participants are mostly related to external events (unwanted route, disruption of the transportation network, having to deal with a mistake made by someone else or meeting a physical obstacle on the route). Conversely, while they also mention external events, participants with cognitive disabilities mostly describe themselves as the main protagonists of the complex situation. More frequently than the control group, they declare being lost and being in conflict with another person as representative complex events that happened to them.

In our study, being lost designates a situation in which the participant declares not knowing where to go anymore. We distinguished this situation from other events such as taking the wrong direction or being on an unwanted route. This representation of the situation of being lost therefore echoes the results of the study carried out by Sohlberg et al. (2005) , which showed that people with cognitive disabilities avoid going outside for fear of getting lost. The present results confirm that being lost is indeed among the complex situations most frequently cited by people with cognitive disabilities. This finding is consistent with the result that taking an unwanted route, therefore running the risk of getting lost, seems to be overall avoided by people with cognitive disabilities in the first place: it is one of the least frequently mentioned events in the experimental group.

Another complex event frequently cited in the experimental group, but not by the control group, is the occurrence of interindividual conflict. These conflicts might thus be seen as a specificity of the experimental group and be related to the disability itself. As many professionals from the partner structures and participants themselves mentioned, the difficulty with cognitive disabilities lies in their absence of visibility. Consequently, other users as well as transport workers may behave with them exactly as with other people without taking their specific situation into account, which causes misunderstandings. Besides, it is well-documented that people with cognitive disabilities tend to experience social difficulties and mood disorders (for reviews, see Morton and Wehman, 1995 ; Carson et al., 2000 ), which could also increase the conflictual potential of a situation.

Interestingly, while they apparently mention similar causes of situations, the two groups mention different types of complex situations. A control participant may be more able to detect a potential change and adapt their route accordingly, therefore declaring not the initial precursor but the new route as problematic, whereas participants with a cognitive disability may experience difficulty in identifying and subsequently adapting their behavior to a new element that is not yet problematic.

Consequences of the Complex Situations: Similarities Across Groups Suggesting Different Traveling Habits

The consequences of the complex situations did not differ between the two groups. Irrespective of the event type, people with and without cognitive disabilities have to make detours, end up being in uncomfortable situations (either physically or emotionally), or have to wait. This is not surprising, as among our sample of 218 critical incidents, 208 deal with situations where people have to go to a specific destination, often at a specific time. Uzan and Wagstaff (2018) proposed five possible categories of motives for an urban journey: physical activity, social activity (e.g., walking around with someone), exploration of an environment, regular route (e.g., going to work), or reaching a place, object or person. The latter two motives comprise more than 95% of the complex situations in our sample. It is worth noting that any consequence of a complex event during this type of journey might therefore disturb the traveler in their activity toward their goal, whatever the event is. These consequences would probably differ for perturbations occurring while doing physical activity outside, for example.

This observation suggests either that participants rarely encounter difficulties when going outside for other reasons than reaching a destination on time, or that they rarely go outside for the other three types of motives. The answer might actually be the latter for the experimental group: as Sohlberg et al. (2005) showed, people with cognitive disabilities avoid outside activities when possible. Therefore, it is not surprising that apart from the journeys that are mandatory (e.g., going to work, to an appointment), they avoid walking around or doing physical or social activity outside. Interestingly, this can be linked to the difference between the two groups regarding the number of complex situations mentioned: participants with cognitive disabilities recall fewer events than control participants. While this may be related to mnesic impairments, it could also be caused by the rarity of their journeys outside, therefore generating fewer complex events.

Different Problems, Different Solutions: People With Cognitive Disabilities Ask for Help While Matched Controls Handle the Situation on Their Own

The problem-solving strategies implemented by the participants reveal an interesting potential for improvements, as they suggest a lesser degree of autonomy for the experimental group. A major distinction lies in the independence of the action taken: while control participants mostly change their route to an alternative one or plan a solution (either on their phone or on a physical map), the only statistical attraction for people with cognitive disabilities is directed toward the request of assistance from another person. This result therefore provides converging evidence with the findings of Lemoncello et al. (2010) who highlighted the use of the same problem-solving strategy in specifically designed situations where people had to follow incomplete instructions at crossroads. The present study is the first to encompass all the problem-solving strategies reported by people with cognitive disabilities in their everyday life. It is also the first study that compares strategies by people with cognitive disabilities with strategies in the control group when facing complex situations. From this comparison, it can be concluded that the request for assistance is the most frequently used strategy by people with cognitive disabilities. The MCA further strengthens this finding: taking into account all modalities across variables, the experimental group shows statistical ties with requesting help from another person, especially when the event is centered on a mistake (which, in our analysis, includes getting lost and mistaking the route). However, as Sohlberg et al. (2007) and Lemoncello et al. (2010) showed, individuals with cognitive disabilities tend to be vague or inaccurate in their request for help, as judged by experimenters as well as transport workers. This problem-solving strategy therefore appears to be ineffectual for this group.

Another interesting result lies in the statistical attraction of the controls toward the “other” types of problem-solving strategies. Across variables, the “other” modalities group unique events or actions that cannot be combined with any other modality. The attraction toward these “other” modalities in the problem-solving strategies could be interpreted as a form of opportunism, characterized by a greater diversity in the solutions implemented by the controls, as they appear to choose unique, unclassifiable solutions more frequently than people with cognitive disabilities.

Subjective Experience of the Complex Situations: Negative Emotions and Reinforcement of the Strategies Already in Place for People With Cognitive Disabilities

The emotions triggered by complex events are unsurprisingly mostly negative for both groups. However, joy is mentioned in the control group only. In comparison, participants with a cognitive disability express mostly fear and sadness. This is congruent with what has been observed with the causes and event types: the negative emotions might be tied to the difficulty evaluating the origin of the situation and anticipating its evolution. Also, people with cognitive disabilities experience fewer situations that they judge positively overall, compared to the control group. While this evaluation of the event is the object of a separate question and is not necessarily related to the emotion actually triggered during the event, this difference between participants is consistent with the absence of joy observed in the experimental group. This finding is interesting from a wayfinding perspective, as it converges with existing evidence in the literature that emotion structures spatial representations ( Storbeck and Maswood, 2016 ; Ruotolo et al., 2019 ). In particular, it has been reported that being in a positive mood and feeling positive emotions enhance spatial working memory by favoring a better retention of spatial information, in comparison to being in a negative mood ( Storbeck and Maswood, 2016 ). Emotion has also been shown to affect spatial representations: participants who see landmarks inducing positive emotions while walking along a virtual route are able to locate the landmarks more accurately on a map afterward, as well as drawing the route, in comparison to participants who see landmarks inducing negative emotions ( Ruotolo et al., 2019 ). These findings have led some researchers to advocate the use of positive emotion to improve wayfinding apps in everyday life, for example by computing instructions and routes based on street segments previously evaluated positively by users to allow for an “emotional” wayfinding ( Gartner, 2012 ; Huang et al., 2014 ). Our results suggest that people with cognitive disabilities, who tend to experience mostly negative emotions when facing an unexpected situation, could also benefit from such a navigational aid proposing routes inducing positive emotions and therefore enhancing spatial working memory and “emotional” wayfinding.

The lessons learnt from the events confirm what is observed with the problem-solving strategies, as a difference emerges between the two groups. People with cognitive disabilities mention more frequently than the control group that they would request assistance, be more attentive or give up and not attempt to make the journey. This strengthens the previous finding that request for assistance seems to be a robust strategy for people with cognitive disabilities. Again, while they mention that they would privilege this action, they cannot plan the action in itself: they do not know in advance at which point of their trip or for what exact reason they would be in need of an outside person. This suggests that for this population, an assistive navigational aid should be available at all times to deal with losing their way at different locations.

The notion of being more attentive is an interesting finding, as it also strengthens the observation that complex situations may indeed emerge from an internal cause. Moreover, this is also congruent with the findings of Lemoncello et al. (2010) : this anticipated change seems to be vague, as the participants cannot know in advance what it will be relevant to pay attention to. This also suggests that the directions provided by a navigational aid adapted to this population should tie directions to specific spatial landmarks in order to facilitate the focus of attention on elements that are relevant to the trip.

Giving up, which is also frequently mentioned by people with cognitive disabilities, further confirms the need for an improvement in the mobility of this population. This population, which already avoids most outside activities, contemplates giving up on journeys that are difficult, thereby increasing their difficulty in accessing leisure and social activities.

Overall Remarks on the Complex Situations’ Profiles: Not All Events Call for a Wayfinding Improvement

The results of the MCA suggest that not all complex situations can be resolved with a navigational aid, as they do not systematically deal with the act of finding one’s way. A frequent profile of complex situations for people with cognitive disabilities concerns an unpleasant event, which causes discomfort, either physical or emotional (e.g., congested transportation, weather conditions). In these situations, the associated problem-solving strategy is mostly passivity, as people wait for the situation to end or simply follow the instructions given by transport workers. This complex situation profile does not seem to hint at a particular solution for people with cognitive disabilities in a wayfinding aid perspective, as they cope with unpleasant conditions rather than spatial cognition. However, while this profile of events does not tie in with decision making, orientation, path integration or closure ( Vandenberg, 2016 ), it can directly impact the following of a path from an origin to a destination ( Golledge, 1992 ). This is particularly true in the case of interindividual conflicts, an event type that occurs especially frequently for people with cognitive disabilities. Our results highlight the multifaceted nature of real-life wayfinding activities, which not only depend on the actual properties of the environment but also on non-spatial properties including the preferences, abilities and beliefs of an individual ( Montello, 2017 ). This emphasizes the interest of taking into account mobility as a whole when discussing wayfinding for specific populations, as an event inducing an actual and recurring difficulty to reach a destination can arise separately from a disability involving the main components of Vandenberg’s (2016) model.

This study examined the specific difficulties experienced by people with cognitive disabilities during wayfinding. Our perspective was exploratory. Our results show that people with cognitive disabilities encounter specific complex events and especially, that they get lost more frequently. Moreover, they rely more on the help of another person.

Some limitations of our study should be noted. First, as already mentioned, the pathogenesis heterogeneity among our experimental participants might weaken the generalizability of our results, and therefore calls for further studies on this matter. A second limitation directly concerns the cognitive disabilities themselves. Participants with a disability mention significantly fewer events than control participants, a difference that could be linked to the rarity of their urban journeys. Another possible explanation could be that participants with cognitive disabilities indeed suffer from memory impairments. These impairments could thus be a limitation for the validity of the data collected from our interviews.

Still, we can sketch out some recommendations for a navigational aid. As our results tie in with Vandenberg’s (2016) cognitive model of wayfinding on several levels, more specific suggestions toward an adapted navigational aid can be proposed. Vandenberg (2016) details four cognitive components of wayfinding: decision making, orientation, path integration, and closure. The analyses of the interviews highlight the relationships between these four components and two variables of complex situations: the event type and the problem-solving strategy implemented by the participant.

The event of “being lost” is among the most frequently encountered by people with cognitive disabilities, and can concern orientation, path integration and closure. One could argue that this situation is already taken into account by existing navigational aids. However, to solve such complex situations, most of the existing solutions provide exclusively bird’s-eye views and information ( Siegel and White, 1975 ) which do not meet human needs well as they deal with the most complex levels of spatial representations ( Golledge, 1991 ; Grison and Gyselinck, 2019 ). These solutions might therefore not be sufficiently helpful for people with cognitive disabilities, since they encounter difficulties mostly based on orientation, path integration or closure when they get lost. The provision of less complex spatial information, such as that based on landmarks, could therefore be considered as more adapted to help this population. Moreover, as “being lost” also refers to situations where individuals do not recognize their destination, obstructing the “closure” part of wayfinding, the analyses of the interviews suggest that an adapted aid should also be able to ease this last step of the journey by describing the destination, either verbally or by showing a picture, in order to make it recognizable by the user.

The problem-solving strategy variable can be directly tied in with the decision-making part of wayfinding. The finding that the most frequently used problem-solving strategy by people with cognitive disabilities is to ask another person for assistance especially hints at a potential improvement for these navigational aids. While people with cognitive disabilities ask someone for help more often than they implement any other strategy, and especially when they are facing an obstacle to the goal of their journey, it has been documented that their requests are often vague, making it difficult for the helper to understand the need and provide sufficient help ( Sohlberg et al., 2007 ; Lemoncello et al., 2010 ). Still, a possible explanation for the high frequency of use of this strategy despite its flaws lies in the fact that when prompted to describe a route, most people do not use bird’s-eye view information such as current aids or maps: they rely on landmarks, which are considered as the key components of route descriptions ( Denis, 1997 ). More specifically, people associate an action to a landmark in order to give directions ( Denis et al., 2007 ). This landmark-based level of spatial information may explain the interest of people with cognitive disabilities in this strategy, also indicated by their intention to ask for help again in the future, as shown by the analysis of the variable “learning from the event.” Then again, one could argue that most navigational aids already provide vocal features that could replace information provided by a bystander. However, in current systems, the content of vocal instructions also differs from what a person would actually give. Therefore, an adapted navigational aid should aim at better matching the directions a real person would give and provide instructions linking a landmark to the action to be performed. This would make the aid more relevant to the needs and actual problem-solving strategies of people with cognitive disabilities. Finally, in addition to landmark-based information, considering the negative subjective experience and emotions felt by people with cognitive disabilities, our results are in favor of the use of positive emotions-inducing landmarks in adapted navigational aids, as recommended by several authors, to support a better memorization of spatial information ( Gartner, 2012 ; Huang et al., 2014 ; Ruotolo et al., 2019 ).

This study also suggests perspectives for future research. The analysis of the answers to the questionnaire on spatial abilities ( Pazzaglia et al., 2000 ) does not indicate any difference in general spatial orientation, suggesting both people with and without cognitive disabilities have similar spatial skills. Yet, as has been documented, a cognitive disability can be related to several impairments in spatial representations and wayfinding ( Lemoncello et al., 2010 ; Claessen and van der Ham, 2017 ). Our results provide evidence that people with cognitive disabilities get lost more often than controls. This absence of difference in the questionnaire on spatial abilities therefore does not substantiate the literature, in which most studies have focused only on participants facing prior difficulties in wayfinding. While the present study might indicate an inadequacy of the questionnaire for the target population, or a failure of the questionnaire to detect a difference between people with cognitive disabilities and matched controls, other results suggest a more nuanced picture. Claessen et al. (2017) carried out a study on people with documented cognitive disabilities resulting from strokes. Among their sample of 77 participants, only 33 (43%) actually mentioned difficulties in wayfinding. Moreover, among these 33 people, seven did not show any impairment in internal spatial representations when compared to matched controls over cognitive tests. These data suggest that among the target population, some people do not experience difficulties in wayfinding, and some do not experience difficulties in internal spatial representations. Importantly, these sub-populations may not entirely overlap. Therefore, we cannot rule out that the difficulties observed in wayfinding in the present study do not translate into differences in auto-evaluation of general spatial abilities as measured by the questionnaire. This strengthens the need to supplement quantitative measures by qualitative investigations, allowing deeper understanding of all the dimensions implied in the diverse wayfinding situations encountered by individuals.

Data Availability Statement

Ethics statement.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

RD designed the experiment, recruited subjects, acquired, analyzed and interpreted the data and drafted the manuscript. J-MB designed the experiment, substantially contributed to the interpretation of the data and critically revised the manuscript. VG designed the experiment, substantially contributed to the interpretation of the data and critically revised the manuscript. All authors gave final approval for publication and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Conflict of Interest

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

Acknowledgments

The authors would like to thank the managers and staff of the four partner institutions, and especially Mrs. Charlotte Cardon, Mrs. Sophie Cuingnet, Mrs. Anne-Claude Fraillon-Cohen, and Mrs. Viviane Vayssié for allowing the recruitment of the participants and connecting the investigator and the participants.

Funding. This work was supported by the French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR).

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