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What is logical thinking?

How can you build logical thinking skills, what is logical thinking.

Logical thinking can also be defined as the act of analysing a situation and coming up with a sensible solution. It is similar to critical thinking. Logical thinking uses reasoning skills to objectively study any problem, which helps make a rational conclusion about how to proceed. For example, you are facing a problem in the office, to address that, you use the available facts, you are using logical reasoning skills.

In this write-up, we will explore tips on how you can improve your logical thinking skills and the reasons why logical thinking can help you be a stronger professional.

Now the question arises in our mind, why are logical thinking skills important?

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Logical thinking skills play a very important and necessary role in developing your career because they can help you reason through important decisions, solve problems, generate creative ideas, and set goals. Whether you want to advance your career or have just entered the industry, you will encounter challenges daily that require logical reasoning skills. The stronger your logical thinking skills are, the more easily you will be able to come up with solutions and plans that can benefit you and your workplace.

There are many ways in which you can strengthen logical thinking in your daily work.

Methods that help you in developing your logical thinking skills are :

• Spend time on creative hobbies.
• Practice questioning.
• Socializing with others.
• Learn a new skill.

1. Spending time on creative hobbies

It has been observed that creative hobbies like drawing, painting, writing, or playing music can stimulate the brain and help promote logical thinking. Creative thinking, in a way, naturally develops problem-solving abilities that can help you become a better performer at your workplace.

Let’s talk about one more example, learning a new instrument requires deep thought and concentration. The logical thinking skills that you will gain from the process of learning a new instrument can help you approach your work more intently, developing your ability to solve problems with more flexibility and ease.

In addition to this, creative hobbies also help reduce stress. When your stress levels are manageable, you will have an easier time focusing and making logical decisions wherever required. There are many different ways in which you can handle stress, but developing a creative mind is especially productive and can help you bolster both personal and professional life.

2. Practice questioning

Another best way to strengthen your logical thinking skills is to question things that you typically accept as fact. When you regularly ask the question, it helps you view situations more completely and intricately, allowing you to approach problems at work more logically and creatively.

Asking more and more questions often leads to discoveries about topics you had not considered before, which may encourage you to explore further. This method can be used anywhere, especially at work. Let us take an example of a department at your workplace you are not familiar with. Create a list of questions where you need clarity or understanding. This will help you understand its purpose.

Let us take an example. If you work in the sales-marketing department and want to know more about search engine optimization skills , consider asking someone in that department for an overview to learn more about their current projects and processes. This will help you think more critically about the role you would be taking at work as it relates to that team.

3. Socialize with others

Socializing and building relationships with others help you broaden your perspective, giving you more opportunities to develop your logical thinking skills. When you get to know the point of view of other people, it helps you approach problems at work in a new and different way.

There are many ways in which you can invest time in building relationships. It can be from participating in an activity to simply eating lunch or meeting over coffee together regularly. It is truly said that the more logically you can handle problems at work, the more easily you will be able to advance in your career.

4. Learn a new skill

Learning a new skill can also help in sharpening logical skills.

If you take the opportunity to learn as often as possible, you apply the same level of thinking to your job, making you successful.

For example, suppose you decide to start learning a new coding language. This process will require careful thinking and planning. Practicing every day will help to put you in the mindset of thoughtfully approaching problems at work and will also help you develop a new skill that will help you advance your career.

5. Anticipating the outcome of your decisions

When you are working to strengthen your logical thinking skills, it is helpful for you to consider what impact your decisions might have in the future. The closer you pay attention to the results of your decisions and analyze them, the easier the process will become.

Whenever you come up with a solution to a problem at the workplace, try to think about what the outcome may be. Slowly and eventually, you will find it easier to think of your decisions’ immediate and long-term results. This is an important aspect of logical thinking.

Logical skills can be easily strengthened with daily practice. When you start applying these exercises regularly, and by learn more from professional courses you will observe yourself start to naturally approach everyday decisions at work with a more logical perspective.

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What is Logical Thinking? Meaning, Skills, Examples

What is Logical Thinking?

Logical thinking is a mental cycle that comprises methodically pondering, breaking down, and analyzing the material. A viewpoint requires clear and objective reasoning to arrive at acceptable resolutions or utilize sound judgment. Logical thinking enables people to discern designs, form relationships, and draw reasonable conclusions based on evidence and consistent criteria.

One of the most important aspects of logical thinking is recognizing and comprehending the connections between diverse bits of data or ideas. Perceiving circumstances and logical conclusion links, identifying similitudes and contrasts, and comprehending how diverse components fit together in a legal system are all part of this. People can use logical thinking to thoroughly examine claims, recognize misrepresentations, and distinguish between substantial and erroneous thinking.

Importance of logical thinking

• Problem-solving: Logical thinking is essential for effective problem-solving. It helps individuals break down complex problems into smaller, manageable parts, analyze the underlying causes, and devise logical strategies to reach solutions.
• Decision-making: Logical thinking aids in making informed and rational decisions. By evaluating the available options, considering the potential outcomes, and weighing the pros and cons, individuals can make decisions that are based on reason rather than emotions or biases.
• Critical thinking: Logical thinking is closely tied to critical thinking. It allows individuals to assess information critically, question assumptions, and examine evidence objectively. This enables them to form well-founded opinions and make informed judgments.
• Communication:  Logical thinking plays a crucial role in effective communication. It helps individuals organize their thoughts coherently, present arguments logically, and convey ideas clearly and in structure.
• Learning and education: Logical thinking is a fundamental skill for learning and education. It helps individuals understand new concepts, connect ideas, and engage in analytical thinking. Students can grasp complex subjects and retain information more effectively by applying logical thinking.

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Ways to improve your logical thinking

• Practice puzzles and games: Engaging in puzzles, brainteasers, and logic games can enhance your logical thinking skills. These activities challenge your ability to analyze information, spot patterns, and make logical deductions.
• Seek out diverse perspectives: Exposing yourself to different viewpoints and perspectives can broaden your thinking and help you develop more comprehensive and logical reasoning. Engage in discussions, read diverse materials, and listen to others’ opinions to expand your cognitive horizons.
• Sharpen your analytical skills: Analytical thinking is inextricably tied to logical reasoning. Improve your analytical abilities by breaking difficult situations down into smaller components, finding crucial aspects, and analyzing their linkages.
• Exercise critical thinking: Critical thinking entails challenging assumptions, scrutinizing evidence, and weighing arguments. Sharpen your logical thinking skills by partaking in critical thinking tasks such as article analysis or discussions.
• Use reasoning in everyday situations: Look for opportunities to use logic in your daily life. Before making a choice or solving an issue, take a step back, analyze the facts objectively, and evaluate the logical consequences.

How can you build logical thinking skills?

Building logical thinking skills can be accomplished through various activities and approaches. Here are some ways to develop and strengthen logical thinking abilities.

• Spending time on creative hobbies: Engaging in creative hobbies offers a unique opportunity to develop logical thinking skills. Activities such as painting, writing, or playing musical instruments require planning, organization, and problem-solving. When you paint a picture or write a story, you need to envision the end result and determine the steps to achieve it. This involves logical sequencing, recognizing patterns, and making decisions based on visual or narrative coherence. Creative hobbies foster analytical thinking by encouraging you to experiment, make connections between elements, and find logical solutions to challenges that arise during the creative process. They cultivate a mindset that values logical reasoning and problem-solving in an artistic context.
• Practising questioning: One of the most effective ways to build logical thinking skills is by developing the habit of questioning. By questioning information, assumptions, and conclusions, you engage in critical thinking and analyze the logical validity of statements. This involves asking probing questions such as “How do you know that?”, “What evidence supports this claim?” or “Are there any logical fallacies in this argument?”. By consistently questioning, you train your mind to think critically and evaluate the soundness of reasoning. This skill is applicable to various aspects of life, from evaluating news articles to solving complex problems, and helps you approach situations with a logical and analytical mindset.
• Socializing with others:  Engaging in conversations and social interactions with diverse individuals effectively enhances logical thinking skills. Interacting with others exposes you to different perspectives, ideas, and thought processes. Engaging in debates or group discussions challenges your assumptions, forces you to consider alternative viewpoints, and evaluates arguments logically. Socializing allows you to practice active listening, critically analyze information, and express your thoughts coherently. It broadens your cognitive frontiers, promotes open-mindedness, and aids in the development of the capacity to think rationally while traversing diverse points of view and engaging in constructive discourse.
• Developing a new skill: Learning a new skill, whether coding, chess, or solving puzzles, is a wonderful way to build logical thinking abilities. Understanding concepts, following logical rules or algorithms, and using problem-solving methods are all required for learning a new skill. Learning to code, for instance, involves breaking down complex problems into smaller, manageable steps and using logical reasoning to devise solutions. Similarly, chess requires strategic thinking, evaluating different moves, and anticipating consequences, all of which require logical thinking. By engaging in skill acquisition, you exercise your logical thinking muscles and cultivate a mindset that embraces systematic and analytical approaches to learning and problem-solving.

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Now, let’s explore the steps to think logically

• Identify the problem or question: Clearly define the issue or question that requires logical thinking. Break it down into manageable parts.
• Gather relevant information: Collect all the necessary information and data related to the problem or question. Ensure the information is reliable and unbiased.
• Analyze the information: Examine the collected information critically. Look for patterns, connections, and relationships between different elements.
• Apply logical reasoning: Use logical principles, such as deductive or inductive reasoning, to draw conclusions or develop solutions. Follow logical rules and avoid fallacies.
• Evaluate and revise:  Assess the logical validity of your conclusions or solutions. Look for potential weaknesses or errors in your reasoning. Revise and refine your thinking based on the evaluation.

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Components of logical thinking include

• Rationality: Logical thinking relies on rational thought processes, using reason rather than emotions or biases to reach conclusions.
• Analytical thinking: Logical thinking involves breaking down complex problems or information into smaller parts and examining their relationships and connections.
• Pattern recognition: Recognising patterns and links between diverse parts or information is an important part of logical reasoning.
• Logical reasoning: Using logical concepts like deductive or inductive reasoning to reach logical conclusions or make solid judgments.
• Problem-solving: Logical thinking is essential for effective problem-solving because it allows individuals to analyze issues, establish methods, and arrive at logical answers.
• Critical thinking: Logical thinking and critical thinking are inextricably linked since they both involve analyzing evidence, questioning assumptions, and participating in objective analysis.

Individuals may improve their logical thinking abilities and use them successfully in all facets of life by knowing and developing these components.

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Examples of thinking logically on different occasions

• Making a purchasing decision: When contemplating the purchase of a new electronic item, logical thinking might entail studying several models, comparing their features, reading user reviews, and analyzing their long-term worth. This methodical technique will assist you in making an informed selection based on objective criteria rather than impulsive impulses.
• Workplace problem-solving: A ssume you have a technological problem at work. Identifying the problem, acquiring relevant facts, analyzing probable causes, and testing alternative remedies methodically are all examples of logical reasoning. By following a logical procedure, you may narrow down the problem and develop an effective solution based on data and reasoning.
• Resolving conflicts: In a disagreement with a colleague or friend, logical thinking helps to approach the situation objectively. It involves listening to both sides, analyzing the underlying causes, identifying common ground, and proposing reasoned compromises. By applying logical thinking, you can navigate emotions, focus on facts, and find resolutions that consider multiple perspectives.

Logical thinking is an invaluable skill that aids in making sound decisions, solving problems effectively, and resolving conflicts. It enables individuals to approach various situations with clarity, reason, and critical analysis. By cultivating logical thinking skills through practice and conscious effort, individuals can enhance their cognitive abilities, improve decision-making, and foster constructive interactions in personal and professional settings.

Keerthi Shivakumar

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Frequently Asked Questions (FAQs)

Yes, logical thinking can be learned and developed through practice, critical thinking exercises, and engaging in activities that promote analytical reasoning.

Logical thinking and critical thinking are closely related but not identical. Critical thinking involves evaluating information, questioning assumptions, and assessing arguments, while logical thinking focuses on reasoning and drawing valid conclusions based on evidence and logical principles.

Absolutely. Logical thinking can be applied in creative fields as it helps organize ideas, identify patterns, and make logical connections that enhance the coherence and effectiveness of creative works.

Developing logical thinking skills can be challenging, especially when faced with complex problems or conflicting information. It requires patience, practice, and a willingness to question assumptions and biases.

Logical thinking is fundamental to academic success as it aids in understanding complex concepts, analyzing information critically, and effectively communicating ideas. It enables students to think deeply, make logical arguments, and excel in problem-solving tasks across various disciplines.

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7 Module 7: Thinking, Reasoning, and Problem-Solving

This module is about how a solid working knowledge of psychological principles can help you to think more effectively, so you can succeed in school and life. You might be inclined to believe that—because you have been thinking for as long as you can remember, because you are able to figure out the solution to many problems, because you feel capable of using logic to argue a point, because you can evaluate whether the things you read and hear make sense—you do not need any special training in thinking. But this, of course, is one of the key barriers to helping people think better. If you do not believe that there is anything wrong, why try to fix it?

The human brain is indeed a remarkable thinking machine, capable of amazing, complex, creative, logical thoughts. Why, then, are we telling you that you need to learn how to think? Mainly because one major lesson from cognitive psychology is that these capabilities of the human brain are relatively infrequently realized. Many psychologists believe that people are essentially “cognitive misers.” It is not that we are lazy, but that we have a tendency to expend the least amount of mental effort necessary. Although you may not realize it, it actually takes a great deal of energy to think. Careful, deliberative reasoning and critical thinking are very difficult. Because we seem to be successful without going to the trouble of using these skills well, it feels unnecessary to develop them. As you shall see, however, there are many pitfalls in the cognitive processes described in this module. When people do not devote extra effort to learning and improving reasoning, problem solving, and critical thinking skills, they make many errors.

As is true for memory, if you develop the cognitive skills presented in this module, you will be more successful in school. It is important that you realize, however, that these skills will help you far beyond school, even more so than a good memory will. Although it is somewhat useful to have a good memory, ten years from now no potential employer will care how many questions you got right on multiple choice exams during college. All of them will, however, recognize whether you are a logical, analytical, critical thinker. With these thinking skills, you will be an effective, persuasive communicator and an excellent problem solver.

The module begins by describing different kinds of thought and knowledge, especially conceptual knowledge and critical thinking. An understanding of these differences will be valuable as you progress through school and encounter different assignments that require you to tap into different kinds of knowledge. The second section covers deductive and inductive reasoning, which are processes we use to construct and evaluate strong arguments. They are essential skills to have whenever you are trying to persuade someone (including yourself) of some point, or to respond to someone’s efforts to persuade you. The module ends with a section about problem solving. A solid understanding of the key processes involved in problem solving will help you to handle many daily challenges.

7.1. Different kinds of thought

7.2. Reasoning and Judgment

7.3. Problem Solving

READING WITH PURPOSE

Remember and understand.

By reading and studying Module 7, you should be able to remember and describe:

• Concepts and inferences (7.1)
• Procedural knowledge (7.1)
• Metacognition (7.1)
• Characteristics of critical thinking:  skepticism; identify biases, distortions, omissions, and assumptions; reasoning and problem solving skills  (7.1)
• Reasoning:  deductive reasoning, deductively valid argument, inductive reasoning, inductively strong argument, availability heuristic, representativeness heuristic  (7.2)
• Fixation:  functional fixedness, mental set  (7.3)
• Algorithms, heuristics, and the role of confirmation bias (7.3)
• Effective problem solving sequence (7.3)

By reading and thinking about how the concepts in Module 6 apply to real life, you should be able to:

• Identify which type of knowledge a piece of information is (7.1)
• Recognize examples of deductive and inductive reasoning (7.2)
• Recognize judgments that have probably been influenced by the availability heuristic (7.2)
• Recognize examples of problem solving heuristics and algorithms (7.3)

Analyze, Evaluate, and Create

By reading and thinking about Module 6, participating in classroom activities, and completing out-of-class assignments, you should be able to:

• Use the principles of critical thinking to evaluate information (7.1)
• Explain whether examples of reasoning arguments are deductively valid or inductively strong (7.2)
• Outline how you could try to solve a problem from your life using the effective problem solving sequence (7.3)

7.1. Different kinds of thought and knowledge

• Take a few minutes to write down everything that you know about dogs.
• Do you believe that:
• Psychic ability exists?
• Hypnosis is an altered state of consciousness?
• Magnet therapy is effective for relieving pain?
• Aerobic exercise is an effective treatment for depression?
• UFO’s from outer space have visited earth?

On what do you base your belief or disbelief for the questions above?

Of course, we all know what is meant by the words  think  and  knowledge . You probably also realize that they are not unitary concepts; there are different kinds of thought and knowledge. In this section, let us look at some of these differences. If you are familiar with these different kinds of thought and pay attention to them in your classes, it will help you to focus on the right goals, learn more effectively, and succeed in school. Different assignments and requirements in school call on you to use different kinds of knowledge or thought, so it will be very helpful for you to learn to recognize them (Anderson, et al. 2001).

Factual and conceptual knowledge

Module 5 introduced the idea of declarative memory, which is composed of facts and episodes. If you have ever played a trivia game or watched Jeopardy on TV, you realize that the human brain is able to hold an extraordinary number of facts. Likewise, you realize that each of us has an enormous store of episodes, essentially facts about events that happened in our own lives. It may be difficult to keep that in mind when we are struggling to retrieve one of those facts while taking an exam, however. Part of the problem is that, in contradiction to the advice from Module 5, many students continue to try to memorize course material as a series of unrelated facts (picture a history student simply trying to memorize history as a set of unrelated dates without any coherent story tying them together). Facts in the real world are not random and unorganized, however. It is the way that they are organized that constitutes a second key kind of knowledge, conceptual.

Concepts are nothing more than our mental representations of categories of things in the world. For example, think about dogs. When you do this, you might remember specific facts about dogs, such as they have fur and they bark. You may also recall dogs that you have encountered and picture them in your mind. All of this information (and more) makes up your concept of dog. You can have concepts of simple categories (e.g., triangle), complex categories (e.g., small dogs that sleep all day, eat out of the garbage, and bark at leaves), kinds of people (e.g., psychology professors), events (e.g., birthday parties), and abstract ideas (e.g., justice). Gregory Murphy (2002) refers to concepts as the “glue that holds our mental life together” (p. 1). Very simply, summarizing the world by using concepts is one of the most important cognitive tasks that we do. Our conceptual knowledge  is  our knowledge about the world. Individual concepts are related to each other to form a rich interconnected network of knowledge. For example, think about how the following concepts might be related to each other: dog, pet, play, Frisbee, chew toy, shoe. Or, of more obvious use to you now, how these concepts are related: working memory, long-term memory, declarative memory, procedural memory, and rehearsal? Because our minds have a natural tendency to organize information conceptually, when students try to remember course material as isolated facts, they are working against their strengths.

One last important point about concepts is that they allow you to instantly know a great deal of information about something. For example, if someone hands you a small red object and says, “here is an apple,” they do not have to tell you, “it is something you can eat.” You already know that you can eat it because it is true by virtue of the fact that the object is an apple; this is called drawing an  inference , assuming that something is true on the basis of your previous knowledge (for example, of category membership or of how the world works) or logical reasoning.

Procedural knowledge

Physical skills, such as tying your shoes, doing a cartwheel, and driving a car (or doing all three at the same time, but don’t try this at home) are certainly a kind of knowledge. They are procedural knowledge, the same idea as procedural memory that you saw in Module 5. Mental skills, such as reading, debating, and planning a psychology experiment, are procedural knowledge, as well. In short, procedural knowledge is the knowledge how to do something (Cohen & Eichenbaum, 1993).

Metacognitive knowledge

Floyd used to think that he had a great memory. Now, he has a better memory. Why? Because he finally realized that his memory was not as great as he once thought it was. Because Floyd eventually learned that he often forgets where he put things, he finally developed the habit of putting things in the same place. (Unfortunately, he did not learn this lesson before losing at least 5 watches and a wedding ring.) Because he finally realized that he often forgets to do things, he finally started using the To Do list app on his phone. And so on. Floyd’s insights about the real limitations of his memory have allowed him to remember things that he used to forget.

All of us have knowledge about the way our own minds work. You may know that you have a good memory for people’s names and a poor memory for math formulas. Someone else might realize that they have difficulty remembering to do things, like stopping at the store on the way home. Others still know that they tend to overlook details. This knowledge about our own thinking is actually quite important; it is called metacognitive knowledge, or  metacognition . Like other kinds of thinking skills, it is subject to error. For example, in unpublished research, one of the authors surveyed about 120 General Psychology students on the first day of the term. Among other questions, the students were asked them to predict their grade in the class and report their current Grade Point Average. Two-thirds of the students predicted that their grade in the course would be higher than their GPA. (The reality is that at our college, students tend to earn lower grades in psychology than their overall GPA.) Another example: Students routinely report that they thought they had done well on an exam, only to discover, to their dismay, that they were wrong (more on that important problem in a moment). Both errors reveal a breakdown in metacognition.

The Dunning-Kruger Effect

In general, most college students probably do not study enough. For example, using data from the National Survey of Student Engagement, Fosnacht, McCormack, and Lerma (2018) reported that first-year students at 4-year colleges in the U.S. averaged less than 14 hours per week preparing for classes. The typical suggestion is that you should spend two hours outside of class for every hour in class, or 24 – 30 hours per week for a full-time student. Clearly, students in general are nowhere near that recommended mark. Many observers, including some faculty, believe that this shortfall is a result of students being too busy or lazy. Now, it may be true that many students are too busy, with work and family obligations, for example. Others, are not particularly motivated in school, and therefore might correctly be labeled lazy. A third possible explanation, however, is that some students might not think they need to spend this much time. And this is a matter of metacognition. Consider the scenario that we mentioned above, students thinking they had done well on an exam only to discover that they did not. Justin Kruger and David Dunning examined scenarios very much like this in 1999. Kruger and Dunning gave research participants tests measuring humor, logic, and grammar. Then, they asked the participants to assess their own abilities and test performance in these areas. They found that participants in general tended to overestimate their abilities, already a problem with metacognition. Importantly, the participants who scored the lowest overestimated their abilities the most. Specifically, students who scored in the bottom quarter (averaging in the 12th percentile) thought they had scored in the 62nd percentile. This has become known as the  Dunning-Kruger effect . Many individual faculty members have replicated these results with their own student on their course exams, including the authors of this book. Think about it. Some students who just took an exam and performed poorly believe that they did well before seeing their score. It seems very likely that these are the very same students who stopped studying the night before because they thought they were “done.” Quite simply, it is not just that they did not know the material. They did not know that they did not know the material. That is poor metacognition.

In order to develop good metacognitive skills, you should continually monitor your thinking and seek frequent feedback on the accuracy of your thinking (Medina, Castleberry, & Persky 2017). For example, in classes get in the habit of predicting your exam grades. As soon as possible after taking an exam, try to find out which questions you missed and try to figure out why. If you do this soon enough, you may be able to recall the way it felt when you originally answered the question. Did you feel confident that you had answered the question correctly? Then you have just discovered an opportunity to improve your metacognition. Be on the lookout for that feeling and respond with caution.

concept :  a mental representation of a category of things in the world

Dunning-Kruger effect : individuals who are less competent tend to overestimate their abilities more than individuals who are more competent do

inference : an assumption about the truth of something that is not stated. Inferences come from our prior knowledge and experience, and from logical reasoning

metacognition :  knowledge about one’s own cognitive processes; thinking about your thinking

Critical thinking

One particular kind of knowledge or thinking skill that is related to metacognition is  critical thinking (Chew, 2020). You may have noticed that critical thinking is an objective in many college courses, and thus it could be a legitimate topic to cover in nearly any college course. It is particularly appropriate in psychology, however. As the science of (behavior and) mental processes, psychology is obviously well suited to be the discipline through which you should be introduced to this important way of thinking.

More importantly, there is a particular need to use critical thinking in psychology. We are all, in a way, experts in human behavior and mental processes, having engaged in them literally since birth. Thus, perhaps more than in any other class, students typically approach psychology with very clear ideas and opinions about its subject matter. That is, students already “know” a lot about psychology. The problem is, “it ain’t so much the things we don’t know that get us into trouble. It’s the things we know that just ain’t so” (Ward, quoted in Gilovich 1991). Indeed, many of students’ preconceptions about psychology are just plain wrong. Randolph Smith (2002) wrote a book about critical thinking in psychology called  Challenging Your Preconceptions,  highlighting this fact. On the other hand, many of students’ preconceptions about psychology are just plain right! But wait, how do you know which of your preconceptions are right and which are wrong? And when you come across a research finding or theory in this class that contradicts your preconceptions, what will you do? Will you stick to your original idea, discounting the information from the class? Will you immediately change your mind? Critical thinking can help us sort through this confusing mess.

But what is critical thinking? The goal of critical thinking is simple to state (but extraordinarily difficult to achieve): it is to be right, to draw the correct conclusions, to believe in things that are true and to disbelieve things that are false. We will provide two definitions of critical thinking (or, if you like, one large definition with two distinct parts). First, a more conceptual one: Critical thinking is thinking like a scientist in your everyday life (Schmaltz, Jansen, & Wenckowski, 2017).  Our second definition is more operational; it is simply a list of skills that are essential to be a critical thinker. Critical thinking entails solid reasoning and problem solving skills; skepticism; and an ability to identify biases, distortions, omissions, and assumptions. Excellent deductive and inductive reasoning, and problem solving skills contribute to critical thinking. So, you can consider the subject matter of sections 7.2 and 7.3 to be part of critical thinking. Because we will be devoting considerable time to these concepts in the rest of the module, let us begin with a discussion about the other aspects of critical thinking.

Let’s address that first part of the definition. Scientists form hypotheses, or predictions about some possible future observations. Then, they collect data, or information (think of this as making those future observations). They do their best to make unbiased observations using reliable techniques that have been verified by others. Then, and only then, they draw a conclusion about what those observations mean. Oh, and do not forget the most important part. “Conclusion” is probably not the most appropriate word because this conclusion is only tentative. A scientist is always prepared that someone else might come along and produce new observations that would require a new conclusion be drawn. Wow! If you like to be right, you could do a lot worse than using a process like this.

A Critical Thinker’s Toolkit 

Now for the second part of the definition. Good critical thinkers (and scientists) rely on a variety of tools to evaluate information. Perhaps the most recognizable tool for critical thinking is  skepticism (and this term provides the clearest link to the thinking like a scientist definition, as you are about to see). Some people intend it as an insult when they call someone a skeptic. But if someone calls you a skeptic, if they are using the term correctly, you should consider it a great compliment. Simply put, skepticism is a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided. People from Missouri should recognize this principle, as Missouri is known as the Show-Me State. As a skeptic, you are not inclined to believe something just because someone said so, because someone else believes it, or because it sounds reasonable. You must be persuaded by high quality evidence.

Of course, if that evidence is produced, you have a responsibility as a skeptic to change your belief. Failure to change a belief in the face of good evidence is not skepticism; skepticism has open mindedness at its core. M. Neil Browne and Stuart Keeley (2018) use the term weak sense critical thinking to describe critical thinking behaviors that are used only to strengthen a prior belief. Strong sense critical thinking, on the other hand, has as its goal reaching the best conclusion. Sometimes that means strengthening your prior belief, but sometimes it means changing your belief to accommodate the better evidence.

Many times, a failure to think critically or weak sense critical thinking is related to a  bias , an inclination, tendency, leaning, or prejudice. Everybody has biases, but many people are unaware of them. Awareness of your own biases gives you the opportunity to control or counteract them. Unfortunately, however, many people are happy to let their biases creep into their attempts to persuade others; indeed, it is a key part of their persuasive strategy. To see how these biases influence messages, just look at the different descriptions and explanations of the same events given by people of different ages or income brackets, or conservative versus liberal commentators, or by commentators from different parts of the world. Of course, to be successful, these people who are consciously using their biases must disguise them. Even undisguised biases can be difficult to identify, so disguised ones can be nearly impossible.

Here are some common sources of biases:

• Personal values and beliefs.  Some people believe that human beings are basically driven to seek power and that they are typically in competition with one another over scarce resources. These beliefs are similar to the world-view that political scientists call “realism.” Other people believe that human beings prefer to cooperate and that, given the chance, they will do so. These beliefs are similar to the world-view known as “idealism.” For many people, these deeply held beliefs can influence, or bias, their interpretations of such wide ranging situations as the behavior of nations and their leaders or the behavior of the driver in the car ahead of you. For example, if your worldview is that people are typically in competition and someone cuts you off on the highway, you may assume that the driver did it purposely to get ahead of you. Other types of beliefs about the way the world is or the way the world should be, for example, political beliefs, can similarly become a significant source of bias.
• Racism, sexism, ageism and other forms of prejudice and bigotry.  These are, sadly, a common source of bias in many people. They are essentially a special kind of “belief about the way the world is.” These beliefs—for example, that women do not make effective leaders—lead people to ignore contradictory evidence (examples of effective women leaders, or research that disputes the belief) and to interpret ambiguous evidence in a way consistent with the belief.
• Self-interest.  When particular people benefit from things turning out a certain way, they can sometimes be very susceptible to letting that interest bias them. For example, a company that will earn a profit if they sell their product may have a bias in the way that they give information about their product. A union that will benefit if its members get a generous contract might have a bias in the way it presents information about salaries at competing organizations. (Note that our inclusion of examples describing both companies and unions is an explicit attempt to control for our own personal biases). Home buyers are often dismayed to discover that they purchased their dream house from someone whose self-interest led them to lie about flooding problems in the basement or back yard. This principle, the biasing power of self-interest, is likely what led to the famous phrase  Caveat Emptor  (let the buyer beware) .  

Knowing that these types of biases exist will help you evaluate evidence more critically. Do not forget, though, that people are not always keen to let you discover the sources of biases in their arguments. For example, companies or political organizations can sometimes disguise their support of a research study by contracting with a university professor, who comes complete with a seemingly unbiased institutional affiliation, to conduct the study.

People’s biases, conscious or unconscious, can lead them to make omissions, distortions, and assumptions that undermine our ability to correctly evaluate evidence. It is essential that you look for these elements. Always ask, what is missing, what is not as it appears, and what is being assumed here? For example, consider this (fictional) chart from an ad reporting customer satisfaction at 4 local health clubs.

Clearly, from the results of the chart, one would be tempted to give Club C a try, as customer satisfaction is much higher than for the other 3 clubs.

There are so many distortions and omissions in this chart, however, that it is actually quite meaningless. First, how was satisfaction measured? Do the bars represent responses to a survey? If so, how were the questions asked? Most importantly, where is the missing scale for the chart? Although the differences look quite large, are they really?

Well, here is the same chart, with a different scale, this time labeled:

Club C is not so impressive any more, is it? In fact, all of the health clubs have customer satisfaction ratings (whatever that means) between 85% and 88%. In the first chart, the entire scale of the graph included only the percentages between 83 and 89. This “judicious” choice of scale—some would call it a distortion—and omission of that scale from the chart make the tiny differences among the clubs seem important, however.

Also, in order to be a critical thinker, you need to learn to pay attention to the assumptions that underlie a message. Let us briefly illustrate the role of assumptions by touching on some people’s beliefs about the criminal justice system in the US. Some believe that a major problem with our judicial system is that many criminals go free because of legal technicalities. Others believe that a major problem is that many innocent people are convicted of crimes. The simple fact is, both types of errors occur. A person’s conclusion about which flaw in our judicial system is the greater tragedy is based on an assumption about which of these is the more serious error (letting the guilty go free or convicting the innocent). This type of assumption is called a value assumption (Browne and Keeley, 2018). It reflects the differences in values that people develop, differences that may lead us to disregard valid evidence that does not fit in with our particular values.

Oh, by the way, some students probably noticed this, but the seven tips for evaluating information that we shared in Module 1 are related to this. Actually, they are part of this section. The tips are, to a very large degree, set of ideas you can use to help you identify biases, distortions, omissions, and assumptions. If you do not remember this section, we strongly recommend you take a few minutes to review it.

skepticism :  a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided

bias : an inclination, tendency, leaning, or prejudice

• Which of your beliefs (or disbeliefs) from the Activate exercise for this section were derived from a process of critical thinking? If some of your beliefs were not based on critical thinking, are you willing to reassess these beliefs? If the answer is no, why do you think that is? If the answer is yes, what concrete steps will you take?

7.2 Reasoning and Judgment

• What percentage of kidnappings are committed by strangers?
• Which area of the house is riskiest: kitchen, bathroom, or stairs?
• What is the most common cancer in the US?
• What percentage of workplace homicides are committed by co-workers?

An essential set of procedural thinking skills is  reasoning , the ability to generate and evaluate solid conclusions from a set of statements or evidence. You should note that these conclusions (when they are generated instead of being evaluated) are one key type of inference that we described in Section 7.1. There are two main types of reasoning, deductive and inductive.

Deductive reasoning

Suppose your teacher tells you that if you get an A on the final exam in a course, you will get an A for the whole course. Then, you get an A on the final exam. What will your final course grade be? Most people can see instantly that you can conclude with certainty that you will get an A for the course. This is a type of reasoning called  deductive reasoning , which is defined as reasoning in which a conclusion is guaranteed to be true as long as the statements leading to it are true. The three statements can be listed as an  argument , with two beginning statements and a conclusion:

Statement 1: If you get an A on the final exam, you will get an A for the course

Statement 2: You get an A on the final exam

Conclusion: You will get an A for the course

This particular arrangement, in which true beginning statements lead to a guaranteed true conclusion, is known as a  deductively valid argument . Although deductive reasoning is often the subject of abstract, brain-teasing, puzzle-like word problems, it is actually an extremely important type of everyday reasoning. It is just hard to recognize sometimes. For example, imagine that you are looking for your car keys and you realize that they are either in the kitchen drawer or in your book bag. After looking in the kitchen drawer, you instantly know that they must be in your book bag. That conclusion results from a simple deductive reasoning argument. In addition, solid deductive reasoning skills are necessary for you to succeed in the sciences, philosophy, math, computer programming, and any endeavor involving the use of logic to persuade others to your point of view or to evaluate others’ arguments.

Cognitive psychologists, and before them philosophers, have been quite interested in deductive reasoning, not so much for its practical applications, but for the insights it can offer them about the ways that human beings think. One of the early ideas to emerge from the examination of deductive reasoning is that people learn (or develop) mental versions of rules that allow them to solve these types of reasoning problems (Braine, 1978; Braine, Reiser, & Rumain, 1984). The best way to see this point of view is to realize that there are different possible rules, and some of them are very simple. For example, consider this rule of logic:

therefore q

Logical rules are often presented abstractly, as letters, in order to imply that they can be used in very many specific situations. Here is a concrete version of the of the same rule:

I’ll either have pizza or a hamburger for dinner tonight (p or q)

I won’t have pizza (not p)

Therefore, I’ll have a hamburger (therefore q)

This kind of reasoning seems so natural, so easy, that it is quite plausible that we would use a version of this rule in our daily lives. At least, it seems more plausible than some of the alternative possibilities—for example, that we need to have experience with the specific situation (pizza or hamburger, in this case) in order to solve this type of problem easily. So perhaps there is a form of natural logic (Rips, 1990) that contains very simple versions of logical rules. When we are faced with a reasoning problem that maps onto one of these rules, we use the rule.

But be very careful; things are not always as easy as they seem. Even these simple rules are not so simple. For example, consider the following rule. Many people fail to realize that this rule is just as valid as the pizza or hamburger rule above.

if p, then q

therefore, not p

Concrete version:

If I eat dinner, then I will have dessert

I did not have dessert

Therefore, I did not eat dinner

The simple fact is, it can be very difficult for people to apply rules of deductive logic correctly; as a result, they make many errors when trying to do so. Is this a deductively valid argument or not?

Students who like school study a lot

Students who study a lot get good grades

Jane does not like school

Therefore, Jane does not get good grades

Many people are surprised to discover that this is not a logically valid argument; the conclusion is not guaranteed to be true from the beginning statements. Although the first statement says that students who like school study a lot, it does NOT say that students who do not like school do not study a lot. In other words, it may very well be possible to study a lot without liking school. Even people who sometimes get problems like this right might not be using the rules of deductive reasoning. Instead, they might just be making judgments for examples they know, in this case, remembering instances of people who get good grades despite not liking school.

Making deductive reasoning even more difficult is the fact that there are two important properties that an argument may have. One, it can be valid or invalid (meaning that the conclusion does or does not follow logically from the statements leading up to it). Two, an argument (or more correctly, its conclusion) can be true or false. Here is an example of an argument that is logically valid, but has a false conclusion (at least we think it is false).

Either you are eleven feet tall or the Grand Canyon was created by a spaceship crashing into the earth.

You are not eleven feet tall

Therefore the Grand Canyon was created by a spaceship crashing into the earth

This argument has the exact same form as the pizza or hamburger argument above, making it is deductively valid. The conclusion is so false, however, that it is absurd (of course, the reason the conclusion is false is that the first statement is false). When people are judging arguments, they tend to not observe the difference between deductive validity and the empirical truth of statements or conclusions. If the elements of an argument happen to be true, people are likely to judge the argument logically valid; if the elements are false, they will very likely judge it invalid (Markovits & Bouffard-Bouchard, 1992; Moshman & Franks, 1986). Thus, it seems a stretch to say that people are using these logical rules to judge the validity of arguments. Many psychologists believe that most people actually have very limited deductive reasoning skills (Johnson-Laird, 1999). They argue that when faced with a problem for which deductive logic is required, people resort to some simpler technique, such as matching terms that appear in the statements and the conclusion (Evans, 1982). This might not seem like a problem, but what if reasoners believe that the elements are true and they happen to be wrong; they will would believe that they are using a form of reasoning that guarantees they are correct and yet be wrong.

deductive reasoning :  a type of reasoning in which the conclusion is guaranteed to be true any time the statements leading up to it are true

argument :  a set of statements in which the beginning statements lead to a conclusion

deductively valid argument :  an argument for which true beginning statements guarantee that the conclusion is true

Inductive reasoning and judgment

Every day, you make many judgments about the likelihood of one thing or another. Whether you realize it or not, you are practicing  inductive reasoning   on a daily basis. In inductive reasoning arguments, a conclusion is likely whenever the statements preceding it are true. The first thing to notice about inductive reasoning is that, by definition, you can never be sure about your conclusion; you can only estimate how likely the conclusion is. Inductive reasoning may lead you to focus on Memory Encoding and Recoding when you study for the exam, but it is possible the instructor will ask more questions about Memory Retrieval instead. Unlike deductive reasoning, the conclusions you reach through inductive reasoning are only probable, not certain. That is why scientists consider inductive reasoning weaker than deductive reasoning. But imagine how hard it would be for us to function if we could not act unless we were certain about the outcome.

Inductive reasoning can be represented as logical arguments consisting of statements and a conclusion, just as deductive reasoning can be. In an inductive argument, you are given some statements and a conclusion (or you are given some statements and must draw a conclusion). An argument is  inductively strong   if the conclusion would be very probable whenever the statements are true. So, for example, here is an inductively strong argument:

• Statement #1: The forecaster on Channel 2 said it is going to rain today.
• Statement #2: The forecaster on Channel 5 said it is going to rain today.
• Statement #3: It is very cloudy and humid.
• Statement #4: You just heard thunder.
• Conclusion (or judgment): It is going to rain today.

Think of the statements as evidence, on the basis of which you will draw a conclusion. So, based on the evidence presented in the four statements, it is very likely that it will rain today. Will it definitely rain today? Certainly not. We can all think of times that the weather forecaster was wrong.

A true story: Some years ago psychology student was watching a baseball playoff game between the St. Louis Cardinals and the Los Angeles Dodgers. A graphic on the screen had just informed the audience that the Cardinal at bat, (Hall of Fame shortstop) Ozzie Smith, a switch hitter batting left-handed for this plate appearance, had never, in nearly 3000 career at-bats, hit a home run left-handed. The student, who had just learned about inductive reasoning in his psychology class, turned to his companion (a Cardinals fan) and smugly said, “It is an inductively strong argument that Ozzie Smith will not hit a home run.” He turned back to face the television just in time to watch the ball sail over the right field fence for a home run. Although the student felt foolish at the time, he was not wrong. It was an inductively strong argument; 3000 at-bats is an awful lot of evidence suggesting that the Wizard of Ozz (as he was known) would not be hitting one out of the park (think of each at-bat without a home run as a statement in an inductive argument). Sadly (for the die-hard Cubs fan and Cardinals-hating student), despite the strength of the argument, the conclusion was wrong.

Given the possibility that we might draw an incorrect conclusion even with an inductively strong argument, we really want to be sure that we do, in fact, make inductively strong arguments. If we judge something probable, it had better be probable. If we judge something nearly impossible, it had better not happen. Think of inductive reasoning, then, as making reasonably accurate judgments of the probability of some conclusion given a set of evidence.

We base many decisions in our lives on inductive reasoning. For example:

Statement #1: Psychology is not my best subject

Statement #2: My psychology instructor has a reputation for giving difficult exams

Statement #3: My first psychology exam was much harder than I expected

Judgment: The next exam will probably be very difficult.

Decision: I will study tonight instead of watching Netflix.

Some other examples of judgments that people commonly make in a school context include judgments of the likelihood that:

• A particular class will be interesting/useful/difficult
• You will be able to finish writing a paper by next week if you go out tonight
• Your laptop’s battery will last through the next trip to the library
• You will not miss anything important if you skip class tomorrow
• Your instructor will not notice if you skip class tomorrow
• You will be able to find a book that you will need for a paper
• There will be an essay question about Memory Encoding on the next exam

Tversky and Kahneman (1983) recognized that there are two general ways that we might make these judgments; they termed them extensional (i.e., following the laws of probability) and intuitive (i.e., using shortcuts or heuristics, see below). We will use a similar distinction between Type 1 and Type 2 thinking, as described by Keith Stanovich and his colleagues (Evans and Stanovich, 2013; Stanovich and West, 2000). Type 1 thinking is fast, automatic, effortful, and emotional. In fact, it is hardly fair to call it reasoning at all, as judgments just seem to pop into one’s head. Type 2 thinking , on the other hand, is slow, effortful, and logical. So obviously, it is more likely to lead to a correct judgment, or an optimal decision. The problem is, we tend to over-rely on Type 1. Now, we are not saying that Type 2 is the right way to go for every decision or judgment we make. It seems a bit much, for example, to engage in a step-by-step logical reasoning procedure to decide whether we will have chicken or fish for dinner tonight.

Many bad decisions in some very important contexts, however, can be traced back to poor judgments of the likelihood of certain risks or outcomes that result from the use of Type 1 when a more logical reasoning process would have been more appropriate. For example:

Statement #1: It is late at night.

Statement #2: Albert has been drinking beer for the past five hours at a party.

Statement #3: Albert is not exactly sure where he is or how far away home is.

Judgment: Albert will have no difficulty walking home.

Decision: He walks home alone.

As you can see in this example, the three statements backing up the judgment do not really support it. In other words, this argument is not inductively strong because it is based on judgments that ignore the laws of probability. What are the chances that someone facing these conditions will be able to walk home alone easily? And one need not be drunk to make poor decisions based on judgments that just pop into our heads.

The truth is that many of our probability judgments do not come very close to what the laws of probability say they should be. Think about it. In order for us to reason in accordance with these laws, we would need to know the laws of probability, which would allow us to calculate the relationship between particular pieces of evidence and the probability of some outcome (i.e., how much likelihood should change given a piece of evidence), and we would have to do these heavy math calculations in our heads. After all, that is what Type 2 requires. Needless to say, even if we were motivated, we often do not even know how to apply Type 2 reasoning in many cases.

So what do we do when we don’t have the knowledge, skills, or time required to make the correct mathematical judgment? Do we hold off and wait until we can get better evidence? Do we read up on probability and fire up our calculator app so we can compute the correct probability? Of course not. We rely on Type 1 thinking. We “wing it.” That is, we come up with a likelihood estimate using some means at our disposal. Psychologists use the term heuristic to describe the type of “winging it” we are talking about. A  heuristic   is a shortcut strategy that we use to make some judgment or solve some problem (see Section 7.3). Heuristics are easy and quick, think of them as the basic procedures that are characteristic of Type 1.  They can absolutely lead to reasonably good judgments and decisions in some situations (like choosing between chicken and fish for dinner). They are, however, far from foolproof. There are, in fact, quite a lot of situations in which heuristics can lead us to make incorrect judgments, and in many cases the decisions based on those judgments can have serious consequences.

Let us return to the activity that begins this section. You were asked to judge the likelihood (or frequency) of certain events and risks. You were free to come up with your own evidence (or statements) to make these judgments. This is where a heuristic crops up. As a judgment shortcut, we tend to generate specific examples of those very events to help us decide their likelihood or frequency. For example, if we are asked to judge how common, frequent, or likely a particular type of cancer is, many of our statements would be examples of specific cancer cases:

Statement #1: Andy Kaufman (comedian) had lung cancer.

Statement #2: Colin Powell (US Secretary of State) had prostate cancer.

Statement #3: Bob Marley (musician) had skin and brain cancer

Statement #4: Sandra Day O’Connor (Supreme Court Justice) had breast cancer.

Statement #5: Fred Rogers (children’s entertainer) had stomach cancer.

Statement #6: Robin Roberts (news anchor) had breast cancer.

Statement #7: Bette Davis (actress) had breast cancer.

Judgment: Breast cancer is the most common type.

Your own experience or memory may also tell you that breast cancer is the most common type. But it is not (although it is common). Actually, skin cancer is the most common type in the US. We make the same types of misjudgments all the time because we do not generate the examples or evidence according to their actual frequencies or probabilities. Instead, we have a tendency (or bias) to search for the examples in memory; if they are easy to retrieve, we assume that they are common. To rephrase this in the language of the heuristic, events seem more likely to the extent that they are available to memory. This bias has been termed the  availability heuristic   (Kahneman and Tversky, 1974).

The fact that we use the availability heuristic does not automatically mean that our judgment is wrong. The reason we use heuristics in the first place is that they work fairly well in many cases (and, of course that they are easy to use). So, the easiest examples to think of sometimes are the most common ones. Is it more likely that a member of the U.S. Senate is a man or a woman? Most people have a much easier time generating examples of male senators. And as it turns out, the U.S. Senate has many more men than women (74 to 26 in 2020). In this case, then, the availability heuristic would lead you to make the correct judgment; it is far more likely that a senator would be a man.

In many other cases, however, the availability heuristic will lead us astray. This is because events can be memorable for many reasons other than their frequency. Section 5.2, Encoding Meaning, suggested that one good way to encode the meaning of some information is to form a mental image of it. Thus, information that has been pictured mentally will be more available to memory. Indeed, an event that is vivid and easily pictured will trick many people into supposing that type of event is more common than it actually is. Repetition of information will also make it more memorable. So, if the same event is described to you in a magazine, on the evening news, on a podcast that you listen to, and in your Facebook feed; it will be very available to memory. Again, the availability heuristic will cause you to misperceive the frequency of these types of events.

Most interestingly, information that is unusual is more memorable. Suppose we give you the following list of words to remember: box, flower, letter, platypus, oven, boat, newspaper, purse, drum, car. Very likely, the easiest word to remember would be platypus, the unusual one. The same thing occurs with memories of events. An event may be available to memory because it is unusual, yet the availability heuristic leads us to judge that the event is common. Did you catch that? In these cases, the availability heuristic makes us think the exact opposite of the true frequency. We end up thinking something is common because it is unusual (and therefore memorable). Yikes.

The misapplication of the availability heuristic sometimes has unfortunate results. For example, if you went to K-12 school in the US over the past 10 years, it is extremely likely that you have participated in lockdown and active shooter drills. Of course, everyone is trying to prevent the tragedy of another school shooting. And believe us, we are not trying to minimize how terrible the tragedy is. But the truth of the matter is, school shootings are extremely rare. Because the federal government does not keep a database of school shootings, the Washington Post has maintained their own running tally. Between 1999 and January 2020 (the date of the most recent school shooting with a death in the US at of the time this paragraph was written), the Post reported a total of 254 people died in school shootings in the US. Not 254 per year, 254 total. That is an average of 12 per year. Of course, that is 254 people who should not have died (particularly because many were children), but in a country with approximately 60,000,000 students and teachers, this is a very small risk.

But many students and teachers are terrified that they will be victims of school shootings because of the availability heuristic. It is so easy to think of examples (they are very available to memory) that people believe the event is very common. It is not. And there is a downside to this. We happen to believe that there is an enormous gun violence problem in the United States. According the the Centers for Disease Control and Prevention, there were 39,773 firearm deaths in the US in 2017. Fifteen of those deaths were in school shootings, according to the Post. 60% of those deaths were suicides. When people pay attention to the school shooting risk (low), they often fail to notice the much larger risk.

And examples like this are by no means unique. The authors of this book have been teaching psychology since the 1990’s. We have been able to make the exact same arguments about the misapplication of the availability heuristics and keep them current by simply swapping out for the “fear of the day.” In the 1990’s it was children being kidnapped by strangers (it was known as “stranger danger”) despite the facts that kidnappings accounted for only 2% of the violent crimes committed against children, and only 24% of kidnappings are committed by strangers (US Department of Justice, 2007). This fear overlapped with the fear of terrorism that gripped the country after the 2001 terrorist attacks on the World Trade Center and US Pentagon and still plagues the population of the US somewhat in 2020. After a well-publicized, sensational act of violence, people are extremely likely to increase their estimates of the chances that they, too, will be victims of terror. Think about the reality, however. In October of 2001, a terrorist mailed anthrax spores to members of the US government and a number of media companies. A total of five people died as a result of this attack. The nation was nearly paralyzed by the fear of dying from the attack; in reality the probability of an individual person dying was 0.00000002.

The availability heuristic can lead you to make incorrect judgments in a school setting as well. For example, suppose you are trying to decide if you should take a class from a particular math professor. You might try to make a judgment of how good a teacher she is by recalling instances of friends and acquaintances making comments about her teaching skill. You may have some examples that suggest that she is a poor teacher very available to memory, so on the basis of the availability heuristic you judge her a poor teacher and decide to take the class from someone else. What if, however, the instances you recalled were all from the same person, and this person happens to be a very colorful storyteller? The subsequent ease of remembering the instances might not indicate that the professor is a poor teacher after all.

Although the availability heuristic is obviously important, it is not the only judgment heuristic we use. Amos Tversky and Daniel Kahneman examined the role of heuristics in inductive reasoning in a long series of studies. Kahneman received a Nobel Prize in Economics for this research in 2002, and Tversky would have certainly received one as well if he had not died of melanoma at age 59 in 1996 (Nobel Prizes are not awarded posthumously). Kahneman and Tversky demonstrated repeatedly that people do not reason in ways that are consistent with the laws of probability. They identified several heuristic strategies that people use instead to make judgments about likelihood. The importance of this work for economics (and the reason that Kahneman was awarded the Nobel Prize) is that earlier economic theories had assumed that people do make judgments rationally, that is, in agreement with the laws of probability.

Another common heuristic that people use for making judgments is the  representativeness heuristic (Kahneman & Tversky 1973). Suppose we describe a person to you. He is quiet and shy, has an unassuming personality, and likes to work with numbers. Is this person more likely to be an accountant or an attorney? If you said accountant, you were probably using the representativeness heuristic. Our imaginary person is judged likely to be an accountant because he resembles, or is representative of the concept of, an accountant. When research participants are asked to make judgments such as these, the only thing that seems to matter is the representativeness of the description. For example, if told that the person described is in a room that contains 70 attorneys and 30 accountants, participants will still assume that he is an accountant.

inductive reasoning :  a type of reasoning in which we make judgments about likelihood from sets of evidence

inductively strong argument :  an inductive argument in which the beginning statements lead to a conclusion that is probably true

heuristic :  a shortcut strategy that we use to make judgments and solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

availability heuristic :  judging the frequency or likelihood of some event type according to how easily examples of the event can be called to mind (i.e., how available they are to memory)

representativeness heuristic:   judging the likelihood that something is a member of a category on the basis of how much it resembles a typical category member (i.e., how representative it is of the category)

Type 1 thinking : fast, automatic, and emotional thinking.

Type 2 thinking : slow, effortful, and logical thinking.

• What percentage of workplace homicides are co-worker violence?

Many people get these questions wrong. The answers are 10%; stairs; skin; 6%. How close were your answers? Explain how the availability heuristic might have led you to make the incorrect judgments.

• Can you think of some other judgments that you have made (or beliefs that you have) that might have been influenced by the availability heuristic?

7.3 Problem Solving

• Please take a few minutes to list a number of problems that you are facing right now.
• Now write about a problem that you recently solved.
• What is your definition of a problem?

Mary has a problem. Her daughter, ordinarily quite eager to please, appears to delight in being the last person to do anything. Whether getting ready for school, going to piano lessons or karate class, or even going out with her friends, she seems unwilling or unable to get ready on time. Other people have different kinds of problems. For example, many students work at jobs, have numerous family commitments, and are facing a course schedule full of difficult exams, assignments, papers, and speeches. How can they find enough time to devote to their studies and still fulfill their other obligations? Speaking of students and their problems: Show that a ball thrown vertically upward with initial velocity v0 takes twice as much time to return as to reach the highest point (from Spiegel, 1981).

These are three very different situations, but we have called them all problems. What makes them all the same, despite the differences? A psychologist might define a  problem   as a situation with an initial state, a goal state, and a set of possible intermediate states. Somewhat more meaningfully, we might consider a problem a situation in which you are in here one state (e.g., daughter is always late), you want to be there in another state (e.g., daughter is not always late), and with no obvious way to get from here to there. Defined this way, each of the three situations we outlined can now be seen as an example of the same general concept, a problem. At this point, you might begin to wonder what is not a problem, given such a general definition. It seems that nearly every non-routine task we engage in could qualify as a problem. As long as you realize that problems are not necessarily bad (it can be quite fun and satisfying to rise to the challenge and solve a problem), this may be a useful way to think about it.

Can we identify a set of problem-solving skills that would apply to these very different kinds of situations? That task, in a nutshell, is a major goal of this section. Let us try to begin to make sense of the wide variety of ways that problems can be solved with an important observation: the process of solving problems can be divided into two key parts. First, people have to notice, comprehend, and represent the problem properly in their minds (called  problem representation ). Second, they have to apply some kind of solution strategy to the problem. Psychologists have studied both of these key parts of the process in detail.

When you first think about the problem-solving process, you might guess that most of our difficulties would occur because we are failing in the second step, the application of strategies. Although this can be a significant difficulty much of the time, the more important source of difficulty is probably problem representation. In short, we often fail to solve a problem because we are looking at it, or thinking about it, the wrong way.

problem :  a situation in which we are in an initial state, have a desired goal state, and there is a number of possible intermediate states (i.e., there is no obvious way to get from the initial to the goal state)

problem representation :  noticing, comprehending and forming a mental conception of a problem

Defining and Mentally Representing Problems in Order to Solve Them

So, the main obstacle to solving a problem is that we do not clearly understand exactly what the problem is. Recall the problem with Mary’s daughter always being late. One way to represent, or to think about, this problem is that she is being defiant. She refuses to get ready in time. This type of representation or definition suggests a particular type of solution. Another way to think about the problem, however, is to consider the possibility that she is simply being sidetracked by interesting diversions. This different conception of what the problem is (i.e., different representation) suggests a very different solution strategy. For example, if Mary defines the problem as defiance, she may be tempted to solve the problem using some kind of coercive tactics, that is, to assert her authority as her mother and force her to listen. On the other hand, if Mary defines the problem as distraction, she may try to solve it by simply removing the distracting objects.

As you might guess, when a problem is represented one way, the solution may seem very difficult, or even impossible. Seen another way, the solution might be very easy. For example, consider the following problem (from Nasar, 1998):

Two bicyclists start 20 miles apart and head toward each other, each going at a steady rate of 10 miles per hour. At the same time, a fly that travels at a steady 15 miles per hour starts from the front wheel of the southbound bicycle and flies to the front wheel of the northbound one, then turns around and flies to the front wheel of the southbound one again, and continues in this manner until he is crushed between the two front wheels. Question: what total distance did the fly cover?

Please take a few minutes to try to solve this problem.

Most people represent this problem as a question about a fly because, well, that is how the question is asked. The solution, using this representation, is to figure out how far the fly travels on the first leg of its journey, then add this total to how far it travels on the second leg of its journey (when it turns around and returns to the first bicycle), then continue to add the smaller distance from each leg of the journey until you converge on the correct answer. You would have to be quite skilled at math to solve this problem, and you would probably need some time and pencil and paper to do it.

If you consider a different representation, however, you can solve this problem in your head. Instead of thinking about it as a question about a fly, think about it as a question about the bicycles. They are 20 miles apart, and each is traveling 10 miles per hour. How long will it take for the bicycles to reach each other? Right, one hour. The fly is traveling 15 miles per hour; therefore, it will travel a total of 15 miles back and forth in the hour before the bicycles meet. Represented one way (as a problem about a fly), the problem is quite difficult. Represented another way (as a problem about two bicycles), it is easy. Changing your representation of a problem is sometimes the best—sometimes the only—way to solve it.

Unfortunately, however, changing a problem’s representation is not the easiest thing in the world to do. Often, problem solvers get stuck looking at a problem one way. This is called  fixation . Most people who represent the preceding problem as a problem about a fly probably do not pause to reconsider, and consequently change, their representation. A parent who thinks her daughter is being defiant is unlikely to consider the possibility that her behavior is far less purposeful.

Problem-solving fixation was examined by a group of German psychologists called Gestalt psychologists during the 1930’s and 1940’s. Karl Dunker, for example, discovered an important type of failure to take a different perspective called  functional fixedness . Imagine being a participant in one of his experiments. You are asked to figure out how to mount two candles on a door and are given an assortment of odds and ends, including a small empty cardboard box and some thumbtacks. Perhaps you have already figured out a solution: tack the box to the door so it forms a platform, then put the candles on top of the box. Most people are able to arrive at this solution. Imagine a slight variation of the procedure, however. What if, instead of being empty, the box had matches in it? Most people given this version of the problem do not arrive at the solution given above. Why? Because it seems to people that when the box contains matches, it already has a function; it is a matchbox. People are unlikely to consider a new function for an object that already has a function. This is functional fixedness.

Mental set is a type of fixation in which the problem solver gets stuck using the same solution strategy that has been successful in the past, even though the solution may no longer be useful. It is commonly seen when students do math problems for homework. Often, several problems in a row require the reapplication of the same solution strategy. Then, without warning, the next problem in the set requires a new strategy. Many students attempt to apply the formerly successful strategy on the new problem and therefore cannot come up with a correct answer.

The thing to remember is that you cannot solve a problem unless you correctly identify what it is to begin with (initial state) and what you want the end result to be (goal state). That may mean looking at the problem from a different angle and representing it in a new way. The correct representation does not guarantee a successful solution, but it certainly puts you on the right track.

A bit more optimistically, the Gestalt psychologists discovered what may be considered the opposite of fixation, namely  insight . Sometimes the solution to a problem just seems to pop into your head. Wolfgang Kohler examined insight by posing many different problems to chimpanzees, principally problems pertaining to their acquisition of out-of-reach food. In one version, a banana was placed outside of a chimpanzee’s cage and a short stick inside the cage. The stick was too short to retrieve the banana, but was long enough to retrieve a longer stick also located outside of the cage. This second stick was long enough to retrieve the banana. After trying, and failing, to reach the banana with the shorter stick, the chimpanzee would try a couple of random-seeming attempts, react with some apparent frustration or anger, then suddenly rush to the longer stick, the correct solution fully realized at this point. This sudden appearance of the solution, observed many times with many different problems, was termed insight by Kohler.

Lest you think it pertains to chimpanzees only, Karl Dunker demonstrated that children also solve problems through insight in the 1930s. More importantly, you have probably experienced insight yourself. Think back to a time when you were trying to solve a difficult problem. After struggling for a while, you gave up. Hours later, the solution just popped into your head, perhaps when you were taking a walk, eating dinner, or lying in bed.

fixation :  when a problem solver gets stuck looking at a problem a particular way and cannot change his or her representation of it (or his or her intended solution strategy)

functional fixedness :  a specific type of fixation in which a problem solver cannot think of a new use for an object that already has a function

mental set :  a specific type of fixation in which a problem solver gets stuck using the same solution strategy that has been successful in the past

insight :  a sudden realization of a solution to a problem

Solving Problems by Trial and Error

Correctly identifying the problem and your goal for a solution is a good start, but recall the psychologist’s definition of a problem: it includes a set of possible intermediate states. Viewed this way, a problem can be solved satisfactorily only if one can find a path through some of these intermediate states to the goal. Imagine a fairly routine problem, finding a new route to school when your ordinary route is blocked (by road construction, for example). At each intersection, you may turn left, turn right, or go straight. A satisfactory solution to the problem (of getting to school) is a sequence of selections at each intersection that allows you to wind up at school.

If you had all the time in the world to get to school, you might try choosing intermediate states randomly. At one corner you turn left, the next you go straight, then you go left again, then right, then right, then straight. Unfortunately, trial and error will not necessarily get you where you want to go, and even if it does, it is not the fastest way to get there. For example, when a friend of ours was in college, he got lost on the way to a concert and attempted to find the venue by choosing streets to turn onto randomly (this was long before the use of GPS). Amazingly enough, the strategy worked, although he did end up missing two out of the three bands who played that night.

Trial and error is not all bad, however. B.F. Skinner, a prominent behaviorist psychologist, suggested that people often behave randomly in order to see what effect the behavior has on the environment and what subsequent effect this environmental change has on them. This seems particularly true for the very young person. Picture a child filling a household’s fish tank with toilet paper, for example. To a child trying to develop a repertoire of creative problem-solving strategies, an odd and random behavior might be just the ticket. Eventually, the exasperated parent hopes, the child will discover that many of these random behaviors do not successfully solve problems; in fact, in many cases they create problems. Thus, one would expect a decrease in this random behavior as a child matures. You should realize, however, that the opposite extreme is equally counterproductive. If the children become too rigid, never trying something unexpected and new, their problem solving skills can become too limited.

Effective problem solving seems to call for a happy medium that strikes a balance between using well-founded old strategies and trying new ground and territory. The individual who recognizes a situation in which an old problem-solving strategy would work best, and who can also recognize a situation in which a new untested strategy is necessary is halfway to success.

Solving Problems with Algorithms and Heuristics

For many problems there is a possible strategy available that will guarantee a correct solution. For example, think about math problems. Math lessons often consist of step-by-step procedures that can be used to solve the problems. If you apply the strategy without error, you are guaranteed to arrive at the correct solution to the problem. This approach is called using an  algorithm , a term that denotes the step-by-step procedure that guarantees a correct solution. Because algorithms are sometimes available and come with a guarantee, you might think that most people use them frequently. Unfortunately, however, they do not. As the experience of many students who have struggled through math classes can attest, algorithms can be extremely difficult to use, even when the problem solver knows which algorithm is supposed to work in solving the problem. In problems outside of math class, we often do not even know if an algorithm is available. It is probably fair to say, then, that algorithms are rarely used when people try to solve problems.

Because algorithms are so difficult to use, people often pass up the opportunity to guarantee a correct solution in favor of a strategy that is much easier to use and yields a reasonable chance of coming up with a correct solution. These strategies are called  problem solving heuristics . Similar to what you saw in section 6.2 with reasoning heuristics, a problem solving heuristic is a shortcut strategy that people use when trying to solve problems. It usually works pretty well, but does not guarantee a correct solution to the problem. For example, one problem solving heuristic might be “always move toward the goal” (so when trying to get to school when your regular route is blocked, you would always turn in the direction you think the school is). A heuristic that people might use when doing math homework is “use the same solution strategy that you just used for the previous problem.”

By the way, we hope these last two paragraphs feel familiar to you. They seem to parallel a distinction that you recently learned. Indeed, algorithms and problem-solving heuristics are another example of the distinction between Type 1 thinking and Type 2 thinking.

Although it is probably not worth describing a large number of specific heuristics, two observations about heuristics are worth mentioning. First, heuristics can be very general or they can be very specific, pertaining to a particular type of problem only. For example, “always move toward the goal” is a general strategy that you can apply to countless problem situations. On the other hand, “when you are lost without a functioning gps, pick the most expensive car you can see and follow it” is specific to the problem of being lost. Second, all heuristics are not equally useful. One heuristic that many students know is “when in doubt, choose c for a question on a multiple-choice exam.” This is a dreadful strategy because many instructors intentionally randomize the order of answer choices. Another test-taking heuristic, somewhat more useful, is “look for the answer to one question somewhere else on the exam.”

You really should pay attention to the application of heuristics to test taking. Imagine that while reviewing your answers for a multiple-choice exam before turning it in, you come across a question for which you originally thought the answer was c. Upon reflection, you now think that the answer might be b. Should you change the answer to b, or should you stick with your first impression? Most people will apply the heuristic strategy to “stick with your first impression.” What they do not realize, of course, is that this is a very poor strategy (Lilienfeld et al, 2009). Most of the errors on exams come on questions that were answered wrong originally and were not changed (so they remain wrong). There are many fewer errors where we change a correct answer to an incorrect answer. And, of course, sometimes we change an incorrect answer to a correct answer. In fact, research has shown that it is more common to change a wrong answer to a right answer than vice versa (Bruno, 2001).

The belief in this poor test-taking strategy (stick with your first impression) is based on the  confirmation bias   (Nickerson, 1998; Wason, 1960). You first saw the confirmation bias in Module 1, but because it is so important, we will repeat the information here. People have a bias, or tendency, to notice information that confirms what they already believe. Somebody at one time told you to stick with your first impression, so when you look at the results of an exam you have taken, you will tend to notice the cases that are consistent with that belief. That is, you will notice the cases in which you originally had an answer correct and changed it to the wrong answer. You tend not to notice the other two important (and more common) cases, changing an answer from wrong to right, and leaving a wrong answer unchanged.

Because heuristics by definition do not guarantee a correct solution to a problem, mistakes are bound to occur when we employ them. A poor choice of a specific heuristic will lead to an even higher likelihood of making an error.

algorithm :  a step-by-step procedure that guarantees a correct solution to a problem

problem solving heuristic :  a shortcut strategy that we use to solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

confirmation bias :  people’s tendency to notice information that confirms what they already believe

An Effective Problem-Solving Sequence

You may be left with a big question: If algorithms are hard to use and heuristics often don’t work, how am I supposed to solve problems? Robert Sternberg (1996), as part of his theory of what makes people successfully intelligent (Module 8) described a problem-solving sequence that has been shown to work rather well:

• Identify the existence of a problem.  In school, problem identification is often easy; problems that you encounter in math classes, for example, are conveniently labeled as problems for you. Outside of school, however, realizing that you have a problem is a key difficulty that you must get past in order to begin solving it. You must be very sensitive to the symptoms that indicate a problem.
• Define the problem.  Suppose you realize that you have been having many headaches recently. Very likely, you would identify this as a problem. If you define the problem as “headaches,” the solution would probably be to take aspirin or ibuprofen or some other anti-inflammatory medication. If the headaches keep returning, however, you have not really solved the problem—likely because you have mistaken a symptom for the problem itself. Instead, you must find the root cause of the headaches. Stress might be the real problem. For you to successfully solve many problems it may be necessary for you to overcome your fixations and represent the problems differently. One specific strategy that you might find useful is to try to define the problem from someone else’s perspective. How would your parents, spouse, significant other, doctor, etc. define the problem? Somewhere in these different perspectives may lurk the key definition that will allow you to find an easier and permanent solution.
• Formulate strategy.  Now it is time to begin planning exactly how the problem will be solved. Is there an algorithm or heuristic available for you to use? Remember, heuristics by their very nature guarantee that occasionally you will not be able to solve the problem. One point to keep in mind is that you should look for long-range solutions, which are more likely to address the root cause of a problem than short-range solutions.
• Represent and organize information.  Similar to the way that the problem itself can be defined, or represented in multiple ways, information within the problem is open to different interpretations. Suppose you are studying for a big exam. You have chapters from a textbook and from a supplemental reader, along with lecture notes that all need to be studied. How should you (represent and) organize these materials? Should you separate them by type of material (text versus reader versus lecture notes), or should you separate them by topic? To solve problems effectively, you must learn to find the most useful representation and organization of information.
• Allocate resources.  This is perhaps the simplest principle of the problem solving sequence, but it is extremely difficult for many people. First, you must decide whether time, money, skills, effort, goodwill, or some other resource would help to solve the problem Then, you must make the hard choice of deciding which resources to use, realizing that you cannot devote maximum resources to every problem. Very often, the solution to problem is simply to change how resources are allocated (for example, spending more time studying in order to improve grades).
• Monitor and evaluate solutions.  Pay attention to the solution strategy while you are applying it. If it is not working, you may be able to select another strategy. Another fact you should realize about problem solving is that it never does end. Solving one problem frequently brings up new ones. Good monitoring and evaluation of your problem solutions can help you to anticipate and get a jump on solving the inevitable new problems that will arise.

Please note that this as  an  effective problem-solving sequence, not  the  effective problem solving sequence. Just as you can become fixated and end up representing the problem incorrectly or trying an inefficient solution, you can become stuck applying the problem-solving sequence in an inflexible way. Clearly there are problem situations that can be solved without using these skills in this order.

Additionally, many real-world problems may require that you go back and redefine a problem several times as the situation changes (Sternberg et al. 2000). For example, consider the problem with Mary’s daughter one last time. At first, Mary did represent the problem as one of defiance. When her early strategy of pleading and threatening punishment was unsuccessful, Mary began to observe her daughter more carefully. She noticed that, indeed, her daughter’s attention would be drawn by an irresistible distraction or book. Fresh with a re-representation of the problem, she began a new solution strategy. She began to remind her daughter every few minutes to stay on task and remind her that if she is ready before it is time to leave, she may return to the book or other distracting object at that time. Fortunately, this strategy was successful, so Mary did not have to go back and redefine the problem again.

Pick one or two of the problems that you listed when you first started studying this section and try to work out the steps of Sternberg’s problem solving sequence for each one.

a mental representation of a category of things in the world

an assumption about the truth of something that is not stated. Inferences come from our prior knowledge and experience, and from logical reasoning

knowledge about one’s own cognitive processes; thinking about your thinking

individuals who are less competent tend to overestimate their abilities more than individuals who are more competent do

Thinking like a scientist in your everyday life for the purpose of drawing correct conclusions. It entails skepticism; an ability to identify biases, distortions, omissions, and assumptions; and excellent deductive and inductive reasoning, and problem solving skills.

a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided

an inclination, tendency, leaning, or prejudice

a type of reasoning in which the conclusion is guaranteed to be true any time the statements leading up to it are true

a set of statements in which the beginning statements lead to a conclusion

an argument for which true beginning statements guarantee that the conclusion is true

a type of reasoning in which we make judgments about likelihood from sets of evidence

an inductive argument in which the beginning statements lead to a conclusion that is probably true

fast, automatic, and emotional thinking

slow, effortful, and logical thinking

a shortcut strategy that we use to make judgments and solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

udging the frequency or likelihood of some event type according to how easily examples of the event can be called to mind (i.e., how available they are to memory)

judging the likelihood that something is a member of a category on the basis of how much it resembles a typical category member (i.e., how representative it is of the category)

a situation in which we are in an initial state, have a desired goal state, and there is a number of possible intermediate states (i.e., there is no obvious way to get from the initial to the goal state)

noticing, comprehending and forming a mental conception of a problem

when a problem solver gets stuck looking at a problem a particular way and cannot change his or her representation of it (or his or her intended solution strategy)

a specific type of fixation in which a problem solver cannot think of a new use for an object that already has a function

a specific type of fixation in which a problem solver gets stuck using the same solution strategy that has been successful in the past

a sudden realization of a solution to a problem

a step-by-step procedure that guarantees a correct solution to a problem

The tendency to notice and pay attention to information that confirms your prior beliefs and to ignore information that disconfirms them.

a shortcut strategy that we use to solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

Introduction to Psychology Copyright © 2020 by Ken Gray; Elizabeth Arnott-Hill; and Or'Shaundra Benson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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The Most Important Logical Thinking Skills (With Examples)

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Logical thinking skills like critical-thinking, research, and creative thinking are valuable assets in the workplace. These skills are sought after by many employers, who want employees that take into account facts and data before deciding on an important course of action. This is because such solutions will ensure the organization’s processes can continue to operate efficiently.

So, if you’re a job seeker or employee looking to explore and brush up on your logical thinking skills, you’re in luck. This article will cover examples of logical thinking skills in the workplace, as well as what you can do to showcase those skills on your resume and in interviews.

Key Takeaways:

Logical thinking is problem solving based on reasoning that follows a strictly structured progression of analysis.

Critical thinking, research, creativity, mathematics, reading, active listening, and organization are all important logical thinking skills in the workplace.

Logical thinking provides objectivity for decision making that multiple people can accept.

Deduction follows valid premises to reach a logical conclusion.

It can be very helpful to demonstrate logical thinking skills at a job interview.

What is logical thinking?

10 examples of logical thinking skills, examples of logical thinking in the workplace, what is deductive reasoning, logical thinking in a job interview, logical thinking skills faq, final thoughts.

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Logical thinking is the ability to reason out an issue after observing and analyzing it from all angles . You can then form a conclusion that makes the most sense. It also includes the ability to take note of reactions and feedback to aid in the formation of the conclusion.

Logical thinking skills enable you to present your justification for the actions you take, the strategies you use, and the decisions you make. You can easily stand in front of your clients, peers, and supervisors and defend your product, service, and course of action if the necessity arises.

Logical thinking is an excellent way of solving complex problems. You can break the problem into smaller parts; solve them individually in a sequence, then present the complete solution. However, it is not infallible.

So, when a problem in the workplace feels overwhelming, you may want to think about it logically first.

Logical thinking skills are a skill set that enables you to reason logically when solving problems. They enable you to provide well-reasoned answers to any issues that arise. They also empower you to make decisions that most people will consider rational.

Critical-thinking skills. If you are a critical thinker, then you can analyze and evaluate a problem before making judgments. You need to improve your critical thinking process to become a logical thinker.

Your critical thinking skills will improve your ability to solve problems. You will be the go-to employee concerning crises. People can rely on you to be reasonable whenever an issue arises instead of letting biases rule you.

Research skills. If you are a good researcher , then you can search and locate data that can be useful when presenting information on your preferred subject.

The more relevant information you have about a particular subject, the more accurate your conclusions are likely to be. The sources you use must be reputable and relevant.

For this reason, your ability to ferret out information will affect how well you can reason logically.

Creative thinking skills. If you are a creative thinker , then you can find innovative solutions to problems.

You are the kind of person that can think outside the box when brainstorming ideas and potential solutions. Your thinking is not rigid. Instead, you tend to look at issues in ways other people have not thought of before.

While logical thinking is based on data and facts, that doesn’t mean it is rigid. You can creatively find ways of sourcing that data or experimenting so that you can form logical conclusions. Your strategic thinking skills will also help enable you to analyze reactions or collect feedback .

Mathematical skills. If you are skilled in mathematics , then you can work well with numbers and represent mathematical ideas using visual symbols. Your brain must be able to compute information.

Business is a numbers game. That means you must have some knowledge of mathematics. You must be able to perform basic mathematical tasks involving addition, subtractions, divisions, multiplications, etc.

So, to become a logical thinker, you must be comfortable working with numbers. You will encounter them in many business-related complex problems. And your ability to understand them will determine whether you can reach an accurate logical conclusion that helps your organization.

Reading skills. If you are a good reader , then you can make sense of the letters and symbols that you see. Your ability to read will determine your competency concerning your logical thinking and reasoning skills.

And that skill set will come in handy when you are presented with different sets of work-related statements from which you are meant to conclude. Such statements may be part of your company policy, technical manual, etc.

Active listening skills. Active listening is an important communication skill to have. If you are an active listener, then you can hear, understand what is being said, remember it, and respond to it if necessary.

Not all instructions are written. You may need to listen to someone to get the information you need to solve problems before you write it down. In that case, your active listening skills will determine how well you can remember the information so that you can use it to reason things out logically.

Information ordering skills. If you have information ordering skills, then you can arrange things based on a specified order following the set rules or conditions. These things may include mathematical operations, words, pictures, etc.

Different organizations have different business processes. The workflow in one organization will be not similar to that of another organization even if both belong to the same industry.

Your ability to order information will depend on an organization’s culture . And it will have a major impact on how you can think and reason concerning solutions to your company problems.

If you follow the wrong order, then no matter how good your problem-solving techniques are your conclusions may be wrong for your organization.

Persuasion skills. Logical thinking can be useful when persuading others, especially in the workplace.

For example, lets say one of your co-workers wants to take a project in an impulsive direction, which will increase the budget. However, after you do your research, you realize a budget increase would be impossible.

You can then use your logical thinking skills to explain the situation to your co-worker , including details facts and numbers, which will help dissuade them from making an uninformed decision.

Decision making skills. Decision making skills go hand and hand with logical thinking, as being able to think logically about solutions and research topics will make it far easier to make informed decisions.

After all, no one likes making a decision that feels like a shot in the dark, so knowing crucial information about the options aviable to you, and thinking about them logically, can improve your confidence around decision making.

Confidence skills. Confidence that stems from an emotional and irrational place will always be fragile, but when you have more knowledge available to you through logical thinking, you can be more confident in your confidence skills.

For instance, if an employee asked you to answer an important question, you will have a lot more confidence in your answer if you can think logically about it, as opposed to having an air of uncertainty.

To improve your logic skills, it would be wise to practice how to solve problems based on facts and data. Below are examples of logical thinking in the workplace that will help you understand this kind of reasoning so that you can improve your thinking:

The human resource department in your organization has determined that leadership skills are important for anyone looking to go into a senior management position. So, it decides that it needs proof of leadership before hiring anyone internally. To find the right person for the senior management position , every candidate must undertake a project that involves a team of five. Whoever leads the winning team will get the senior managerial position.

This example shows a logical conclusion that is reached by your organization’s human resource department. In this case, your HR department has utilized logical thinking to determine the best internal candidate for the senior manager position.

It could be summarized as follows:

Statement 1: People with excellent leadership skills that produce winning teams make great senior managers. Statement 2: Candidate A is an excellent leader that has produced a winning team. Conclusion: Candidate A will make an excellent senior manager .

A marketing company researches working women on behalf of one of their clients – a robotics company. They find out that these women feel overwhelmed with responsibilities at home and in the workplace. As a result, they do not have enough time to clean, take care of their children, and stay productive in the workplace. A robotics company uses this research to create a robot cleaner that can be operated remotely . Then they advertise this cleaner specifically to working women with the tag line, “Working women can do it all with a little bit of help.” As a result of this marketing campaign, their revenues double within a year.

This example shows a logical conclusion reached by a robotics company after receiving the results of marketing research on working women. In this case, logical thinking has enabled the company to come up with a new marketing strategy for their cleaning product.

Statement 1: Working women struggle to keep their homes clean. Statement 2: Robot cleaners can take over cleaning duties for women who struggle to keep their homes clean. Conclusion: Robot cleaner can help working women keep their homes clean.

CalcX. Inc. has created a customer survey concerning its new finance software. The goal of the survey is to determine what customers like best about the software. After reading through over 100 customer reviews and ratings, it emerges that 60% of customers love the new user interface because it’s easy to navigate. CalcX. Inc. then decides to improve its marketing strategy. It decides to train every salesperson to talk about the easy navigation feature and how superior it is to the competition. So, every time a client objects to the price, the sales rep could admit that it is expensive, but the excellent user interface makes up for the price. At the end of the year, it emerges that this strategy has improved sales revenues by 10%.

The above example shows how logical thinking has helped CalcX. Sell more software and improve its bottom line.

Statement 1: If the majority of customers like a particular software feature, then sales reps should use it to overcome objections and increase revenues. Statement 2: 60% of the surveyed customers like the user interface of the new software, and; they think it makes navigation easier. Conclusion: The sales reps should market the new software’s user interface and the fact that it is easy to navigate to improve the company’s bottom line.

A political candidate hires a focus group to discuss hot-button issues they feel strongly about. It emerges that the group is torn on sexual reproductive health issues, but most support the issue of internal security . However, nearly everyone is opposed to the lower wages being paid due to the current economic crisis. Based on the results of this research, the candidate decides to focus on improving the economy and security mechanisms in the country. He also decides to let go of the sexual productive health issues because it would potentially cause him to lose some support.

In this case, the political candidate has made logical conclusions on what topics he should use to campaign for his seat with minimal controversies so that he doesn’t lose many votes.

This situation could be summarized as follows:

Statement 1: Most people find sexual reproductive health issues controversial and cannot agree. Statement 2: Most people feel that the internal security of the country is in jeopardy and something should be done about it. Statement 3: Most people want higher wages and an improved economy. Statement 4: Political candidates who want to win must avoid controversy and speak up on things that matter to people. Conclusion: To win, political candidates must focus on higher wages, an improved economy, and the internal security of the country while avoiding sexual reproductive health matters.

Deductive reasoning is an aspect of logical reasoning. It is a top-down reasoning approach that enables you to form a specific logical conclusion based on generalities. Therefore, you can use one or more statements, usually referred to as premises, to conclude something.

For example:

Statement 1: All mothers are women Statement 2: Daisy is a mother. Conclusion: Daisy is a woman.

Based on the above examples, all mothers are classified as women, and since Daisy is a mother, then it’s logical to deduce that she is a woman too.

It’s worth noting though, that deductive reasoning does not always produce an accurate conclusion based on reality.

Statement 1: All caregivers in this room are nurses. Statement 2: This dog, Tom, is a caregiver . Conclusion: This dog, Tom, is a nurse .

From the above example, we have deduced that Tom, the dog, is a nurse simply because the first statement stated that all caregivers are nurses. And yet, in reality, we know that dogs cannot be nurses. They do not have the mental capacity to become engaged in the profession.

For this reason, you must bear in mind that an argument can be validly based on the conditions but it can also be unsound if some statements are based on a fallacy.

Since logical thinking is so important in the workplace, most job interviewers will want to see you demonstrate this skill at the job interview. It is very important to keep in mind your logical thinking skills when you talk about yourself at the interview.

There are many ways in which an interviewer may ask you to demonstrate your logical thinking skills. For example:

You may have to solve an example problem. If the interviewer provides you a problem similar to one you might find at your job, make sure to critically analyze the problem to deduce a solution.

You may be asked about a previous problem or conflict you had to solve. This classic question provides you the opportunity to show your skills in action, so make sure to highlight the objectivity and logic of your problem solving.

Show your logic when talking about yourself. When given the opportunity to talk about yourself, highlight how logic comes into play in your decision making. This could be in how you picked the job position, why you choose your career or education, or what it is about yourself that makes you a great candidate.

Why is it important to think logically?

It’s important to think logically because it allows you to analyze a situation and come up with a logical solution. It allows for you to reason through the important decisions and solve problems with a better understanding of what needs to be done. This is necessary for developing a strong career.

Why is logic important?

Logic is important because it helps develop critical thinking skills. Critical thinking skills are important because they help you analyze and evaluate a problem before you make a decision. It also helps you improve your problem-solving skills to allow you to make better decisions.

How do you improve your logical thinking skills?

When improving your logical thinking skills make sure you spend time on a creative hobby and practice questioning. Creative hobbies can help reduce stress levels, and lower stress leads to having an easier time focusing on tasks and making logical thinking. Creative hobbies can include things like drawing, painting, and writing.

Another way to improve your logical thinking is to start asking questions about things. Asking questions allows for you to discover new things and learn about new topics you may not have thought about before.

What are logical thinking skills you need to succeed at work?

There are many logical thinking skills you need to succeed in the workplace. Our top four picks include:

Observation

Active Listening

Problem-solving

Logical thinking skills are valuable skills to have. You need to develop them so that you can become an asset to any organization that hires you. Be sure to include them in your resume and cover letter .

And if you make it to the interview, also ensure that you highlight these skills. You can do all this by highlighting the career accomplishments that required you to use logical thinking in the workplace.

It’s Your Yale – Consider Critical Thinking Skills to Articulate Your Work Quality

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Dheanisa Prachma Maharani , Ali Mahmudi; How is the relation between problem solving ability and logical thinking ability?. AIP Conf. Proc. 8 December 2022; 2575 (1): 050007. https://doi.org/10.1063/5.0107931

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Education is primarily aimed at forming individuals to be able to think and solve problems well. There are various general process in problem solving. One of these process is logical thinking. This underlies that problem solving ability and logical thinking ability is important to developed students can solve problems both in mathematical problems and everyday problems. Therefore, this study discussed problem solving ability and logical thinking ability and their relationship. This research used literature and library studies. Based on the results of this discussion, it was found that the ability problem solving ability has a relationship with logical thinking ability when viewed from indicators and process of thinking. In other words, we can use instruments that measure logical thinking ability to measure problem solving ability.

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How is logical thinking important in problem-solving?

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Logical thinking is a cognitive operation in which decisions are made based on sound reasoning principles. It is the ability to critically analyze events, evaluate information, and reach decisions methodically and rationally. Logical thinking emphasizes simplicity, consistency, and clarity in intellectual processes.

Logical thinking and problem-solving

Logical thinking is an essential skill for problem-solving, especially in complex and challenging situations at work. Logical thinking is a cornerstone of effective problem-solving for several reasons:

Structured problem analysis: Logical thinking aids in the breakdown of a problem into more manageable parts that are smaller. Using logic, we may break down an extensive problem into simpler components, which makes it easier to grasp and address.

Identification of cause and effect: Using logic is essential for determining linkages between various issue parts. Understanding cause and effect allows us to determine the fundamental source of a problem, which is critical for finding a successful solution.

Development of step-by-step solutions: Critical thinking guides the creation of a step-by-step procedure for solving an issue. It employs sequential reasoning, with each step built on the rational progression of the previous one, guaranteeing that the answer is cohesive and successful.

Critical evaluation of potential solutions: Logic evaluates the merits and demerits of different potential solutions. This involves weighing evidence, assessing the validity of arguments, and making decisions based on rational analysis rather than emotion or bias.

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IT Careers: Why Tech-Savvy Problem Solvers Thrive In This Industry

IT Problem Solvers must be able to think quickly and critically in order to identify and solve IT issues. The younger generation raised in a digital world are tech-savvy and are more inclined toward a career in technology. They may have used IT as fun activities, hobbies, schoolwork, or learning experiences with their parents and friends. Once they bond to IT in school, the tech-savvy students adapt and progress further than other students. Contact ITI Technical College today for more information .

There is a huge demand in business and industry for not only IT specialists but also IT problem solvers. The IT people who are best at identifying and solving problems are the tech-savvy ones. Many companies depend on professionals that can propel them ahead of the competition. They need innovators who can energize other IT workers to create new products and services to improve the bottom line. Tech-savvy problem solvers are the answer to reach these goals.

IT Problems Solvers Are In Demand

According to Recruiter.com , the most current in-demand skill for employees is problem-solving. There are three typical human approaches to problem-solving:

• Systematic – These people evaluate the problems and all potential solutions. They consider the future and put processes in place to see the problems don’t occur again.
• Inconsistent – Those who use this approach go with their gut feelings. They should be more consistent than hap-hazard. Their problem-solving skills are up and down.
• Intuitive – People who use this approach apply their intuition to solve problems. They do not see problems as opportunities for positive change. Like the inconsistent person, their approach works sometimes and sometimes not.

If you can master the systematic approach , you are the tech-savvy IT problem solver that employers are looking for. You can also improve your skills by earning an Associate Degree .

The Business World Is So Competitive

It’s an understatement to say today’s business world is competitive. Companies are looking for employees with innovative ideas and approaches to their challenges and problems, especially in technology. Tech-savvy workers are highly sought after and tech-savvy problem solvers are needed even more.

Tech-savvy people are typically self-starters who thrive on new challenges that are difficult for others to solve. They have the knowledge, skills, creativity, and analytical skills to have that ‘systematic’ approach to business situations. They can spot some potential problems before others see them and can act on them with or without supervision.

Tech-savvy employees come from all ages and backgrounds. Their contributions to companies’ bottom lines, reputations, and progress are invaluable. These employees typically seek new ways to cultivate a higher level of their skills.

How to Cultivate a Higher Level of Problem-Solving Skills

• Conduct more individual research
• See what competitors are doing
• Collaborate more with co-workers
• Brainstorm solutions
• Create spider diagrams
• Develop more analytical skills

Problem-solving skills are an asset at any company or in any industry. One approach to improve yours is to mimic a person successful at finding answers. Some simple steps to take with the above concepts are defining the problem and asking why it occurred. Next, brainstorm a list of solutions and explore and learn from each of them. Choose the one or one with the most benefits. Do not discard your possible list of solutions because a different one may be next week’s answer.

You May Be Able to Pick Your Company

By improving your problem-solving skills to a higher level, you may be the next person hired to meet industry demands. Tech-savvy problem solvers have a leg up on the average information technology applicant. Those who stress this skill on their resumes will get noticed and their resumes will be put in the ‘interview lineup.’

IT pros that change jobs also have a great advantage over other applicants when their current employer gives them an excellent rating as problem solvers. In essence, those who are prepared with the right skills and attitude may be able to pick the company they want. Become a tech-savvy problem solver and become one of these people. Call us today at (877) 591-1070 for more information

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Critical thinking and problem-solving, jump to: , what is critical thinking, characteristics of critical thinking, why teach critical thinking.

• Teaching Strategies to Help Promote Critical Thinking Skills

References and Resources

When examining the vast literature on critical thinking, various definitions of critical thinking emerge. Here are some samples:

• "Critical thinking is the intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action" (Scriven, 1996).
• "Most formal definitions characterize critical thinking as the intentional application of rational, higher order thinking skills, such as analysis, synthesis, problem recognition and problem solving, inference, and evaluation" (Angelo, 1995, p. 6).
• "Critical thinking is thinking that assesses itself" (Center for Critical Thinking, 1996b).
• "Critical thinking is the ability to think about one's thinking in such a way as 1. To recognize its strengths and weaknesses and, as a result, 2. To recast the thinking in improved form" (Center for Critical Thinking, 1996c).

Perhaps the simplest definition is offered by Beyer (1995) : "Critical thinking... means making reasoned judgments" (p. 8). Basically, Beyer sees critical thinking as using criteria to judge the quality of something, from cooking to a conclusion of a research paper. In essence, critical thinking is a disciplined manner of thought that a person uses to assess the validity of something (statements, news stories, arguments, research, etc.).

Back        

Wade (1995) identifies eight characteristics of critical thinking. Critical thinking involves asking questions, defining a problem, examining evidence, analyzing assumptions and biases, avoiding emotional reasoning, avoiding oversimplification, considering other interpretations, and tolerating ambiguity. Dealing with ambiguity is also seen by Strohm & Baukus (1995) as an essential part of critical thinking, "Ambiguity and doubt serve a critical-thinking function and are a necessary and even a productive part of the process" (p. 56).

Another characteristic of critical thinking identified by many sources is metacognition. Metacognition is thinking about one's own thinking. More specifically, "metacognition is being aware of one's thinking as one performs specific tasks and then using this awareness to control what one is doing" (Jones & Ratcliff, 1993, p. 10 ).

In the book, Critical Thinking, Beyer elaborately explains what he sees as essential aspects of critical thinking. These are:

• Dispositions: Critical thinkers are skeptical, open-minded, value fair-mindedness, respect evidence and reasoning, respect clarity and precision, look at different points of view, and will change positions when reason leads them to do so.
• Criteria: To think critically, must apply criteria. Need to have conditions that must be met for something to be judged as believable. Although the argument can be made that each subject area has different criteria, some standards apply to all subjects. "... an assertion must... be based on relevant, accurate facts; based on credible sources; precise; unbiased; free from logical fallacies; logically consistent; and strongly reasoned" (p. 12).
• Argument: Is a statement or proposition with supporting evidence. Critical thinking involves identifying, evaluating, and constructing arguments.
• Reasoning: The ability to infer a conclusion from one or multiple premises. To do so requires examining logical relationships among statements or data.
• Point of View: The way one views the world, which shapes one's construction of meaning. In a search for understanding, critical thinkers view phenomena from many different points of view.
• Procedures for Applying Criteria: Other types of thinking use a general procedure. Critical thinking makes use of many procedures. These procedures include asking questions, making judgments, and identifying assumptions.

Oliver & Utermohlen (1995) see students as too often being passive receptors of information. Through technology, the amount of information available today is massive. This information explosion is likely to continue in the future. Students need a guide to weed through the information and not just passively accept it. Students need to "develop and effectively apply critical thinking skills to their academic studies, to the complex problems that they will face, and to the critical choices they will be forced to make as a result of the information explosion and other rapid technological changes" (Oliver & Utermohlen, p. 1 ).

As mentioned in the section, Characteristics of Critical Thinking , critical thinking involves questioning. It is important to teach students how to ask good questions, to think critically, in order to continue the advancement of the very fields we are teaching. "Every field stays alive only to the extent that fresh questions are generated and taken seriously" (Center for Critical Thinking, 1996a ).

Beyer sees the teaching of critical thinking as important to the very state of our nation. He argues that to live successfully in a democracy, people must be able to think critically in order to make sound decisions about personal and civic affairs. If students learn to think critically, then they can use good thinking as the guide by which they live their lives.

Teaching Strategies to Help Promote Critical Thinking

The 1995, Volume 22, issue 1, of the journal, Teaching of Psychology , is devoted to the teaching critical thinking. Most of the strategies included in this section come from the various articles that compose this issue.

• CATS (Classroom Assessment Techniques): Angelo stresses the use of ongoing classroom assessment as a way to monitor and facilitate students' critical thinking. An example of a CAT is to ask students to write a "Minute Paper" responding to questions such as "What was the most important thing you learned in today's class? What question related to this session remains uppermost in your mind?" The teacher selects some of the papers and prepares responses for the next class meeting.
• Cooperative Learning Strategies: Cooper (1995) argues that putting students in group learning situations is the best way to foster critical thinking. "In properly structured cooperative learning environments, students perform more of the active, critical thinking with continuous support and feedback from other students and the teacher" (p. 8).
• Case Study /Discussion Method: McDade (1995) describes this method as the teacher presenting a case (or story) to the class without a conclusion. Using prepared questions, the teacher then leads students through a discussion, allowing students to construct a conclusion for the case.
• Using Questions: King (1995) identifies ways of using questions in the classroom:
• Reciprocal Peer Questioning: Following lecture, the teacher displays a list of question stems (such as, "What are the strengths and weaknesses of...). Students must write questions about the lecture material. In small groups, the students ask each other the questions. Then, the whole class discusses some of the questions from each small group.
• Reader's Questions: Require students to write questions on assigned reading and turn them in at the beginning of class. Select a few of the questions as the impetus for class discussion.
• Conference Style Learning: The teacher does not "teach" the class in the sense of lecturing. The teacher is a facilitator of a conference. Students must thoroughly read all required material before class. Assigned readings should be in the zone of proximal development. That is, readings should be able to be understood by students, but also challenging. The class consists of the students asking questions of each other and discussing these questions. The teacher does not remain passive, but rather, helps "direct and mold discussions by posing strategic questions and helping students build on each others' ideas" (Underwood & Wald, 1995, p. 18 ).
• Use Writing Assignments: Wade sees the use of writing as fundamental to developing critical thinking skills. "With written assignments, an instructor can encourage the development of dialectic reasoning by requiring students to argue both [or more] sides of an issue" (p. 24).
• Written dialogues: Give students written dialogues to analyze. In small groups, students must identify the different viewpoints of each participant in the dialogue. Must look for biases, presence or exclusion of important evidence, alternative interpretations, misstatement of facts, and errors in reasoning. Each group must decide which view is the most reasonable. After coming to a conclusion, each group acts out their dialogue and explains their analysis of it.
• Spontaneous Group Dialogue: One group of students are assigned roles to play in a discussion (such as leader, information giver, opinion seeker, and disagreer). Four observer groups are formed with the functions of determining what roles are being played by whom, identifying biases and errors in thinking, evaluating reasoning skills, and examining ethical implications of the content.
• Ambiguity: Strohm & Baukus advocate producing much ambiguity in the classroom. Don't give students clear cut material. Give them conflicting information that they must think their way through.
• Angelo, T. A. (1995). Beginning the dialogue: Thoughts on promoting critical thinking: Classroom assessment for critical thinking. Teaching of Psychology, 22(1), 6-7.
• Beyer, B. K. (1995). Critical thinking. Bloomington, IN: Phi Delta Kappa Educational Foundation.
• Center for Critical Thinking (1996a). The role of questions in thinking, teaching, and learning. [On-line]. Available HTTP: http://www.criticalthinking.org/University/univlibrary/library.nclk
• Center for Critical Thinking (1996b). Structures for student self-assessment. [On-line]. Available HTTP: http://www.criticalthinking.org/University/univclass/trc.nclk
• Center for Critical Thinking (1996c). Three definitions of critical thinking [On-line]. Available HTTP: http://www.criticalthinking.org/University/univlibrary/library.nclk
• Cooper, J. L. (1995). Cooperative learning and critical thinking. Teaching of Psychology, 22(1), 7-8.
• Jones, E. A. & Ratcliff, G. (1993). Critical thinking skills for college students. National Center on Postsecondary Teaching, Learning, and Assessment, University Park, PA. (Eric Document Reproduction Services No. ED 358 772)
• King, A. (1995). Designing the instructional process to enhance critical thinking across the curriculum: Inquiring minds really do want to know: Using questioning to teach critical thinking. Teaching of Psychology, 22 (1) , 13-17.
• McDade, S. A. (1995). Case study pedagogy to advance critical thinking. Teaching Psychology, 22(1), 9-10.
• Oliver, H. & Utermohlen, R. (1995). An innovative teaching strategy: Using critical thinking to give students a guide to the future.(Eric Document Reproduction Services No. 389 702)
• Robertson, J. F. & Rane-Szostak, D. (1996). Using dialogues to develop critical thinking skills: A practical approach. Journal of Adolescent & Adult Literacy, 39(7), 552-556.
• Scriven, M. & Paul, R. (1996). Defining critical thinking: A draft statement for the National Council for Excellence in Critical Thinking. [On-line]. Available HTTP: http://www.criticalthinking.org/University/univlibrary/library.nclk
• Strohm, S. M., & Baukus, R. A. (1995). Strategies for fostering critical thinking skills. Journalism and Mass Communication Educator, 50 (1), 55-62.
• Underwood, M. K., & Wald, R. L. (1995). Conference-style learning: A method for fostering critical thinking with heart. Teaching Psychology, 22(1), 17-21.
• Wade, C. (1995). Using writing to develop and assess critical thinking. Teaching of Psychology, 22(1), 24-28.

Other Reading

• Bean, J. C. (1996). Engaging ideas: The professor's guide to integrating writing, critical thinking, & active learning in the classroom. Jossey-Bass.
• Bernstein, D. A. (1995). A negotiation model for teaching critical thinking. Teaching of Psychology, 22(1), 22-24.
• Carlson, E. R. (1995). Evaluating the credibility of sources. A missing link in the teaching of critical thinking. Teaching of Psychology, 22(1), 39-41.
• Facione, P. A., Sanchez, C. A., Facione, N. C., & Gainen, J. (1995). The disposition toward critical thinking. The Journal of General Education, 44(1), 1-25.
• Halpern, D. F., & Nummedal, S. G. (1995). Closing thoughts about helping students improve how they think. Teaching of Psychology, 22(1), 82-83.
• Isbell, D. (1995). Teaching writing and research as inseparable: A faculty-librarian teaching team. Reference Services Review, 23(4), 51-62.
• Jones, J. M. & Safrit, R. D. (1994). Developing critical thinking skills in adult learners through innovative distance learning. Paper presented at the International Conference on the practice of adult education and social development. Jinan, China. (Eric Document Reproduction Services No. ED 373 159)
• Sanchez, M. A. (1995). Using critical-thinking principles as a guide to college-level instruction. Teaching of Psychology, 22(1), 72-74.
• Spicer, K. L. & Hanks, W. E. (1995). Multiple measures of critical thinking skills and predisposition in assessment of critical thinking. Paper presented at the annual meeting of the Speech Communication Association, San Antonio, TX. (Eric Document Reproduction Services No. ED 391 185)
• Terenzini, P. T., Springer, L., Pascarella, E. T., & Nora, A. (1995). Influences affecting the development of students' critical thinking skills. Research in Higher Education, 36(1), 23-39.

On the Internet

• Carr, K. S. (1990). How can we teach critical thinking. Eric Digest. [On-line]. Available HTTP: http://ericps.ed.uiuc.edu/eece/pubs/digests/1990/carr90.html
• The Center for Critical Thinking (1996). Home Page. Available HTTP: http://www.criticalthinking.org/University/
• Ennis, Bob (No date). Critical thinking. [On-line], April 4, 1997. Available HTTP: http://www.cof.orst.edu/cof/teach/for442/ct.htm
• Montclair State University (1995). Curriculum resource center. Critical thinking resources: An annotated bibliography. [On-line]. Available HTTP: http://www.montclair.edu/Pages/CRC/Bibliographies/CriticalThinking.html
• No author, No date. Critical Thinking is ... [On-line], April 4, 1997. Available HTTP: http://library.usask.ca/ustudy/critical/
• Sheridan, Marcia (No date). Internet education topics hotlink page. [On-line], April 4, 1997. Available HTTP: http://sun1.iusb.edu/~msherida/topics/critical.html

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Language is a defining characteristic of our species, but the function, or functions, that it serves has been debated for centuries. Here we bring recent evidence from neuroscience and allied disciplines to argue that in modern humans, language is a tool for communication, contrary to a prominent view that we use language for thinking. We begin by introducing the brain network that supports linguistic ability in humans. We then review evidence for a double dissociation between language and thought, and discuss several properties of language that suggest that it is optimized for communication. We conclude that although the emergence of language has unquestionably transformed human culture, language does not appear to be a prerequisite for complex thought, including symbolic thought. Instead, language is a powerful tool for the transmission of cultural knowledge; it plausibly co-evolved with our thinking and reasoning capacities, and only reflects, rather than gives rise to, the signature sophistication of human cognition.

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Barham, L. & Everett, D. Semiotics and the origin of language in the Lower Palaeolithic. J. Archaeol. Method Theory 28 , 535–579 (2021).

Article   Google Scholar  

Hockett, C. F. The origin of speech. Sci. Am. 203 , 88–97 (1960). A classic overview of the relationship between key features of human language and communication systems found in other species, with a focus on distinctive and shared properties .

Jackendoff, R. & Pinker, S. The faculty of language: what’s special about it? Cognition 95 , 201–236 (2005).

Article   PubMed   Google Scholar  

Hurford, J. R. Language in the Light of Evolution: Volume 1, The Origins of Meaning (Oxford Univ. Press, 2007).

Kirby, S., Cornish, H. & Smith, K. Cumulative cultural evolution in the laboratory: an experimental approach to the origins of structure in human language. Proc. Natl Acad. Sci. USA 105 , 10681–10686 (2008). This behavioural investigation introduces an experimental paradigm based on iterated learning of artificial languages for studying the cultural evolution of language; the findings suggest that languages evolve to maximize their transmissibility by becoming easier to learn and more structured .

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Seyfarth, R. M. & Cheney, D. L. The Social Origins of Language (Princeton Univ. Press, 2018).

Gibson, E. et al. How efficiency shapes human language. Trends Cogn. Sci. 23 , 389–407 (2019).

Chomsky, N. The Minimalist Program (MIT Press, 1995).

Carruthers, P. The cognitive functions of language. Behav. Brain Sci. 25 , 657–674 (2002). This comprehensive review discusses diverse language-for-thought views and puts forward a specific proposal whereby language has a critical role in cross-domain integration .

Gentner, D. & Goldin-Meadow, S. Language in Mind: Advances in the Study of Language and Thought (MIT Press, 2003).

Majid, A., Bowerman, M., Kita, S., Haun, D. B. & Levinson, S. C. Can language restructure cognition? The case for space. Trends Cogn. Sci. 8 , 108–114 (2004).

Vygotsky, L. S. Thought and Language (MIT Press, 2012).

Lupyan, G. The centrality of language in human cognition. Lang. Learn. 66 , 516–553 (2016).

Davidson, D. in Mind and Language (ed. Guttenplan, S.) 1975–1977 (Oxford Univ. Press, 1975).

Dummett, M. Origins of Analytical Philosophy (Harvard Univ. Press, 1994).

Gleitman, L. & Papafragou, A. in The Cambridge Handbook of Thinking and Reasoning (eds Holyoak, K. J. & Morrison, R. G.) 633–661 (Cambridge Univ. Press, 2005).

de Villiers, J. in Understanding Other Minds: Perspectives from Developmental Cognitive Neuroscience (eds Baron-Cohen, S. et al.) 83–123 (Oxford Univ. Press, 2000).

Gentner, D. in Language in Mind: Advances in the Study of Language and Thought (eds Gentner, D. & Goldin-Meadow, S.) 3–14 (MIT Press, 2003). This position piece articulates one version of a language-for-thought hypothesis, whereby human intelligence is due to a combination of our analogical reasoning ability, possession of symbolic representations, and the ability of relational language to improve analogical reasoning abilities .

Buller, D. J. Adapting Minds: Evolutionary Psychology and the Persistent Quest for Human Nature (MIT Press, 2005).

Gould, S. J. & Vrba, E. S. Exaptation—a missing term in the science of form. Paleobiology 8 , 4–15 (1982).

Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27 , 379–423 (1948). This article introduces a formal framework for systems of information transfer, with core concepts such as channel capacity, and lays a foundation for the field of information theory .

Article   MathSciNet   Google Scholar  

Goldberg, A. E. Constructions: A Construction Grammar Approach to Argument Structure (Univ. Chicago Press, 1995).

Jackendoff, R. Foundations of Language: Brain, Meaning, Grammar, Evolution (Oxford Univ. Press, 2002).

Geschwind, N. The organization of language and the brain: language disorders after brain damage help in elucidating the neural basis of verbal behavior. Science 170 , 940–944 (1970).

Article   ADS   CAS   PubMed   Google Scholar  

Friederici, A. D. Towards a neural basis of auditory sentence processing. Trends Cogn. Sci. 6 , 78–84 (2002).

Bates, E. et al. Voxel-based lesion–symptom mapping. Nat. Neurosci. 6 , 448–450 (2003).

Article   CAS   PubMed   Google Scholar  

Hagoort, P. The neurobiology of language beyond single-word processing. Science 366 , 55–58 (2019).

Fedorenko, E., Ivanova, A. I. & Regev, T. I. The language network as a natural kind within the broader landscape of the human brain. Nat. Rev. Neurosci. 25 , 289–312 (2024).

Neville, H. J. et al. Cerebral organization for language in deaf and hearing subjects: biological constraints and effects of experience. Proc. Natl Acad. Sci. USA 95 , 922–929 (1998).

Fedorenko, E., Hsieh, P.-J., Nieto-Castañon, A., Whitfield-Gabrieli, S. & Kanwisher, N. A new method for fMRI investigations of language: defining ROIs functionally in individual subjects. J. Neurophysiol. 104 , 1177–1194 (2010).

Article   PubMed   PubMed Central   Google Scholar  

Vagharchakian, L., Dehaene-Lambertz, G., Pallier, C. & Dehaene, S. A temporal bottleneck in the language comprehension network. J. Neurosci. 32 , 9089–9102 (2012).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Regev, M., Honey, C. J., Simony, E. & Hasson, U. Selective and invariant neural responses to spoken and written narratives. J. Neurosci. 33 , 15978–15988 (2013).

Hu, J. et al. Precision fMRI reveals that the language-selective network supports both phrase-structure building and lexical access during language production. Cereb. Cortex 33 , 4384–4404 (2022).

Article   PubMed Central   Google Scholar  

Menenti, L., Gierhan, S. M. E., Segaert, K. & Hagoort, P. Shared language: overlap and segregation of the neuronal infrastructure for speaking and listening revealed by functional MRI. Psychol. Sci. 22 , 1173–1182 (2011). This fMRI investigation establishes that language comprehension and language production draw on the same brain areas in the left frontal and temporal cortex .

Hauser, M. D., Chomsky, N. & Fitch, W. T. The faculty of language: what is it, who has it, and how did it evolve? Science 298 , 1569–1579 (2002).

Pallier, C., Devauchelle, A. D. & Dehaene, S. Cortical representation of the constituent structure of sentences. Proc. Natl Acad. Sci. USA 108 , 2522–2527 (2011).

Bozic, M., Fonteneau, E., Su, L. & Marslen‐Wilson, W. D. Grammatical analysis as a distributed neurobiological function. Hum. Brain Mapp. 36 , 1190–1201 (2015).

Rodd, J. M., Vitello, S., Woollams, A. M. & Adank, P. Localising semantic and syntactic processing in spoken and written language comprehension: an activation likelihood estimation meta-analysis. Brain Lang. 141 , 89–102 (2015).

Blank, I., Balewski, Z., Mahowald, K. & Fedorenko, E. Syntactic processing is distributed across the language system. NeuroImage 127 , 307–323 (2016).

Fedorenko, E. et al. Neural correlate of the construction of sentence meaning. Proc. Natl Acad. Sci. USA 113 , E6256–E6262 (2016).

Nelson, M. J. et al. Neurophysiological dynamics of phrase-structure building during sentence processing. Proc. Natl Acad. Sci. USA 114 , E3669–E3678 (2017).

Fedorenko, E., Blank, I. A., Siegelman, M. & Mineroff, Z. Lack of selectivity for syntax relative to word meanings throughout the language network. Cognition 203 , 104348 (2020). This fMRI investigation establishes that every part of the language network that is sensitive to syntactic structure building is also sensitive to word meanings and comprehensively reviews literature relevant to the syntax selectivity debate .

Giglio, L., Ostarek, M. O., Weber, K. & Hagoort, P. Commonalities and asymmetries in the neurobiological infrastructure for language production and comprehension. Cereb. Cortex 32 , 1405–1418 (2022).

Heilbron, M., Armeni, K., Schoffelen, J. M., Hagoort, P. & De Lange, F. P. A hierarchy of linguistic predictions during natural language comprehension. Proc. Natl Acad. Sci. USA 119 , e2201968119 (2022).

Shain, C., Blank, I. A., Fedorenko, E., Gibson, E. & Schuler, W. Robust effects of working memory demand during naturalistic language comprehension in language-selective cortex. J. Neurosci. 42 , 7412–7430 (2022).

Desbordes, T. et al. Dimensionality and ramping: signatures of sentence integration in the dynamics of brains and deep language models. J. Neurosci. 43 , 5350–5364 (2023).

Shain, C. et al. Distributed sensitivity to syntax and semantics throughout the language network. J. Cogn. Neurosci. 22 , 1–43 (2024). This fMRI investigation establishes distributed sensitivity to cognitive demands associated with lexical access, syntactic structure building and semantic composition across the language network.

Tuckute, G. et al. Driving and suppressing the human language network using large language models. Nat. Hum. Behav. 8 , 544–561 (2024).

Gentner, D. Structure-mapping: a theoretical framework for analogy. Cogn. Sci. 7 , 155–170 (1983).

Google Scholar  

Duncan, J. How Intelligence Happens (Yale Univ. Press, 2012).

Varley, R. A., Klessinger, N. J., Romanowski, C. A. & Siegal, M. Agrammatic but numerate. Proc. Natl Acad. Sci. USA 102 , 3519–3524 (2005). Patients with acquired damage to the language network display aphasia and linguistic deficits (including severe grammatical difficulties) but perform at the level of neurotypical control participants on diverse numerical reasoning tasks .

Klessinger, N., Szczerbinski, M. & Varley, R. Algebra in a man with severe aphasia. Neuropsychologia 45 , 1642–1648 (2007).

Lecours, A. & Joanette, Y. Linguistic and other psychological aspects of paroxysmal aphasia. Brain and Language 10 , 1–23 (1980).

Kertesz, A. in Thought Without Language (ed. Weiskrantz, L.) 451–463 (Oxford Univ. Press, 1988).

Varley, R. & Siegal, M. Evidence for cognition without grammar from causal reasoning and ‘theory of mind’ in an agrammatic aphasic patient. Curr. Biol. 10 , 723–726 (2000).

Siegal, M., Varley, R. & Want, S. C. Mind over grammar: reasoning in aphasia and development. Trends Cogn. Sci. 5 , 296–301 (2001).

Varley, R. In Cognitive Bases of Science (eds Carruthers, P. et al.) 99–116 (Cambridge Univ. Press, 2002).

Woolgar, A., Duncan, J., Manes, F. & Fedorenko, E. Fluid intelligence is supported by the multiple-demand system not the language system. Nat. Hum. Behav. 2 , 200–204 (2018).

Dronkers, N. F., Ludy, C. A. & Redfern, B. B. Pragmatics in the absence of verbal language: descriptions of a severe aphasic and a language-deprived adult. J. Neurolinguistics 11 , 179–190 (1998).

Varley, R., Siegal, M. & Want, S. C. Severe impairment in grammar does not preclude theory of mind. Neurocase 7 , 489–493 (2001).

Apperly, I. A., Samson, D., Carroll, N., Hussain, S. & Humphreys, G. Intact first-and second-order false belief reasoning in a patient with severely impaired grammar. Soc. Neurosci. 1 , 334–348 (2006). A person with acquired damage to the language network and consequent aphasia exhibits linguistic deficits but performs at the level of neurotypical control participants on theory of mind tasks .

Willems, R. M., Benn, Y., Hagoort, P., Toni, I. & Varley, R. Communicating without a functioning language system: Implications for the role of language in mentalizing. Neuropsychologia 49 , 3130–3135 (2011).

Bek, J., Blades, M., Siegal, M. & Varley, R. Language and spatial reorientation: evidence from severe aphasia. J. Exp. Psychol. 36 , 646 (2010).

Caramazza, A., Berndt, R. S. & Brownell, H. H. The semantic deficit hypothesis: Perceptual parsing and object classification by aphasic patients. B. Lang. 15 , 161–189 (1982).

Article   CAS   Google Scholar  

Chertkow, H., Bub, D., Deaudon, C. & Whitehead, V. On the status of object concepts in aphasia. Brain Lang. 58 , 203–232 (1997).

Saygın, A. P., Wilson, S. M., Dronkers, N. F. & Bates, E. Action comprehension in aphasia: linguistic and non-linguistic deficits and their lesion correlates. Neuropsychologia 42 , 1788–1804 (2004).

Jefferies, E. & Lambon Ralph, M. A. Semantic impairment in stroke aphasia versus semantic dementia: a case-series comparison. Brain 129 , 2132–2147 (2006).

Dickey, M. W. & Warren, T. The influence of event-related knowledge on verb-argument processing in aphasia. Neuropsychologia 67 , 63–81 (2015).

Ivanova, A. A. et al. The language network is recruited but not required for nonverbal event semantics. Neurobiol. Lang. 2 , 176–201 (2021). In this fMRI study, semantic processing of event pictures in neurotypical individuals engages the language network, but less than verbal descriptions of the same events; however, individuals with acquired damage to the language network and consequent aphasia perform at the level of neurotypical control participants on a non-verbal semantic task .

Benn, Y. et al. The language network is not engaged in object categorization. Cereb. Cortex 33 , 10380–10400 (2023).

Varley, R. Reason without much language. Lang. Sci. 46 , 232–244 (2014).

Dehaene, S., Spelke, E., Pinel, P., Stanescu, R. & Tsivkin, S. Sources of mathematical thinking: behavioral and brain-imaging evidence. Science 284 , 970–974 (1999).

Hermer, L. & Spelke, E. Modularity and development: the case of spatial reorientation. Cognition 61 , 195–232 (1996).

Lupyan, G. Extracommunicative functions of language: verbal interference causes selective categorization impairments. Psychon. Bull. Rev. 16 , 711–718 (2009).

Braga, R. M., DiNicola, L. M., Becker, H. C. & Buckner, R. L. Situating the left-lateralized language network in the broader organization of multiple specialized large-scale distributed networks. J. Neurophysiol. 124 , 1415–1448 (2020). This fMRI investigation of the language network establishes this network as one of the intrinsic large-scale networks in the human brain, distinct from nearby cognitive networks .

Fedorenko, E. & Blank, I. A. Broca’s area is not a natural kind. Trends Cogn. Sci. 24 , 270–284 (2020).

Fedorenko, E., Behr, M. K. & Kanwisher, N. Functional specificity for high-level linguistic processing in the human brain. Proc. Natl Acad. Sci. USA 108 , 16428–16433 (2011). This fMRI investigation finds that arithmetic addition, demanding executive function tasks and music processing do not engage the language areas, thus establishing their selectivity for linguistic input over non-linguistic inputs and tasks .

Monti, M. M., Parsons, L. M. & Osherson, D. N. Thought beyond language: neural dissociation of algebra and natural language. Psychol. Sci. 23 , 914–922 (2012).

Amalric, M. & Dehaene, S. A distinct cortical network for mathematical knowledge in the human brain. NeuroImage 189 , 19–31 (2019).

Monti, M. M., Osherson, D. N., Martinez, M. J. & Parsons, L. M. Functional neuroanatomy of deductive inference: a language-independent distributed network. NeuroImage 37 , 1005–1016 (2007).

Monti, M. M., Parsons, L. M. & Osherson, D. N. The boundaries of language and thought in deductive inference. Proc. Natl Acad. Sci. USA 106 , 12554–12559 (2009). This fMRI investigation finds largely non-overlapping activations of brain regions to language processing and logical processing, thus establishing the selectivity of language areas for linguistic input over logic statements .

Ivanova, A. A. et al. Comprehension of computer code relies primarily on domain-general executive brain regions. eLife 9 , e58906 (2020).

Liu, Y. F., Kim, J., Wilson, C. & Bedny, M. Computer code comprehension shares neural resources with formal logical inference in the fronto-parietal network. eLife 9 , e59340 (2020).

Paunov, A. M., Blank, I. A. & Fedorenko, E. Functionally distinct language and theory of mind networks are synchronized at rest and during language comprehension. J. Neurophysiol. 121 , 1244–1265 (2019).

Paunov, A. M. et al. Differential tracking of linguistic vs. mental state content in naturalistic stimuli by language and theory of mind (ToM) brain networks. Neurobiol. Lang. 3 , 413–440 (2022).

Shain, C., Paunov, A., Chen, X., Lipkin, B. & Fedorenko, E. No evidence of theory of mind reasoning in the human language network. Cereb. Cortex 33 , 6299–6319 (2023).

Sueoka, Y., Paunov, A., Ivanova, A., Blank, I. A. & Fedorenko, E. The language network reliably “tracks” naturalistic meaningful non-verbal stimuli. Neurobiol. Lang. https://doi.org/10.1162/nol_a_00135 (2024).

Piaget, J. The Language and Thought of the Child (Harcourt Brace, 1926).

Gentner, D. & Loewenstein, J. in Language, Literacy, and Cognitive Development: The Development and Consequences of Symbolic Communication (eds Amsel, E. & Byrnes, J. P.) 89–126 (Lawrence Erlbaum Associates, 2002).

Appleton, M. & Reddy, V. Teaching three year‐olds to pass false belief tests: a conversational approach. Soc. Dev. 5 , 275–291 (1996).

Slaughter, V. & Gopnik, A. Conceptual coherence in the child’s theory of mind: training children to understand belief. Child Dev. 67 , 2967–2988 (1996).

Hiersche, K. J., Schettini, E., Li, J. & Saygin, Z. M. (2022). Functional dissociation of the language network and other cognition in early childhood. Preprint at bioRxiv https://doi.org/10.1101/2022.08.11.503597 (2023).

Hiersche, K. J. Functional Organization and Modularity of the Superior Temporal Lobe in Children . Masters thesis, The Ohio State University (2023).

Hall, W. C. What you don’t know can hurt you: the risk of language deprivation by impairing sign language development in deaf children. Matern. Child Health J. 21 , 961–965 (2017).

Hall, M. L., Hall, W. C. & Caselli, N. K. Deaf children need language, not (just) speech. First Lang. 39 , 367–395 (2019).

Bedny, M. & Saxe, R. Insights into the origins of knowledge from the cognitive neuroscience of blindness. Cogn. Neuropsychol. 29 , 56–84 (2012).

Grand, G., Blank, I. A., Pereira, F. & Fedorenko, E. Semantic projection recovers rich human knowledge of multiple object features from word embeddings. Nat. Hum. Behav. 6 , 975–987 (2022).

Jackendoff, R. How language helps us think. Pragmat. Cogn. 4 , 1–34 (1996).

Jackendoff. R. The User’s Guide to Meaning (MIT Press, 2012).

Curtiss, S. Genie: A Psycholinguistic Study of a Modern-day Wild Child (Academic Press, 1977).

Peterson, C. C. & Siegal, M. Representing inner worlds: theory of mind in autistic, deaf, and normal hearing children. Psychol. Sci. 10 , 126–129 (1999).

Richardson, H. et al. Reduced neural selectivity for mental states in deaf children with delayed exposure to sign language. Nat. Commun. 11 , 3246 (2020).

Spelke, E. S. What Babies Know: Core Knowledge and Composition , Vol. 1 (Oxford Univ. Press, 2022).

Cheney, D. L. & Seyfarth, R. M. How Monkeys See the World: Inside the Mind of Another Species (Univ. Chicago Press, 1990).

Herrmann, E., Call, J., Hernández-Lloreda, M. V., Hare, B. & Tomasello, M. Humans have evolved specialized skills of social cognition: the cultural intelligence hypothesis. Science 317 , 1360–1366 (2007).

Tomasello, M. & Herrmann, E. Ape and human cognition: what’s the difference? Curr. Dir. Psychol. Sci. 19 , 3–8 (2010).

Fischer, J. Monkeytalk: Inside the Worlds and Minds of Primates (Univ. Chicago Press, 2017).

Krupenye, C. & Call, J. Theory of mind in animals: current and future directions. Wiley Interdiscip. Rev. Cogn. Sci. 10 , e1503 (2019).

Shimizu, T. Why can birds be so smart? Background, significance, and implications of the revised view of the avian brain. Comparat. Cogn. Behav. Rev. 4 , 103–115 (2009).

Güntürkün, O. & Bugnyar, T. Cognition without cortex. Trends Cogn. Sci. 20 , 291–303 (2016).

Hart, B. L., Hart, L. A. & Pinter-Wollman, N. Large brains and cognition: where do elephants fit in? Neurosci. Biobehav. Rev. 32 , 86–98 (2008).

Godfrey-Smith, P. Other Minds: The Octopus and the Evolution of Intelligent Life (William Collins, 2016).

Schnell, A. K., Amodio, P., Boeckle, M. & Clayton, N. S. How intelligent is a cephalopod? Lessons from comparative cognition. Biol. Rev. 96 , 162–178 (2021).

Gallistel, C. R. Prelinguistic thought. Lang. Learn. Dev. 7 , 253–262 (2011).

Fitch, W. T. Animal cognition and the evolution of human language: why we cannot focus solely on communication. Philos. Trans. R. Soc. B 375 , 20190046 (2020).

Yamada, J. E. & Marshall, J. C. Laura: A Case Study for the Modularity of Language (MIT Press, 1990).

Rondal, J. A. Exceptional Language Development in Down Syndrome (Cambridge Univ. Press, 1995).

Bellugi, U., Lichtenberger, L., Jones, W., Lai, Z. & St George, M. The neurocognitive profile of Williams syndrome: a complex pattern of strengths and weaknesses. J. Cogn. Neurosci. 12 , 7–29 (2000).

Little, B. et al. Language in schizophrenia and aphasia: the relationship with non-verbal cognition and thought disorder. Cogn. Neuropsychiatry 24 , 389–405 (2019).

Mahowald, K. et al. Dissociating language and thought in large language models. Trends Cogn. Sci. 28 , 517–540(2024).

Chomsky, N., Belleti, A. & Rizzi, L. in On Nature and Language (eds Belleti, A. & Rizzi, L.) 92–161 (Cambridge Univ. Press, 2002).

Schwartz, J. L., Boë, L. J., Vallée, N. & Abry, C. The dispersion–focalization theory of vowel systems. J. Phonetics 25 , 255–286 (1997).

Diehl, R. L. Acoustic and auditory phonetics: the adaptive design of speech sound systems. Philos. Trans. R. Soc. B 363 , 965–978 (2008).

Everett, C., Blasi, D. E. & Roberts, S. G. Climate, vocal folds, and tonal languages: Connecting the physiological and geographic dots. Proc. Natl Acad. Sci. USA 112 , 1322–1327 (2015).

Blasi, D. E. et al. Human sound systems are shaped by post-Neolithic changes in bite configuration. Science 363 , eaav3218 (2019).

Dautriche, I., Mahowald, K., Gibson, E., Christophe, A. & Piantadosi, S. T. Words cluster phonetically beyond phonotactic regularities. Cognition 163 , 128–145 (2017).

Piantadosi, S. T., Tily, H. & Gibson, E. Word lengths are optimized for efficient communication. Proc. Natl Acad. Sci. USA 108 , 3526–3529 (2011).

Levelt, W. J. Speaking: From Intention to Articulation (MIT Press, 1993).

Kemp, C. & Regier, T. Kinship categories across languages reflect general communicative principles. Science 336 , 1049–1054 (2012). This study provides a computational demonstration that the kinship systems across world’s languages trade off between simplicity and informativeness in a near-optimal way, and argue that these principles also characterize other category systems .

Gibson, E. et al. Color naming across languages reflects color use. Proc. Natl Acad. Sci. USA 114 , 10785–10790 (2017).

Zaslavsky, N., Kemp, C., Regier, T. & Tishby, N. Efficient compression in color naming and its evolution. Proc. Natl Acad. Sci. USA 115 , 7937–7942 (2018).

Kemp, C., Gaby, A. & Regier, T. Season naming and the local environment. Proc. 41st Annual Meeting of the Cognitive Science Society 539–545 (2019).

Xu, Y., Liu, E. & Regier, T. Numeral systems across languages support efficient communication: From approximate numerosity to recursion. Open Mind 4 , 57–70 (2020).

Denić, M., Steinert-Threlkeld, S. & Szymanik, J. Complexity/informativeness trade-off in the domain of indefinite pronouns. Semant. Linguist. Theor. 30 , 166–184 (2021).

Mollica, F. et al. The forms and meanings of grammatical markers support efficient communication. Proc. Natl Acad. Sci. USA 118 , e2025993118 (2021).

van de Pol, I., Lodder, P., van Maanen, L., Steinert-Threlkeld, S. & Szymanik, J. Quantifiers satisfying semantic universals have shorter minimal description length. Cognition 232 , 105150 (2023).

Clark, H. H. in Context in Language Learning and Language Understanding (eds Malmkj’r, K. & Williams, J.) 63–87) (Cambridge Univ. Press, 1998).

Winter, B., Perlman, M. & Majid, A. Vision dominates in perceptual language: English sensory vocabulary is optimized for usage. Cognition 179 , 213–220 (2018).

von Humboldt, W. Uber die Verschiedenheit des Menschlichen Sprachbaues (1836).

Hurford, J. R. Linguistic Evolution Through Language Acquisition: Formal and Computational Models (ed. Briscoe, E.) 301–344 (Cambridge Univ. Press, 2002).

Smith, K., Brighton, H. & Kirby, S. Complex systems in language evolution: the cultural emergence of compositional structure. Adv. Complex Syst. 6 , 537–558 (2003).

Piantadosi, S. T. & Fedorenko, E. Infinitely productive language can arise from chance under communicative pressure. J. Lang. Evol. 2 , 141–147 (2017).

Gibson, E. Linguistic complexity: locality of syntactic dependencies. Cognition 68 , 1–76 (1998).

Lewis, R. L., Vasishth, S. & Van Dyke, J. A. Computational principles of working memory in sentence comprehension. Trends Cogn. Sci. 10 , 447–454 (2006).

Liu, H. Dependency distance as a metric of language comprehension difficulty. J. Cogn. Sci. 9 , 151–191 (2008).

ADS   CAS   Google Scholar  

Futrell, R., Mahowald, K. & Gibson, E. Large-scale evidence of dependency length minimization in 37 languages. Proc. Natl Acad. Sci. USA 112 , 10336–10341 (2015). This investigation of syntactic dependency lengths across 37 diverse languages suggests that dependencies are predominantly local cross-linguistically, presumably because non-local dependencies are cognitively costly in both production and comprehension .

Dryer, M. S. The Greenbergian word order correlations. Language 68 , 81–138 (1992).

Hahn, M., Jurafsky, D. & Futrell, R. Universals of word order reflect optimization of grammars for efficient communication. Proc. Natl Acad. Sci. USA 117 , 2347–2353 (2020).

Goldin-Meadow, S., Wing, C. S., Özyürek, A. & Mylander, C. The natural order of events: how speakers of different languages represent events nonverbally. Proc. Natl Acad. Sci. USA 105 , 9163–9168 (2008).

Senghas, A., Kita, S. & Ozyürek, A. Children creating core properties of language: evidence from an emerging sign language in Nicaragua. Science 305 , 1779–1782 (2004).

Sandler, W., Meir, I., Padden, C. & Aronoff, M. The emergence of grammar: systematic structure in a new language. Proc. Natl Acad. Sci. USA 102 , 2661–2665 (2005).

Gibson, E. et al. A noisy-channel account of crosslinguistic word-order variation. Psychol. Sci. 24 , 1079–1088 (2013).

Levy, R. A noisy-channel model of human sentence comprehension under uncertain input. In Proc. Conference on Empirical Methods in Natural Language Processing 234–243 (2008).

Gibson, E., Bergen, L. & Piantadosi, S. T. Rational integration of noisy evidence and prior semantic expectations in sentence interpretation. Proc. Natl Acad. Sci. USA 110 , 8051–8056 (2013). This behavioural investigation demonstrates that language comprehension is robust to noise: in the presence of corrupt linguistic input, listeners and readers rely on a combination of prior expectations about messages that are likely to be communicated and knowledge of how linguistic signals can get corrupted by noise .

Futrell, R., Levy, R. P. & Gibson, E. Dependency locality as an explanatory principle for word order. Language 96 , 371–412 (2020).

Hahn, M. & Xu, Y. Crosslinguistic word order variation reflects evolutionary pressures of dependency and information locality. Proc. Natl Acad. Sci. USA 119 , e2122604119 (2022).

Hahn, M., Futrell, R., Levy, R. & Gibson, E. A resource-rational model of human processing of recursive linguistic structure. Proc. Natl Acad. Sci. USA 119 , e2122602119 (2022).

Piantadosi, S. T., Tily, H. & Gibson, E. The communicative function of ambiguity in language. Cognition 122 , 280–291 (2012).

Quijada, J. A grammar of the Ithkuil language—introduction. ithkuil.net https://ithkuil.net/00_intro.html (accessed 27 February 2022).

Srinivasan, M. & Rabagliati, H. The implications of polysemy for theories of word learning. Child Dev. Perspect. 15 , 148–153 (2021).

Bizzi, E. Motor control revisited: a novel view. Curr. Trends Neurol. 10 , 75–80 (2016).

Darwin, C. On the Origin of Species–A Facsimile of the First Edition (Harvard Univ. Press, 1964).

Herculano-Houzel, S. The remarkable, yet not extraordinary, human brain as a scaled-up primate brain and its associated cost. Proc. Natl Acad. Sci. USA 109 , 10661–10668 (2012).

White, L. T. The historical roots of our ecologic crisis. Science 155 , 1203–1207 (1967).

Article   ADS   PubMed   Google Scholar  

King, M. C. & Wilson, A. C. Evolution at two levels in humans and chimpanzees. Science 188 , 107–116 (1975).

Chimpanzee Sequencing and Analysis Consortium. Initial sequence of the chimpanzee genome and comparison with the human genome. Nature 437 , 69–87 (2005).

Buckner, R. L. & Krienen, F. M. The evolution of distributed association networks in the human brain. Trends Cogn. Sci. 17 , 648–665 (2013). This review presents the evidence for the disproportionate expansion of the association cortex relative to other brain areas in humans .

Duncan, J., Assem, M. & Shashidhara, S. Integrated intelligence from distributed brain activity. Trends Cogn. Sci. 24 , 838–852 (2020).

Saxe, R. & Kanwisher, N. People thinking about thinking people: the role of the temporo-parietal junction in “theory of mind”. NeuroImage 19 , 1835–1842 (2003).

Buckner, R. L. & DiNicola, L. M. The brain’s default network: updated anatomy, physiology and evolving insights. Nat. Rev. Neurosci. 20 , 593–608 (2019).

Deen, B. & Freiwald, W. A. Parallel systems for social and spatial reasoning within the cortical apex. Preprint at bioRxiv https://doi.org/10.1101/2021.09.23.461550 (2021).

Mitchell, D. J. et al. A putative multiple-demand system in the macaque brain. J. Neurosci. 36 , 8574–8585 (2016).

Cantlon, J. & Piantadosi, S. Uniquely human intelligence arose from expanded information capacity. Nat. Rev. Psychol. 3 , 275–293 (2024).

Tomasello, M. The Cultural Origins of Human Cognition (Harvard Univ. Press, 2009).

Boyd, R., Richerson, P. J. & Henrich, J. The cultural niche: Why social learning is essential for human adaptation. Proc. Natl Acad. Sci. USA 108 , 10918–10925 (2011).

Henrich, J. The Secret of Our Success: How Culture is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter (Princeton Univ. Press, 2016).

Heyes, C. Cognitive Gadgets (Harvard Univ. Press, 2018).

Gumperz, J. J. & Levinson, S. C. (eds). Rethinking Linguistic Relativity (Cambridge Univ. Press, 1996).

Piaget, J. Language and Thought of the Child: Selected Works , Vol. 5 (Routledge, 2005).

Gleitman, L. R. & Papafragou, A. in Cambridge Handbook of Thinking and Reasoning (eds Holyoak, K. & Morrison, R.) 2nd edn (Oxford Univ. Press, 2016).

Fedorenko, E. & Varley, R. Language and thought are not the same thing: evidence from neuroimaging and neurological patients. Ann. NY Acad. Sci. 1369 , 132–153 (2016).

Gentner, D. Language as cognitive tool kit: How language supports relational thought. Am. Psychol. 71 , 650 (2016).

Frank, M. C., Everett, D. L., Fedorenko, E. & Gibson, E. Number as a cognitive technology: Evidence from Pirahã language and cognition. Cognition 108 , 819–824 (2008).

Wernicke, C. The aphasic symptom-complex: a psychological study on an anatomical basis. Arch. Neurol. 22 , 280–282 (1869).

Lichteim, L. On aphasia. Brain 7 , 433–484 (1885).

Poeppel, D., Emmorey, K., Hickok, G. & Pylkkänen, L. Towards a new neurobiology of language. J. Neurosci. 32 , 14125–14131 (2012).

Tremblay, P. & Dick, A. S. Broca and Wernicke are dead, or moving past the classic model of language neurobiology. Brain Lang. 162 , 60–71 (2016).

Hillis, A. E. et al. Re‐examining the brain regions crucial for orchestrating speech articulation. Brain 127 , 1479–1487 (2004).

Flinker, A. et al. Redefining the role of Broca’s area in speech. Proc. Natl Acad. Sci. USA 112 , 2871–2875 (2015).

Long, M. A. et al. Functional segregation of cortical regions underlying speech timing and articulation. Neuron 89 , 1187–1193 (2016).

Guenther, F. H. Neural Control of Speech (MIT Press, 2016).

Basilakos, A., Smith, K. G., Fillmore, P., Fridriksson, J. & Fedorenko, E. Functional characterization of the human speech articulation network. Cereb. Cortex 28 , 1816–1830 (2018).

Obleser, J., Zimmermann, J., Van Meter, J. & Rauschecker, J. P. Multiple stages of auditory speech perception reflected in event-related fMRI. Cereb. Cortex 17 , 2251–2257 (2007).

Mesgarani, N., Cheung, C., Johnson, K. & Chang, E. F. Phonetic feature encoding in human superior temporal gyrus. Science 343 , 1006–1010 (2014).

Norman-Haignere, S., Kanwisher, N. G. & McDermott, J. H. Distinct cortical pathways for music and speech revealed by hypothesis-free voxel decomposition. Neuron 88 , 1281–1296 (2015).

Overath, T., McDermott, J., Zarate, J. & Poeppel, D. The cortical analysis of speech-specific temporal structure revealed by responses to sound quilts. Nat. Neurosci. 18 , 903–911 (2015).

Norman-Haignere, S. V. et al. A neural population selective for song in human auditory cortex. Curr. Biol. 32 , 1470–1484.e12 (2022).

Hickok, G. & Poeppel, D. The cortical organization of speech processing. Nat. Rev. Neurosci. 8 , 393–402 (2007).

Friederici, A. D. The cortical language circuit: from auditory perception to sentence comprehension. Trends Cogn. Sci. 16 , 262–268 (2012).

Wilson, S. M. et al. Recovery from aphasia in the first year after stroke. Brain 146 , 1021–1039 (2023).

Radford, A. et al. Language models are unsupervised multitask learners. OpenAI blog 1 , 9 (2019).

Jain, S. & Huth, A. Incorporating context into language encoding models for fMRI. in Proc. 32nd International Conf. Neural Information Processing Systems (eds Bengio, S. et al.) (Curran Associates, 2018).

Schrimpf, M. et al. The neural architecture of language: Integrative modeling converges on predictive processing. Proc. Natl Acad. Sci. USA 118 , e2105646118 (2021).

Caucheteux, C. & King, J. R. Brains and algorithms partially converge in natural language processing. Commun. Biol. 5 , 134 (2022).

Goldstein, A. et al. Shared computational principles for language processing in humans and deep language models. Nat. Neurosci. 25 , 369–380 (2022).

Tuckute, T., Kanwisher, N. & Fedorenko, E. Language in brains, minds, and machines. Annu. Rev. Neurosci. https://doi.org/10.1146/annurev-neuro-120623-101142 (2024).

Paulk, A. C. et al. Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex. Nat. Neurosci. 25 , 252–263 (2022).

Leonard, M. K. et al. Large-scale single-neuron speech sound encoding across the depth of human cortex. Nature 626 , 593–602 (2024).

Fodor, J. A. The Language of Thought (Crowell, 1975).

Fodor, J. A. & Pylyshyn, Z. W. Connectionism and cognitive architecture: a critical analysis. Cognition 28 , 3–71 (1988).

Rule, J. S., Tenenbaum, J. B. & Piantadosi, S. T. The child as hacker. Trends Cogn. Sci. 24 , 900–915 (2020).

Quilty-Dunn, J., Porot, N. & Mandelbaum, E. The best game in town: the reemergence of the language-of-thought hypothesis across the cognitive sciences. Behav. Brain Sci. 46 , e261 (2023).

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

Smolensky, P. & Legendre, G. The Harmonic Mind: From Neural Computation to Optimality–Theoretic Grammar Vol. 1: Cognitive Architecture (MIT Press, 2006).

Frankland, S. M. & Greene, J. D. Concepts and compositionality: in search of the brain’s language of thought. Annu. Rev. Psychol. 71 , 273–303 (2020).

Lake, B. M. & Baroni, M. Human-like systematic generalization through a meta-learning neural network. Nature 623 , 115–121 (2023).

Dehaene-Lambertz, G., Dehaene, S. & Hertz-Pannier, L. Functional neuroimaging of speech perception in infants. Science 298 , 2013–2015 (2002).

Pena, M. et al. Sounds and silence: an optical topography study of language recognition at birth. Proc. Natl Acad. Sci. USA 100 , 11702–11705 (2003).

Cristia, A., Minagawa, Y. & Dupoux, E. Responses to vocalizations and auditory controls in the human newborn brain. PLoS ONE 9 , e115162 (2014).

Article   ADS   PubMed   PubMed Central   Google Scholar  

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Acknowledgements

The authors thank A. Ivanova, R. Jackendoff, N. Kanwisher, K. Mahowald, R. Seyfarth, C. Shain and N. Zaslavsky for helpful comments on earlier drafts of the manuscript; N. Caselli, M. Coppola, A. Hillis, L. Menn, R. Varley and S. Wilson for comments on specific sections; C. Casto, T. Regev, F. Mollica and R. Futrell for help with the figures; and S. Swords, N. Jhingan, H. S. Kim and A. Sathe for help with references. E.F. was supported by NIH awards DC016607 and DC016950 from NIDCD, NS121471 from NINDS, and from funds from MIT’s McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Simons Center for the Social Brain, and Quest for Intelligence.

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Fedorenko, E., Piantadosi, S.T. & Gibson, E.A.F. Language is primarily a tool for communication rather than thought. Nature 630 , 575–586 (2024). https://doi.org/10.1038/s41586-024-07522-w

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Title: learn beyond the answer: training language models with reflection for mathematical reasoning.

Abstract: Supervised fine-tuning enhances the problem-solving abilities of language models across various mathematical reasoning tasks. To maximize such benefits, existing research focuses on broadening the training set with various data augmentation techniques, which is effective for standard single-round question-answering settings. Our work introduces a novel technique aimed at cultivating a deeper understanding of the training problems at hand, enhancing performance not only in standard settings but also in more complex scenarios that require reflective thinking. Specifically, we propose reflective augmentation, a method that embeds problem reflection into each training instance. It trains the model to consider alternative perspectives and engage with abstractions and analogies, thereby fostering a thorough comprehension through reflective reasoning. Extensive experiments validate the achievement of our aim, underscoring the unique advantages of our method and its complementary nature relative to existing augmentation techniques.

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