Citations & References:"One day," Swara began the talk, when she came after greeting with Basmalah and Salaam. "The King of Eagles, in front of thousands of journalists, quoting John Gray's eponymous metaphor, he anounced, 'Men Are from Mars, Women Are from Venus. So, in the next few weeks, we will land on Venus.'Hearing the announcement, the Duck King immediately reacted. And in front of thousands of journalists, he, accompanied by a minister and a governor, announced, 'In one and a half months, we have completed the runway of a spaceship with astronauts, will land into the Sun!' The audience had mixed responses, some said, 'That's a great idea!' But a journalist commented, 'Pardon me sir, how do you do that? Isn't the Sun very hot, and perhaps the spaceship will catch fire before it even gets closer?'The minister and the governor whispered to the Duck King, he nodded and without even thinking, then said, 'Don't worry, based on the data we have collected and an exact calculations, we're going to land the spaceship on the Sun, at midnight!'For a moment, Swara stopped, then said, 'The French philosopher René Descartes famously declared, 'Cogito, ergo sum [I think, therefore I am].' Every fully functioning human adult, shares a sense that the ability to think, to reason, is a part of one’s fundamental identity. A person may be struck blind or deaf, yet still recognize his or her core cognitive capacities as intact. Even loss of language, the gift often claimed as the sine qua non of Homo sapiens, does not take away a person’s essential humanness. Perhaps thinking, not language, lies closest to both the core of our individual identity and to what is special about our species.A person who loses language but can still make intelligent decisions, as demonstrated by actions, is viewed as mentally competent. In contrast, the kinds of brain damage that rob an individual of the capacity to think and reason are considered the harshest blows that can be struck against a sense of personhood, 'Cogito, ergo sum.'We can start to answer a question, 'What is Thinking?' by looking at the various ways the word thinking is used in everyday language. 'I think that water is necessary for life,' and 'Keith and Bob think George was a fascist'; both express beliefs (of varying degrees of apparent plausibility)—explicit claims of what someone takes to be a truth about the world. 'Ann is sure to think of a solution' carries us into the realm of problem solving, the mental construction of an action plan to achieve a goal. The complaint, 'Why didn’t you think before you went ahead with your half-baked scheme?' emphasizes that thinking can be a kind of foresight, a way of 'seeing' the possible future. 'What do you think about it?' calls for a judgment, an assessment of the desirability of an option. 'Genocide is evil' takes judgment into the moral domain. And then there’s 'Albert is lost in thought,' where thinking becomes some sort of mental meadow through which a person might meander on a rainy afternoon, oblivious to the world outside.Rips and Conrad elicited judgments from college students about how various mentalistic terms relate to one another. Using statistical techniques, the investigators were able to summarize these relationships. Roughly, people think planning is a kind of deciding, which is a kind of reasoning, which is a kind of conceptualizing, which is a kind of thinking. People also think (that verb again!) that thinking is part of conceptualizing, which is part of remembering, which is part of reasoning, and so on. The kinds ordering and the parts ordering are quite similar; most strikingly, thinking is the most general term in both orderings—the grand superordinate of mental activities, which permeates all the others.It is not easy to make the move from the free flow of everyday speech to scientific definitions of mental terms, but let us nonetheless offer a preliminary definition of thinking, 'Thinking is the systematic transformation of mental representations of knowledge to characterize actual or possible states of the world, often in service of goals.'The study of thinking includes several interrelated subfields, which reflect slightly different perspectives on thinking. Reasoning, which has a long tradition that springs from philosophy and logic, places emphasis on the process of drawing inferences (conclusions) from some initial information (premises). In standard logic, an inference is deductive if the truth of the premises guarantees the truth of the conclusion by virtue of the argument form.If the truth of the premises renders the truth of the conclusion more credible, but does not bestow certainty, the inference is called inductive. Judgment and decision making involve assessment of the value of an option or the probability that it will yield a certain payoff (judgment), coupled with choice among alternatives (decision making). Problem solving involves the construction of a course of action that can achieve a goal.Although these distinct perspectives on thinking are useful in organizing the field, these aspects of thinking overlap in every conceivable way. To solve a problem, one is likely to reason about the consequences of possible actions and to make decisions to select among alternative actions.A logic problem, as the name implies, is a problem to be solved (with the goal of deriving or evaluating a possible conclusion). Making a decision is often a problem that requires reasoning. And so on. These subdivisions of the field, like our preliminary definition of thinking, should be treated as guideposts, not destinations.Thinking and reasoning, long the academic province of philosophy, have over the past century emerged as core topics of empirical investigation and theoretical analysis in the modern fields known as cognitive psychology, cognitive science, and cognitive neuroscience. Before psychology was founded, the 18th-century philosophers Immanuel Kant (in Germany) and David Hume (in Scotland) laid the foundations for all subsequent work on the origins of causal knowledge, perhaps the most central problem in the study of thinking. And if we were to choose one phrase to set the stage for modern views of thinking, it would be an observation of the British philosopher Thomas Hobbes, who in 1651 in his treatise Leviathan proposed 'Reasoning is but reckoning.' Reckoning is an odd term today, but in the 17th century it meant 'computation,' as in arithmetic calculations.It was not until the 20th century that the psychology of thinking became a scientific endeavor. The first half of the century gave rise to many important pioneers who in very different ways laid the foundations for the emergence of the modern field of thinking and reasoning. Foremost were the Gestalt psychologists of Germany, who provided deep insights into the nature of problem solving. Most notable of the Gestaltists were Karl Duncker and Max Wertheimer, students of human problem solving, and Wolfgang Köhler, a keen observer of problem solving by great apes.The pioneers of the early 20th century also include Sigmund Freud, whose complex and evercontroversial legacy includes the notions that forms of thought can be unconscious, and that 'cold' cognition is tangled up with 'hot' emotion. As the founder of clinical psychology, Freud’s legacy also includes the ongoing integration of research on 'normal' thinking with studies of thought disorders, such as schizophrenia.Other early pioneers in the early and mid-century contributed to various fields of study that are now embraced within thinking and reasoning. Cognitive development continues to be influenced by the early theories developed by the Swiss psychologist Jean Piaget and the Russian psychologist Lev Vygotsky. In the United States, Charles Spearman was a leader in the systematic study of individual differences in intelligence. In the middle of the century, the Russian neurologist Alexander Luria made immense contributions to our understanding of how thinking depends on specific areas of the brain, anticipating the modern field of cognitive neuroscience. Around the same time in the United States, Herbert Simon argued that the traditional rational model of economic theory should be replaced with a framework that accounted for a variety of human resource constraints, such as bounded attention and memory capacity and limited time. This was one of the contributions that in 1978 earned Simon the Nobel Prize in Economics.In 1943, the British psychologist Kenneth Craik sketched the fundamental notion that a mental representation provides a kind of model of the world that can be 'run' to make predictions (much like an engineer might use a physical scale model of a bridge to anticipate the effects of stress on the actual bridge intended to span a river). In the 1960s and 1970s, modern work on the psychology of reasoning began in Britain with the contributions of Peter Wason and his collaborator Philip Johnson-Laird.The modern conception of thinking as computation became prominent in the 1970s. In their classic treatment of human problem solving, Allen Newell and Herbert Simon showed that the computational analysis of thinking (anticipated by Alan Turing, the father of computer science) could yield important empirical and theoretical results. Like a program running on a digital computer, a person thinking through a problem can be viewed as taking an input that represents initial conditions and a goal, and applying a sequence of operations to reduce the difference between the initial conditions and the goal. The work of Newell and Simon established computer simulation as a standard method for analyzing human thinking. It also highlighted the potential of production systems, which were subsequently developed extensively as cognitive models by John Anderson and his colleagues.The 1970s saw a wide range of major developments that continue to shape the field. Eleanor Rosch, building on earlier work by Jerome Bruner, addressed the fundamental question of why people have the categories they do, and not other logically possible groupings of objects. Rosch argued that natural categories often have fuzzy boundaries (a whale is an odd mammal), but nonetheless have clear central tendencies, or prototypes (people by and large agree that a bear makes a fine mammal). The psychology of human judgment was reshaped by the insights of Amos Tversky and Daniel Kahneman, who identified simple cognitive strategies, or heuristics, that people use to make judgments of frequency and probability. Often quick and accurate, these strategies can in some circumstances lead to nonnormative judgments. After Tversky’s death in 1996, this line of work was continued by Kahneman, who was awarded the Nobel Prize in Economics in 2002. The current view of judgment that has emerged from 30 years of research is summarized by Griffin et al. Goldstone and Son review Tversky’s influential theory of similarity judgments.In 1982 David Marr, a young vision scientist, laid out a vision of how the science of mind should proceed. Marr distinguished three levels of analysis, which he termed the levels of computation, representation and algorithm, and implementation. Each level, according to Marr, addresses different questions, which he illustrated with the example of a physical device, the cash register. At Marr’s most abstract level, computation (not to be confused with computation of an algorithm on a computer), the basic questions are 'What is the goal that the cognitive process is meant to accomplish?' and ' What is the logic of the mapping from the input to the output that distinguishes this mapping from other inputoutput mappings?' A cash register, viewed at this level, is used to achieve the goal of calculating how much is owed for a purchase. This task maps precisely onto the axioms of addition (e.g., the amount owed shouldn’t vary with the order in which items are presented to the sales clerk, a constraint that precisely matches the commutativity property of addition). It follows that without knowing anything else about the workings of a particular cash register, we can be sure that (if it is working properly) it will be doing addition (not division).The level of representation and algorithm, as the name implies, deals with the questions, 'What is the representation of the input and output?' and 'What is the algorithm for transforming the former into the latter?' Within a cash register, addition might be performed using numbers in either decimal or binary code, starting with either the leftmost or rightmost digit. Finally, the level of implementation addresses the question, 'How are the representation and algorithm realized physically? The cash register could be implemented as an electronic calculator, or a mechanical adding machine, or even a mental abacus in the mind of the clerk.In his book, Marr stressed the importance of the computational level of analysis, arguing that it could be seriously misleading to focus prematurely on the more concrete levels of analysis for a cognitive task without understanding the goal or nature of the mental computation. Sadly, Marr died of leukemia before his book was published, so we do not know how his thinking about levels of analysis might have evolved.There are two kinds of thinking we call 'scientific.' The first, and most obvious, is thinking about the content of science. People are engaged in scientific thinking when they are reasoning about such entities and processes as force, mass, energy, equilibrium, magnetism, atoms, photosynthesis, radiation, geology, or astrophysics (and, of course, cognitive psychology!). The second kind of scientific thinking includes the set of reasoning processes that permeate the field of science: induction, deduction, experimental design, causal reasoning, concept formation, hypothesis testing, and so on. However, these reasoning processes are not unique to scientific thinking: They are the very same processes involved in everyday thinking. As Einstein put it: 'The scientific way of forming concepts differs from that which we use in our daily life, not basically, but merely in the more precise definition of concepts and conclusions; more painstaking and systematic choice of experimental material, and greater logicaleconomy.'One of the primary goals of accounts of scientific thinking has been to provide an overarching framework to understand the scientific mind. One framework that has had a great influence in cognitive science is that scientific thinking and scientific discovery can be conceived as a form of problem solving. Simon argued that both scientific thinking in general and problem solving in particular could be thought of as a search in a problem space.Many researchers have also regarded testing specific hypotheses predicted by theories as one of the key attributes of scientific thinking. Hypothesis testing is the process of evaluating a proposition by collecting evidence regarding its truth. Experimental cognitive research on scientific thinking that specifically examines this issue has tended to fall into two broad classes of investigations. The first class is concerned with the types of reasoning that lead scientists astray, thus blocking scientific ingenuity. A large amount of research has been conducted on the potentially faulty reasoning strategies that both participants in experiments and scientists use, such as considering only one favored hypothesis at a time and how this prevents the scientists from making discoveries.The second class is concerned with uncovering the mental processes underlying the generation of new scientific hypotheses and concepts. This research has tended to focus on the use of analogy and imagery in science, as well as the use of specific types of problem-solving heuristics.One of the most basic characteristics of science is that scientists assume that the universe that we live in follows predictable rules. Scientists reason using a variety of different strategies to make new scientific discoveries. Three frequently used types of reasoning strategies that scientists use are inductive, abductive, and deductive reasoning. In the case of inductive reasoning, a scientist may observe a series of events and try to discover a rule that governs the event. Once a rule is discovered, scientists can extrapolate from the rule to formulate theories of observed and yet-to-beobserved phenomena. Example of induction: Daisy is swan and white. Danny is a swan and white. Dante is a swan and white [and so on]. Therefore, all swans are white.One example is the discovery using inductive reasoning that a certain type of bacterium is a cause of many ulcers. Example of abduction: All swans are white. Daisy is white. Therefore, Daisy is a swan.While less commonly mentioned than inductive reasoning, abductive reasoning is an important form of reasoning that scientists use when they are seeking to propose explanations for events such as unexpected findings.Turning now to deductive thinking, many thinking processes that scientists adhere to follow traditional rules of deductive logic. These processes correspond to those conditions in which a hypothesis may lead to, or is deducible to, a conclusion. Example of deductive : All swans are white. Daisy is swan. Therefore, Daisy is white.Though they are not always phrased in syllogistic form, deductive arguments can be phrased as 'syllogisms,' or as brief, mathematical statements in which the premises lead to the conclusion. Deductive reasoning is an extremely important aspect of scientific thinking because it underlies a large component of how scientists conduct their research. By looking at many scientific discoveries, we can often see that deductive reasoning is at work. Deductive reasoning statements all contain information or rules that state an assumption about how the world works, as well as a conclusion that would necessarily follow from the rule. Numerous discoveries in physics such as the discovery of dark matter by Vera Rubin are based on deductions.One of the most widely mentioned reasoning processes used in science is analogy. Scientists use analogies to form a bridge between what they already know and what they are trying to explain, understand, or discover. In fact, many scientists have claimed that the making of certain analogies was instrumental in their making a scientific discovery, and almost all scientific autobiographies and biographies feature one particular analogy that is discussed in depth. Coupled with the fact that there has been an enormous research program on analogical thinking and reasoning, we now have a number of models and theories of analogical reasoning that suggest how analogy can play a role in scientific discovery. By analyzing several major discoveries in the history of science, Thagard and Croft, Nersessian, and Gentner and Jeziorski have all shown that analogical reasoning is a key aspect of scientific discovery.The similarities between children’s thinking and scientists’ thinking have an inherent allure and an internal contradiction. Before their first birthday, children appear to know several fundamental facts about the physical world. For example, studies with infants show that they behave as if they understand that solid objects endure over time (e.g., they don’t just disappear and reappear, they cannot move through each other, and they move as a result of collisions with other solid objects or the force of gravity. And even 6-month-olds are able to predict the future location of a moving object that they are attempting to grasp. In addition, they appear to be able to make nontrivial inferences about causes and their effects.Computational approaches have provided a more complete account of the scientific mind. Computational models provide specific detailed accounts of the cognitive processes underlying scientific thinking. Early computational work consisted of taking a scientific discovery and building computational models of the reasoning processes involved in the discovery.The legal profession has long claimed that there are process-based differences between legal reasoning—that is, the thinking and reasoning of lawyers and judges—and the reasoning of those without legal training. Whether those claims are sound, however, is a subject of considerable debate.The practice of medicine requires art as well as science. The latter argues for a deeper understanding of the mechanisms underlying disease processes and use of scientific evidence in making patient care decisions. The study of medical reasoning and thinking underlies much of medical cognition and has been the focus of research in cognitive science and artificial intelligence in medicine. Expertise and medical knowledge organization, the directionality of reasoning, and the nature of medical errors are intricately tied to thinking and decision-making processes in medicine. With the recent advancement of technology in medicine, technology-mediated reasoning and reasoning support systems will be a focus for future research.Thinking and reasoning enter into the practice of business in limitless ways. The practice of business is enormously variable. The marketer influencing a customer’s purchase, the executive negotiating a deal, the manager coordinating the production of goods, the analyst reviewing company performance, and the accountant trying to make the numbers add up are all engaging in aspects of the practice of business.Businesses need to sell their products and services, so a major concern of business is shaping how consumers make purchases, use products, and think about brands. Consumer behavior researchers study these questions and generate more psychological research on individual thinking and reasoning than researchers in any other area of business academia. As a simple indication of the role of cognition research in consumer behavior, the Handbook of Consumer Psychology dedicates about half of its 1,200 pages to reviewing information processing and social cognition research. Most consumer behavior research on thinking and reasoning is experimental. There is also mathematical and computational modeling, field survey research, observations of consumer activity, examinations of archival measures of consumer activity, and some qualitative research.Consumer purchasing is a decision-making activity. For example, one prominent feature of the consumer decision-making context (as any walk through a grocery store or time spent shopping online will make apparent) is a concern for how people make decisions hen confronted by large numbers of options. More generally, decision making is an activity, and the many goals decision makers have as they engage in that activity guide the choices that result.Listening to music entails processes in which auditory input is automatically analyzed and classified, and conscious processes in which listeners interpret and evaluate the music. Performing music involves engaging in rehearsed movements that reflect procedural (embodied) knowledge of music, along with conscious efforts to guide and refine these movements through online monitoring of the sounded output. Composing music balances the use of intuition that reflects implicit knowledge of music with conscious and deliberate efforts to invent musical textures and devices that are innovative and consistent with individual aesthetic goals. Listeners and musicians also interact with one another in ways that blur the boundary between them: Listeners tap or clap in time with music, monitor the facial expressions and gestures of performers, and empathize emotionally with musicians; musicians, in turn, attend to their audience and perform differently depending on the perceived energy and attitude of their listeners.Musicians and listeners are roped together through shared cognitive, emotional, and motor experiences, exhibiting remarkable synchrony in behavior and thought.Learning to think is about transfer. 'Learning to think' is different from 'learning' in that it implies that a learner achieves an increase in more general intellectual capability, rather than just in more specific domain content. Learning to think implies more than learning English, learning math, learning history, or learning science. In other words, learning to think implies transfer.One way to learn to think is learning languages with which to think more powerfully. The obvious case is learning a natural language, like English. It seems uncontroversial that a good part of what makes human thinking so powerful is our capability for language. The spoken form of natural language not only facilitates communication and collaborative thinking but also provides a medium for reasoning and logical thinking. The written form further facilitates collaborative endeavors over wide stretches of time and space and is also arguably a vehicle for improving thought. Anyone who has written a manuscript, is likely to have had the experience that the writing process changes one’s thinking and yields a better product than spontaneous speech would have. Clearly, language-learning activities are a huge part of learning to think and a major responsibility of our educational systems.'""And lastly, before I go, " Swara was going to end her discussion, 'liston to this story, 'In November, the Indian chief began to think it was going to be a cold winter. 'Winter is coming!' said he. So he instructed his tribe to collect firewood. To double-check his prediction, the chief called the National Weather Service and asked a meteorologist if the winter was going to be a cold one. The man responded, 'According to our indicators, we think it just might be.'Following the phone call, the chief told his people to find extra wood, just in case. A week later he called the National Weather Service again, and they conɹrmed that a harsh winter was indeed headed their way.The chief ordered all of the villagers to scavenge every scrap of wood they could. Two weeks later, he called the National Weather Service again and asked, 'Are you absolutely certain this winter is going to be very cold?''Oh, we sure are,' the man replied, 'the Indians are collecting wood like crazy.'And Allah knows best."
- Keith J. Holyoak & Robert G. Morrison (Ed.), The Oxford Handbook of Thinking and Reasoning, Oxford University Press
- D.Q. McInerny, Being Logical - A Guide to Good Thinking, Random House
