We confess that the first part of our title is somewhat of a misnomer. Bayesian reasoning is a normative approach to probabilistic belief revision and, as such, it is in need of no improvement. Rather, it is the typical individual whose reasoning and judgments often fall short of the Bayesian ideal who is the focus of improvement. What have we learnt from over a half-century of research and theory on this topic that could explain why people are often non-Bayesian? Can Bayesian reasoning be facilitated, and if so why? These are the questions that motivate this Frontiers in Psychology Research Topic. Bayes' theorem, named after English statistician, philosopher, and Presbyterian minister, Thomas Bayes, offers a method for updating one’s prior probability of an hypothesis H on the basis of new data D such that P(H|D) = P(D|H)P(H)/P(D). The first wave of psychological research, pioneered by Ward Edwards, revealed that people were overly conservative in updating their posterior probabilities (i.e., P(D|H)). A second wave, spearheaded by Daniel Kahneman and Amos Tversky, showed that people often ignored prior probabilities or base rates, where the priors had a frequentist interpretation, and hence were not Bayesians at all. In the 1990s, a third wave of research spurred by Leda Cosmides and John Tooby and by Gerd Gigerenzer and Ulrich Hoffrage showed that people can reason more like a Bayesian if only the information provided takes the form of (non-relativized) natural frequencies. Although Kahneman and Tversky had already noted the advantages of frequency representations, it was the third wave scholars who pushed the prescriptive agenda, arguing that there are feasible and effective methods for improving belief revision. Most scholars now agree that natural frequency representations do facilitate Bayesian reasoning. However, they do not agree on why this is so. The original third wave scholars favor an evolutionary account that posits human brain adaptation to natural frequency processing. But almost as soon as this view was proposed, other scholars challenged it, arguing that such evolutionary assumptions were not needed. The dominant opposing view has been that the benefit of natural frequencies is mainly due to the fact that such representations make the nested set relations perfectly transparent. Thus, people can more easily see what information they need to focus on and how to simply combine it. This Research Topic aims to take stock of where we are at present. Are we in a proto-fourth wave? If so, does it offer a synthesis of recent theoretical disagreements? The second part of the title orients the reader to the two main subtopics: what works and why? In terms of the first subtopic, we seek contributions that advance understanding of how to improve people’s abilities to revise their beliefs and to integrate probabilistic information effectively. The second subtopic centers on explaining why methods that improve non-Bayesian reasoning work as well as they do. In addressing that issue, we welcome both critical analyses of existing theories as well as fresh perspectives. For both subtopics, we welcome the full range of manuscript types.

Modern Mathematics is constructed rigorously through proofs, based on truths, which are either axioms or previously proven theorems. Thus, it is par excellence a model of rational inquiry. Links between Cognitive Psychology and Mathematics Education have been particularly strong during the last decades. Indeed, the Enlightenment view of the rational human mind that reasons, makes decisions and solves problems based on logic and probabilities, was shaken during the second half of the twentieth century. Cognitive psychologists discovered that humans' thoughts and actions often deviate from rules imposed by strict normative theories of inference. Yet, these deviations should not be called "errors": as Cognitive Psychologists have demonstrated, these deviations may be either valid heuristics that succeed in the environments in which humans have evolved, or biases that are caused by a lack of adaptation to abstract information formats. Humans, as the cognitive psychologist and economist Herbert Simon claimed, do not usually optimize, but rather satisfice, even when solving problem. This Research Topic aims at demonstrating that these insights have had a decisive impact on Mathematics Education. We want to stress that we are concerned with the view of bounded rationality that is different from the one espoused by the heuristics-and-biases program. In Simon’s bounded rationality and its direct descendant ecological rationality, rationality is understood in terms of cognitive success in the world (correspondence) rather than in terms of conformity to content-free norms of coherence (e.g., transitivity).

This book focuses on how statistical reasoning works and on training programs that can exploit people's natural cognitive capabilities to improve their statistical reasoning. Training programs that take into account findings from evolutionary psychology and instructional theory are shown to have substantially larger effects that are more stable over time than previous training regimens. The theoretical implications are traced in a neural network model of human performance on statistical reasoning problems. This book apppeals to judgment and decision making researchers and other cognitive scientists, as well as to teachers of statistics and probabilistic reasoning.

Decision making is a critical element in the field of medicine that can lead to life-or-death outcomes, yet it is an element fraught with complex and conflicting variables, diagnostic and therapeutic uncertainties, patient preferences and values, and costs. Together, decisions made by physicians, patients, insurers, and policymakers determine the quality of health care, quality that depends inherently on counterbalancing risks and benefits and competing objectives such as maximizing life expectancy versus optimizing quality of life or quality of care versus economic realities. Broadly speaking, concepts in medical decision making (MDM) may be divided into two major categories: prescriptive and descriptive. Work in the area of prescriptive MDM investigates how medical decisions should be done using complicated analyses and algorithms to determine cost-effectiveness measures, prediction methods, and so on. In contrast, descriptive MDM studies how decisions actually are made involving human judgment, biases, social influences, patient factors, and so on. The Encyclopedia of Medical Decision Making gives a gentle introduction to both categories, revealing how medical and healthcare decisions are actually made—and constrained—and how physician, healthcare management, and patient decision making can be improved to optimize health outcomes. Key Features Discusses very general issues that span many aspects of MDM, including bioethics; health policy and economics; disaster simulation modeling; medical informatics; the psychology of decision making; shared and team medical decision making; social, moral, and religious factors; end-of-life decision making; assessing patient preference and patient adherence; and more Incorporates both quantity and quality of life in optimizing a medical decision Considers characteristics of the decisionmaker and how those characteristics influence their decisions Presents outcome measures to judge the quality or impact of a medical decision Examines some of the more commonly encountered biostatistical methods used in prescriptive decision making Provides utility assessment techniques that facilitate quantitative medical decision making Addresses the many different assumption perspectives the decision maker might choose from when trying to optimize a decision Offers mechanisms for defining MDM algorithms With comprehensive and authoritative coverage by experts in the fields of medicine, decision science and cognitive psychology, and healthcare management, this two-volume Encyclopedia is a must-have resource for any academic library.

This undergraduate textbook reviews psychological research in the major areas of reasoning and thinking: deduction, induction, hypothesis testing, probability judgement, and decision making. It also covers the major theoretical debates in each area, and devotes a chapter to one of the liveliest issues in the field: the question of human rationality. Central themes that recur throughout the book include not only rationality, but also the relation between normative theories such as logic, probability theory, and decision theory, and human performance, both in experiments and in the world outside the laboratory. No prior acquaintance with formal systems is assumed, and everyday examples are used throughout to illustrate technical and theoretical points. The book differs from others in the market firstly in the range of material covered: other tend to focus primarily on on either reasoning or thinking. It is also the first student-level text to survey an imporatant new theoretical perspective, the information-gain or rational analysis approach, and to review the rationality debate from the standpoint of psuchological research in a wide range of areas.

Getting the right diagnosis is a key aspect of health care - it provides an explanation of a patient's health problem and informs subsequent health care decisions. The diagnostic process is a complex, collaborative activity that involves clinical reasoning and information gathering to determine a patient's health problem. According to Improving Diagnosis in Health Care, diagnostic errors-inaccurate or delayed diagnoses-persist throughout all settings of care and continue to harm an unacceptable number of patients. It is likely that most people will experience at least one diagnostic error in their lifetime, sometimes with devastating consequences. Diagnostic errors may cause harm to patients by preventing or delaying appropriate treatment, providing unnecessary or harmful treatment, or resulting in psychological or financial repercussions. The committee concluded that improving the diagnostic process is not only possible, but also represents a moral, professional, and public health imperative. Improving Diagnosis in Health Care, a continuation of the landmark Institute of Medicine reports To Err Is Human (2000) and Crossing the Quality Chasm (2001), finds that diagnosis-and, in particular, the occurrence of diagnostic errorsâ€"has been largely unappreciated in efforts to improve the quality and safety of health care. Without a dedicated focus on improving diagnosis, diagnostic errors will likely worsen as the delivery of health care and the diagnostic process continue to increase in complexity. Just as the diagnostic process is a collaborative activity, improving diagnosis will require collaboration and a widespread commitment to change among health care professionals, health care organizations, patients and their families, researchers, and policy makers. The recommendations of Improving Diagnosis in Health Care contribute to the growing momentum for change in this crucial area of health care quality and safety.

Mental health professionals often must make judgments or decisions involving vital matters. Is an individual likely to act violently? Has a child been sexually abused? Is a police officer fit to carry a gun? An explosion of research in clinical and cognitive psychology provides practical means for enhancing the accuracy of clinical decision making and prediction and thereby improving outcomes and the quality of care. Unfortunately, this research has not been broadly disseminated in the mental health field. The book is designed to familiarize readers with essential findings from decision science and its practical, immediate applications in the mental health field.

The problems faced by medical doctors and automobile mechanics are in some ways quite similar-something isn't working right and must be fixed. They must both figure out the cause of malfunctions and determine the appropriate treatments. Yet, the mechanichas no need to worry about an automobile's psyche; the specific mechanical factors are the only ones that come into play. In health care, however, the factors influencing outcomes are broader, more complicated, and colored by the underlying psychologicalfactors of those involved. These factors have profound effects. Doctors are often influenced by patients' description of symptoms, yet information is often incomplete or inaccurate or colored by the patient's own experiences. The doctor's own demeanor maygreatly affect outcomes, as can the doctor's ability to interpret the ever-expanding medical literature. These underlying influences are often not acknowledged, and yet they can have far-reaching consequences. Acknowledging these psychological factors and learning how to overcome them is the first step in improving communications between doctors and patients and to improving diagnosis and treatment. Here, the authors offer strategies for remedying the situation and moving forward to a better understanding of doctor-patient visits and their outcomes.

While common sense and rationality often have been viewed as two distinct features in a unitifed cognitive map, this this volume offers novel, even paradoxical views of the relationship. Touching on various disciplines, it considers what constitutes human rationality, behavior, and intelligence.