Strategies for hypothesis testing in scientific investigation and everyday reasoning have interested both psychologists and philosophers. A number of these scholars stress the importance of disconnrmation in reasoning and suggest that people are instead prone to a general deleterious "confirmation bias." In particular, it is suggested that people tend to test those cases that have the best chance of verifying current beliefs rather than those that have the best chance of falsifying them. We show, however; that many phenomena labeled "confirmation bias" are better understood in terms of a general positive test strategy. With this strategy, there is a tendency to test cases that are expected (or known) to have the property of interest rather than those expected (or known) to lack that property. This strategy is not equivalent to confirmation bias in the first sense; we show that the positive test strategy can be a very good heuristic for determining the truth or falsity of a hypothesis under realistic conditions. It can, however, lead to systematic errors or inefficiencies. The appropriateness of human hypothesis-testing strategies and prescriptions about optimal strategies must be understood in terms of the interaction between the strategy and the task at hand.

Confirmation bias, as the term is typically used in the psychological literature, connotes the seeking or interpreting of evidence in ways that are partial to existing beliefs, expectations, or a hypothesis in hand. The author reviews evidence of such a bias in a variety of guises and gives examples of its operation in several practical contexts. Possible explanations are considered, and the question of its utility or disutility is discussed. When men wish to construct or support a theory, how they torture facts into their service! (Mackay, 1852/ 1932, p. 552) Confirmation bias is perhaps the best known and most widely accepted notion of inferential error to come out of the literature on human reasoning. (Evans, 1989, p. 41) If one were to attempt to identify a single problematic aspect of human reasoning that deserves attention above all others, the confirmation bias would have to be among the candidates for consideration. Many have written about this bias, and it appears to be sufficiently strong and pervasive that one is led to wonder whether the bias, by itself, might account for a significant fraction of the disputes, altercations, and misunderstandings that occur among individuals, groups, and nations. Confirmation bias has been used in the psychological literature to refer to a variety of phenomena. Here I take the term to represent a generic concept that subsumes several more specific ideas that connote the inappropriate bolstering of hypotheses or beliefs whose truth is in question.

this article should be addressed to Evan Heir, Department of Psychology, Northwestern University, 2029 Sheridan Road, Evanston, Illinois 60208. Electronic mail may be sent to heit@nwu.edu

This review proposes that implicit learning processes are the cognitive substrate of social intuition. This hypothesis is supported by (a) the conceptual correspondence between implicit learning and social intuition (nonverbal communication) and (b) a review of relevant neuropsychological (Huntington's and Parkinson's disease), neuroimaging, neurophysiological, and neuroanatomical data. It is concluded that the caudate and putamen, in the basal ganglia, are central components of both intuition and implicit learning, supporting the proposed relationship. Parallel, but distinct, processes of judgment and action are demonstrated at each of the social, cognitive, and neural levels of analysis. Additionally, explicit attempts to learn a sequence can interfere with implicit learning. The possible relevance of the computations of the basal ganglia to emotional appraisal, automatic evaluation, script processing, and decision making are discussed. These "feelings " have an efficiency of operation which it is impossi-ble for thought to match. Even our most highly intellectualized operations depend upon them as a "fringe " by which to guide our inferential movements. They give us our sense of rightness and wrongness, of what to select and emphasize and follow up, and what

The experience of willing an act arises from interpreting one's thought as the cause of the act. Conscious will is thus experienced as a function of the priority, consistency, and exclusivity of the thought about the action. The thought must occur before the action, be consistent with the action, and not be accompanied by other causes. An experiment illustrating the role of priority found that people can arrive at the mistaken belief that they have intentionally caused an action that in fact they were forced to perform when they are simply led to think about the action just before its occurrence. Conscious will is a pervasive human experience. We all have the sense that we do things, that we cause our acts, that we are agents. As William James (1890) observed, "the whole sting and excitement of our voluntary life... depends on our sense that in it things are really being decided from one moment to another, and that it is not the dull rattling off of a chain that was forged innumerable ages ago " (p. 453). And yet, the very notion of the will seems to contradict the core assumption of psychological science. After all, psychology examines how behavior is caused by mechanisms—the rattling off of genetic, unconscious, neural, cognitive, emotional, social, and yet other chains that lead, dully or not, to the things people do. If the things we do are caused by such mechanisms, how is it that we nonetheless experience willfully doing them? Our approach to this problem is to look for yet another chain—to examine the mechanisms that produce the experience of conscious will itself. In this article, we do this by exploring the possibility that the experience of will is a result of the same mental processes that people use in the perception of causality more generally. Quite simply, it may be that people experience conscious will when they interpret their own thought as the cause of their action. This idea means that people can experience conscious will quite independent of any actual causal connection between

Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations of the co-occurrence frequencies between causes and effects, interactions between physical objects, or patterns of spatial or temporal coincidence. These different modes of learning are typically thought of as distinct psychological processes and are rarely studied together, but at heart they present the same inductive challenge—identifying the unobservable mechanisms that generate observable relations between variables, objects, or events, given only sparse and limited data. We present a computational-level analysis of this inductive problem and a framework for its solution, which allows us to model all these forms of causal learning in a common language. In this framework, causal induction is the product of domain-general statistical inference guided by domain-specific prior knowledge, in the form of an abstract causal theory. We identify 3 key aspects of abstract prior knowledge—the ontology of entities, properties, and relations that organizes a domain; the plausibility of specific causal relationships; and the functional form of those relationships—and show how they provide the constraints that people need to induce useful causal models from sparse data.

An integrative framework, designed to organize the heterogeneous constructs related to "'control'; is based on 2 fundamental distinctions: (a) objective, subjective, and experiences of controt; and (b) agents, means, and ends of control. The framework is used to analyze more than 100 terms, such as sense of control, proxy control, and primary control. It is argued that although many terms reflect aspects of perceived control (both distinct and overlapping), some are more usefully considered aspects of objective control conditions (e.g., contingency), potential antecedents of perceived control (e.g., choice), potential consequences (e.g., secondary control), sources of motivation for control (e.g., mastery), or other sources of motivation (e.g., autonomy). Implications for theory, measure-ment, research, and intervention are explored. Control is important to psychological functioning. Decades of research in sociology and psychology have demonstrated that a sense of control is a robust predictor of physical and mental

When participants assess the relationship between two variables, each with levels of presence and absence, the two most robust phenomena are that: (a) observing the joint presence of the variables has the largest impact on judgment and observing joint absence has the smallest impact, and (b) participants’ prior beliefs about the variables ’ relationship influence judgment. Both phenomena represent departures from the traditional normative model (the phi coefficient or related measures) and have therefore been interpreted as systematic errors. However, both phenomena are consistent with a Bayesian approach to the task. From a Bayesian perspective: (a) joint presence is normatively more informative than joint absence if the presence of variables is rarer than their absence, and (b) failing to incorporate prior beliefs is a normative error. Empirical evidence is reported showing that joint absence is seen as more informative than joint presence when it is clear that absence of the variables, rather than their presence, is rare.

People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.

This is a reformatted version of the original dissertation, and