Human reasoning in hypothesis-testing tasks like Wason's (1966, 1968) selection task has been depicted as prone to systematic biases. However, performance on this task has been assessed against a now outmoded falsificationist philosophy of science. Therefore, the experimental data is reassessed in the light of a Bayesian model of optimal data selection in inductive hypothesis testing. The model provides a rational analysis (Anderson, 1990) of the selection task that fits well with people's performance on both abstract and thematic versions of the task. The model suggests that reasoning in these tasks may be rational rather than subject to systematic bias. Over the past 30 years, results in the psychology of reasoning have raised doubts about human rationality. The assumption of human rationality has a long history. Aristotle took the capacity for rational thought to be the defining characteristic of human beings, the capacity that separated us from the animals. Descartes regarded the ability to use language and to reason as the hallmarks of the mental that separated it from the merely physical. Many contemporary philosophers of mind also appeal to a basic principle of rationality in accounting for everyday, folk psychological explanation whereby we explain each other's behavior in terms of our beliefs and desires (Cherniak, 1986; Cohen, 1981; Davidson, 1984; Dennett, 1987; but see Stich, 1990). These philosophers, both ancient and modern, share a common view of rationality: To be rational is to reason according to rules (Brown, 1989). Logic and mathematics provide the normative rules that tell us how we should reason. Rationality therefore seems to demand that the human cognitive system embodies the rules of logic and mathematics. However, results in the psychology of reasoning appear to show that people do not reason according to these rules. In both deductive (Evans, 1982, 1989;

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.

ABSTRACT: A century has passed since the publication

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Research suggests that causal judgment is influenced primarily by counterfactual or covariational reasoning. In contrast, the author of this article develops judgment dissociation theory (JDT), which predicts that these types of reasoning differ in function and can lead to divergent judgments. The actuality principle proposes that causal selections focus on antecedents that are sufficient to generate the actual outcome. The substitution principle proposes that ad hoc categorization plays a key role in counterfactual and covariational reasoning such that counterfactual selections focus on antecedents that would have been sufficient to prevent the outcome or something like it and covariational selections focus on antecedents that yield the largest increase in the probability of the outcome or something like it. The findings of 4 experiments support JDT but not the competing counterfactual and covariational accounts. If causation is the cement of the universe, as the philosopher David Hume (1740/1938) put it, then it is fair to say that causal knowledge is the cement that binds together each person’s representational universe. Causal reasoning—the process that generates this glue—confers many functional advantages. In virtually every sphere of human interest, our abilities to learn and categorize

Abstract. Parapsychology, the laboratory study of psychic phenomena, has had its history interwoven with that of statistics. Many of the controversies in parapsychology have focused on statistical issues, and statistical models have played an integral role in the experimental work. Recently, parapsychologists have been using meta-analysis as a tool for synthesizing large bodies of work. This paper presents an overview of the use of statistics in parapsychology and offers a summary of the meta-analyses that have been conducted. It begins with some anecdotal information about the involvement of statistics and statisticians with the early history of parapsychology. Next, it is argued that most nonstatisticians do not appreciate the connection between power and "successful " replication of experimental effects. Returning to parapsychology, a particular experimental regime is examined by summarizing an extended debate over the interpretation of the results. A new set of experiments designed to resolve the debate is then reviewed. Finally,

this paper and the writing of the paper were supported, in part, by grants to Dominic W. Massaro from the Public Health Service (PHS R01 NS 20314), the National Science Foundation (BNS 8812728), a James McKeen Cattell Fellowship, and the graduate division of the University of California, Santa Cruz. Cohen & Massaro On the Similarity of Categorization Models 2

The culture-only (0 % genetic–100 % environmental) and the hereditarian (50% genetic–50 % environmental) models of the causes of mean Black–White differences in cognitive ability are compared and contrasted across 10 categories of evidence: the worldwide distribution of test scores, g factor of mental ability, heritability, brain size and cognitive ability, transracial adoption, racial admixture, regression, related life-history traits, human origins research, and hypothesized environmental variables. The new evidence reviewed here points to some genetic component in Black–White differences in mean IQ. The implication for public policy is that the discrimination model (i.e., Black–White differences in socially valued outcomes will be equal barring discrimination) must be tempered by a distributional model (i.e., Black–White outcomes reflect underlying group characteristics). Section 1: Background Throughout the history of psychology, no question has been so persistent or so resistant to resolution as that of the relative roles of nature and nurture in causing individual and group differences in cognitive ability (Degler, 1991;

This discussion outlines and implements the theory of an inductive inference technique that automatically discovers classes among large numbers of input patterns, generates operational definitions of class membership with explicit levels of confidence, creates a continuously updated "self-organized" coded hierarchical taxonomic classification of patterns, and recognizes to which already discovered class or classes, if any, a new input belongs in an information-theoretically efficient way. Relationships to the "scientific method" and learning are discussed.

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