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

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ABSTRACT: A main goal of science education is to help students learn to reason scientifically. A main way to facilitate learning is to engage students in inquiry activities such as conducting experiments. This article presents a theoretical framework for evaluating inquiry tasks in terms of how similar they are to authentic science. The framework helps identify the respects in which these reasoning tasks are similar to and different from real scientific research. The framework is based on a recent theory of reasoning, models-of-data theory. We argue that inquiry tasks commonly used in schools evoke reasoning processes that are qualitatively different from the processes employed in real scientific inquiry. Moreover, school reasoning tasks appear to be based on an epistemology that differs from the epistemology of authentic science. Inquiry tasks developed by researchers have increasingly captured features of authentic science, but further improvement is still possible. We conclude with a discussion of the implications of our analysis for research, assessment, and instruction.

ctive, and will ignore much of the fine detail in the attempt to frame a rather general picture. All of this will be prefaced by a consideration of the psychological consequences of trust for the individual, and a brief discussion of what I am taking trust to be. <<32>> I The background to the observations and proposals which follow is the clear and simple fact that, without trust, the everyday social life which we take for granted is simply not possible. Luhmann makes this point in the opening chapter of his Trust and Power (1979), and Garfinkel (1963) illustrated the importance of trusting other persons claims as to the nature of social reality in his (by now infamous) rule-breaching studies. In these, Garfinkels confederates behaved as if the nature of a social situation was other than that which it might be taken to be by the unwitting subjects with whom they were interacting. The subjects (or, perhaps more appropriately, victims)

Experimental philosophy is the name for a recent movement whose participants use the methods of experimental psychology to probe the way people think about philosophical issues and then examine how the results of such studies bear on traditional philosophical debates. Given both the breadth of the research being carried out by experimental philosophers and the controversial nature of some of their central methodological assumptions, it is of no surprise that their work has recently come under attack. In this paper we respond to some criticisms of experimental philosophy that have recently been put forward by Antti Kauppinen. Unlike the critics of experimental philosophy, we do not think the fledgling movement either will or should fall before it has even had a chance to rise up to explain what it is, what it seeks to do (and not to do), and exactly how it plans to do it. Filling in some of the salient details is the main goal of the present paper.

We investigate a topic at the interface of machine learning and cognitive science. Human active learning, where learners can actively query the world for information, is contrasted with passive learning from random examples. Furthermore, we compare human active learning performance with predictions from statistical learning theory. We conduct a series of human category learning experiments inspired by a machine learning task for which active and passive learning error bounds are well understood, and dramatically distinct. Our results indicate that humans are capable of actively selecting informative queries, and in doing so learn better and faster than if they are given random training data, as predicted by learning theory. However, the improvement over passive learning is not as dramatic as that achieved by machine active learning algorithms. To the best of our knowledge, this is the first quantitative study comparing human category learning in active versus passive settings. 1

Disciplines that study science are relatively well established in philosophy, history, and sociology. Psychology of science, by comparison, is a late bloomer but has recently shown signs of codification. The authors further this codification by integrating and reviewing the growing literature in the developmental, cognitive, personality, and social psychology of science. Only by integrating the findings from each of these perspectives can the basic questions in the study of scientific behavior be answered: Who becomes a scientist and what role do biology, family, school, and gender play? Are productivity, scientific reasoning, and theory acceptance influenced by age? What thought processes and heuristics lead to successful discovery? What personality characteristics distinguish scientists from nonscientists and eminent from less eminent scientists? Finally, how do intergroup relations and social forces influence scientific behavior? A model that integrates the consensual empirical findings from the psychology of science is pro-posed. Without the addition of a psychological dimension, I believe, it is impossible to appreciate fully the essence

We use an in vivo methodology to investigate the responses of scientists to anomalies. Protocols of 3 scientists performing data analysis in 2 domains were analyzed.