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.

This paper surveys and evaluates some techniques for eliciting requirements of computer-based systems, paying particular attention to how they deal with social issues. The methods surveyed include introspection, interviews, questionnaires, and protocol, conversation, interaction, and discourse analyses. Although they are relatively untried in Requirements Engineering, we believe there is much promise in the last three techniques, which grew out of ethnomethodology and sociolinguistics. In particular, they can elicit tacit knowledge by observing actual interactions in the workplace, and can also be applied to the system development process itself.

The control of routine action is a complex process subject both to minor lapses in normals and to more severe breakdown followingcertain forms of neurological damage. A number of recent empirical studies (e.g. Humphreys & Ford, 1998; Schwartz et al., 1991, 1995, 1998) have examined the details of breakdown in certain classes of patient, and attempted to relate the findings to existing psychological theory. This paper complements those studies by presenting a computational model of the selection of routine actions based on competitive activation within a hierarchically organised network of action schemas (cf. Norman & Shallice, 1980, 1986). Simulations are reported which demonstrate that the model is capable of organised sequential action selection in a complex naturalistic domain. It is further demonstrated that, after lesioning, the model exhibits behaviour qualitatively equivalent to that observed by Schwartz et al., in their action disorganisation syndrome patients.

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 chapter presents an overview of goals and directions in machine learning research, and is intended to serve as a conceptual road map to other chapters. It investigates intrinsic aspects of the learning process, classifies current lines of research, and presents the author's view of the relationship among learning paradigms, strategies and orientations.

This article argues that (a) ego, or self, is an organization of knowledge, (b) ego is characterized by cognitive biases strikingly analogous to totalitarian information-control strategies, and (c) these totalitarian-ego biases junction to preserve organization in cognitive structures. Ego's cognitive biases are egocentricity (self as the focus of knowledge), "beneffectance" (perception of responsibility for desired, but not undesired, outcomes), and cognitive conservatism (resistance to cognitive change). In addition to being pervasively evident in recent studies of normal human cognition, these three biases are found in actively functioning, higher level organizations of knowledge, perhaps best exemplified by theoretical paradigms in science. The thesis that egocentricity, beneffectance, and

The design-based approach is a methodology for investigating mechanisms capable of generating mental phenomena, whether introspectively or externally observed, and whether they occur in humans, other animals or robots. The study of designs satisfying requirements for autonomous agency can provide new deep theoretical insights at the information processing level of description of mental mechanisms. Designs for working systems (whether on paper or implemented on computers) can systematically explicate old explanatory concepts and generate new concepts that allow new and richer interpretations of human phenomena. To illustrate this, some aspects of human grief are analysed in terms of a particular information processing architecture being explored in our research group. We do not claim that this architecture is part of the causal structure of the human mind; rather, it represents an early stage in the iterative search for a deeper and more general architecture, capable of explaining more...

Online spaces that enable shared public interpersonal communications are of significant social, organizational, and economic importance. In this paper, a theoretical model and associated unobtrusive method are proposed for researching the relationship between online spaces and the behavior they host. The model focuses on the collective impact that individual information-overload coping strategies have on the dynamics of open, interactive public online group discourse. Empirical research was undertaken to assess the validity of both the method and the model, based on the analysis of over 2.65 million postings to 600 Usenet newsgroups over a 6-month period. Our findings support the assertion that individual strategies for coping with “information overload” have an observable impact on large-scale online group discourse. Evidence was found for the hypotheses that: (1) users are more likely to respond to simpler messages in overloaded mass interaction; (2) users are more likely to end active participation as the overloading of mass interaction increases; and (3) users are more likely to generate simpler responses as the overloading of mass interaction grows. The theoretical model outlined offers insight into aspects of computer-mediated communication tool usability, technology design, and provides a road map for future empirical research.

this document. 9.5.2 A comparison of CUE and libido