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.

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

Consumer knowledge is seldom complete or errorless. Therefore, the self-assessed validity of knowledge and consequent knowledge calibration (i.e., the correspondence between self-assessed and actual validity) is an important issue for the study of consumer decision making. In this article we describe methods and models used in calibration research. We then review a wide variety of empirical results indicating that high levels of calibration are achieved rarely, moderate levels that include some degree of systematic bias are the norm, and confidence and accuracy are sometimes completely uncorrelated. Finally, we examine the explanations of miscalibration and offer suggestions for future research. Consumers are overconfident—they think they know more than they actually do. Our simple goal is to evaluate this proposition. Ultimately, we conclude that overconfidence is indeed a robust phenomenon and can be adopted by researchers as a stylized fact about human cognition; however, there are critical qualifications and

The dynamics model, which is based on L. Talmy’s (1988) theory of force dynamics, characterizes causation as a pattern of forces and a position vector. In contrast to counterfactual and probabilistic models, the dynamics model naturally distinguishes between different cause-related concepts and explains the induction of causal relationships from single observations. Support for the model is provided in experiments in which participants categorized 3-D animations of realistically rendered objects with trajectories that were wholly determined by the force vectors entered into a physics simulator. Experiments 1–3 showed that causal judgments are based on several forces, not just one. Experiment 4 demonstrated that people compute the resultant of forces using a qualitative decision rule. Experiments 5 and 6 showed that a dynamics approach extends to the representation of social causation. Implications for the relationship between causation and time are discussed.

Philosophical theories summarized here include regularity and necessity theories from Hume to the present; manipulability theory; the theory of powerful particulars; causation as connected changes within a defined state of affairs; departures from "normal " events or from some standard for compar-ison; causation as a transfer of something between objects; and causal propagation and production. Issues found in this literature and of relevance for psychology include whether actual causal relations can be perceived or known; what sorts of things people believe can be causes; different levels of causal analysis; the distinction between the causal relation itself and cues to causal relations; causal frames or fields; internal and external causes; and understanding of causation in different realms of the world, such as the natural and artificial realms. A full theory of causal inference by laypeople should address all of these issues. The main purpose of this article is to survey philosophical theories of causation in a manner intended to be suitable for psychologists interested in causation. The article has two sec-tions: The first presents brief summaries of philosophical theo-ries of causation from Aristotle to the present. In the second, issues found in the philosophical literature are used to suggest new approaches to the study of causation in psychology. Philosophical Theories of Causation Several psychologists have written about selected philosophi-cal theories of causation (Cook & Campbell, 1979; Einhorn &

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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.

Introduction: Characterizing the Questions of causal reasoning This chapter describes the mechanism approach to the study of causal reasoning. We will first offer a characterization of the central issues in human causal reasoning, and will discuss how the mechanism approach addresses these issues. In the course of this presentation, we will frequently compare the mechanism approach with alternative accounts based on analyses of covariation, or what is often termed the regularity view. The aims of this chapter are the following: to explain why covariation and mechanism are different, to discuss why such a distinction is actually a useful tool for our understanding of causal reasoning, and to explicate the complementary nature of the two views. Before presenting these two approaches, it is necessary first to offer a description of the domain or problem itself : namely, what are these alternative approaches to? Although there are a number of different ways of characterizing the study of

According to the two-stage model of voluntary action, the ability to perform voluntary action is acquired in two sequential steps. Firstly, associations are acquired between representations of movements and of the e#ects that frequently follow them. Secondly, the anticipation or perception of an acquired action effect primes the movement that has been learnt to produce this effect; the acquired action-e#ect associations thus mediate the selection of actions that are most appropriate to achieve an intended action goal. If action-effect learning has an associative basis, it should be influenced by factors that are known to a#ect instrumental learning, such as the temporal contiguity and the probabilistic contingency of movement and effect. In two experiments, the contiguity or the contingency between key presses and subsequent tones was manipulated in various ways. As expected, both factors affected the acquisition of action-effect relations as assessed by the potency of action effects to prime the corresponding action in a later behavioral test. In particular, evidence of action-effect associations was obtained only if the effect of the action was delayed for no more than 1 s, if the effect appeared more often in the presence than in the absence of the action, or if action and effect were entirely uncorrelated but the effect appeared very often. These findings support the assumption that the control of voluntary actions is based on action-effect representations that are acquired by associative learning mechanisms.

Associative learning theory postulates two main determinants for human causal learning: contingency and contiguity. In line with such an account, participants in Shanks, Pearson, and Dickinson (1989) failed to discover causal relations involving delays of more than two seconds. More recent research has shown that the impact of contiguity and delay is mediated by prior knowledge about the timeframe of the causal relation in question. Buehner and May (2002, 2003) demonstrated that the detrimental effect of delay can be significantly reduced if reasoners are aware of potential delays. Here we demonstrate for the first time that the negative influence of delay can be abolished completely by a subtle change in the experimental instructions. Temporal contiguity is thus not essential for human causal learning. An associative learning analysis of human causal learning postulates two main determinants of judged causal strength: the contingency and the contiguity between the potential cause (cue) and the effect (outcome) (e.g., see Shanks & Dickinson, 1987). Empirical research in the last decades has largely focused on the congruency between cue–outcome contingency and judged causal strength. Early reports suggested that human causal judgements closely track variations in cue-outcome contingency (e.g., Jenkins & Ward, 1965), while more recent studies revealed a more complex picture (e.g., Chapman & Robbins, 1990). In fact, the theoretical and empirical relations between contingency and judged causality are still the subject of a hot debate