This article presents a theory of categorization that accounts for the effects of causal knowledge that relates the features of categories. According to causal-model theory, people explicitly represent the probabilistic causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught causal knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches. For the last several decades, research on the topic of categorization has focused on the problem of learning new categories via examples of category members, that is, from empirical observations. The result has been a host of categorization models that are based on representational ideas such as central prototypes, stored exemplars, and variabilized rules, and on processing principles such as similarity, that have considerable explanatory power and experimental support. More recently, the influence of the prior “theoretical ” knowledge that learners often contribute to their representations of categories has also been a topic of study (Carey,

ents. A Bayesian model may be considered an optimal account of induction that, ideally, would make predictions that bear some resemblance to what people actually do in inductive reasoning tasks. Assumptions for Rational Analysis The Bayesian model presented here is meant to be a computational-level account (Marr, 1982), in that it is a description of the task that is performed in evaluating inductive arguments, rather than a detailed process-level account. In this way, the Bayesian account fulfils the first step of Anderson's (1990) scheme for rational analyses, specifying the goals of the system during a particular task. However, this account does not contain other elements of a rational analysis, such as a description of the environment. For inductive reasoning, the environment might be something as large as all properties of all objects, or all beliefs about properties of objects, and it is not clear how a description of the environment would be undertaken. The Bayesian model for in

ositive side, though, the second person might have some advantage over the first person in learning how to shift gears, because the second person would not have to overcome negative transfer from experience with automatic transmissions. As another example, imagine that you are an explorer visiting a remote island, with the purpose of writing a book about the people that you see there. You bring to this island many forms of prior knowledge that will guide you in learning about these new people. For example, based on your experiences in other places, you would expect to see males and females, younger and older people, shy people and arrogant people. You would also have certain hypotheses at a more abstract level, for example, that the clothes that someone wears may be related to the person's age and gender. (Goodman, 1955, referred to such abstract hypotheses as overhypotheses.) In a way, these biases due to previous knowledge might seem to be undesirable. After all, wouldn't be it be be

The paper presents a model suggesting that inductive generalizations in young children could be a function of similarity among compared stimuli. Predictions derived from the model were tested in two experiments where young children and preadolescents were presented with triads of schematic faces (a Target and two Test stimuli) that varied in perceptual similarity, with one of the Test stimuli sharing a linguistic label with the Target. Participants were taught a biological property about the Target and asked to generalize the property to one of the Test stimuli. Results from both experiments support predictions, indicating that for young children, proportions of label-based generalizations varied with featural overlap among the compared stimuli. There were also developmental differences found in effects of labels: while for young children these effects varied with featural overlap, preadolescents relied solely on linguistic labels when performing inductive generalizations.

... This paper shows that, for the average student, the conceptual changes sketched here are not completed until well into the second decade of life.

This essay examines the proposal that psychological essentialism results from a history of natural selection acting on human representation and inference systems. It has been argued that the features that distinguish essentialist representational systems are especially well suited for representing natural kinds. If the evolved function of essentialism is to exploit the rich inductive potential of such kinds, then it must be subserved by cognitive mechanisms that carry out at least three distinct functions: identifying these kinds in the envi - ronment, constructing essentialized representations of them, and constraining inductive infer - ences about kinds. Moreover, there are different kinds of kinds, ranging from nonliving sub - stances to biological taxa to within-species kinds such as sex, and the causal processes that render these categories coherent for the purposes of inductive generalization vary. If the evolved function of essentialism is to support inductive generalization under ignorance of true causes, and if kinds of kinds vary in the implicit assumptions that support valid inductive inferences about them, then we expect different, functionally incompatible modes of essen - tialist thinking for different kinds. In particular, there should be differences in how biological and nonbiological substances, biological taxa, and biological and social role kinds are essen - tialized. The functional differences between these kinds of essentialism are discussed.

Teleological explanations (TEs) account for the existence or properties of an entity in terms of a function: we have hearts because they pump blood, and telephones for communication. While many teleological explanations seem appropriate, others are clearly not warranted—for example, that rain exists for plants to grow. Five experiments explore the theoretical commitments that underlie teleological explanations. With the analysis of [Wright, L. (1976). Teleological Explanations. Berkeley, CA: University of California Press] from philosophy as a point of departure, we examine in Experiment 1 whether teleological explanations are interpreted causally, and confirm that TEs are only accepted when the function invoked in the explanation played a causal role in bringing about what is being explained. However, we also find that playing a causal role is not sufficient for all participants to accept TEs. Experiment 2 shows that this is not because participants fail to appreciate the causal structure of the scenarios used as stimuli. In Experiments 3–5 we show that the additional requirement for TE acceptance is that the process by which the function played a causal role must be general in the sense of conforming to a predictable pattern. These findings motivate a proposal, Explanation for Export, which suggests that a psychological function of explanation is to highlight information likely to subserve future prediction and intervention. We relate our proposal to normative accounts of explanation from philosophy of science, as well as to claims from psychology and artificial intelligence.

The paper compares predictions derived from the similaritybased and the theory-based accounts of young children's induction. The former predicts the primacy of induction from one single entity to another single entity (one-to-one induction), whereas the latter does not predict such primacy. Predictions were tested in three experiments where 4-5 yearolds and 11-12 year-olds were asked to perform inductive generalization of biological properties. Participants could generalize properties either from a single animal to another single animal (one-to-one induction) or from a group of animals to a single animal (many-to-one induction). Experiments 1 and 2 revealed that under various stimuli presentation conditions, young children exhibited a strong preference of one-to-one induction, performing generalizations in a similarity-based manner. At the same time, preadolescents exhibited a strong preference of manyto -one induction, performing generalizations in a theorybased manner. In Experiment 3, an alternative explanation that one-to-one induction stems from a tendency to match quantifiers or label endings was tested and eliminated. Results are discussed in relation to cognitive and developmental aspects of inductive inference.

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The tendency to test outcomes that are predicted by our current theory (the confirmation bias) is one of the best-known biases of human decision making. We prove that the confirmation bias is an optimal strategy for testing hypotheses when those hypotheses are deterministic, each making a single prediction about the next event in a sequence. Our proof applies for two normative standards commonly used for evaluating hypothesis testing: maximizing expected information gain and maximizing the probability of falsifying the current hypothesis. This analysis rests on two assumptions: (1) that people predict the next event in a sequence in a way that is consistent with Bayesian inference and (2) when testing hypotheses, people test the hypothesis that they assign highest posterior probability. We present four behavioral experiments that support these assumptions, showing that a simple Bayesian model can capture people’s predictions about numerical sequences (Experiments 1 and 2), and that we can alter the hypotheses that people choose to test by manipulating the prior probability of those hypotheses