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One of the major problems in categorization research is the lack of systematic ways of constraining feature weights. We propose one method of operationalizing feature centrality, a causal status hypothesis which states that a cause feature is judged to be more central than its effect feature in categorization. In Experiment 1, participants learned a novel(More)
The order in which people receive information has a substantial effect on subsequent judgment and inference. Our focus is on the order of covariation evidence in causal learning. The first experiment shows that the initial presentation of evidence suggesting a generative causal relationship (the joint presence or joint absence of cause and effect) leads to(More)
Traditional approaches to causal attribution propose that information about covariation of factors is used to identify causes of events. In contrast, we present a series of studies showing that people seek out and prefer information about causal mechanisms rather than information about covariation. Experiments 1, 2 and 3 asked subjects to indicate the kind(More)
The current study examined the causal status effect (weighing cause features more than effect features in categorization) in children. Adults (Study 1) and 7-9-year-old children (Study 2) learned descriptions of novel animals, in which one feature caused two other features. When asked to determine which transfer item was more likely to be an example of the(More)
In the present study, we examine what types of feature correlations are salient in our conceptual representations. It was hypothesized that of all possible feature pairs, those that are explicitly recognized as correlated (i.e., explicit pairs) and affect typicality judgments are the ones that are more likely theory based than are those that are not(More)
Dealing with alternative causes is necessary to avoid making inaccurate causal inferences from covariation data. However, information about alternative causes is frequently unavailable, rendering them unobserved. The current article reviews the way in which current learning models deal, or could deal, with unobserved causes. A new model of causal learning,(More)
Existing models of causal induction primarily rely on the contingency between the presence and the absence of a causal candidate and an effect. Yet, classification of observations into these four types of covariation data may not be straightforward because (a) most causal candidates, in real life, are continuous with ambiguous, intermediate values and(More)