A causal-model theory of conceptual representation and categorization (2003)
| Venue: | Journal of Experimental Psychology: Learning, Memory, and Cognition |
| Citations: | 34 - 8 self |
BibTeX
@ARTICLE{Rehder03acausal-model,
author = {Bob Rehder},
title = {A causal-model theory of conceptual representation and categorization},
journal = {Journal of Experimental Psychology: Learning, Memory, and Cognition},
year = {2003},
volume = {29},
pages = {1141--1159}
}
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Abstract
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,
