Russell C Burnett | PubMed Health

PubMed2005

Ideal is typical.

A well-established finding in research on concepts and categories is that some members are rated as better or more typical examples than others. It is generally thought that typicality reflects centrality, that is, that typical examples are those that are similar to many other members of the category. This interpretation of typicality is based on studies in which participants had little knowledge about the relevant categories. In the present study, experienced fishermen were asked to give goodness-of-example ratings to familiar freshwater fish. These fishermen were of two cultural groups with somewhat different goals and ideals. Typicality was well predicted by fishes' desirability and poorly predicted by their centrality. Further, the two cultural groups differed in their typicality ratings in ways that corresponded to their different goals and ideals. For knowledgeable reasoners typicality in natural taxonomic categories appears based on ideals rather than on centrality.

Animals↗

PubMed2004

Feature inference and the causal structure of categories.

The purpose of this article was to establish how theoretical category knowledge-specifically, knowledge of the causal relations that link the features of categories-supports the ability to infer the presence of unobserved features. Our experiments were designed to test proposals that causal knowledge is represented psychologically as Bayesian networks. In five experiments we found that Bayes' nets generally predicted participants' feature inferences quite well. However, we also observed a pervasive violation of one of the defining principles of Bayes' nets-the causal Markov condition-because the presence of characteristic features invariably led participants to infer yet another characteristic feature. We argue that this effect arises from a domain-general bias to assume the presence of underlying mechanisms associated with the category. Specifically, people take an exemplar to be a "well functioning" category member when it has most or all of the category's characteristic features, and thus are likely to infer a characteristic value on an unobserved dimension.

Analysis of Variance↗