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This article reviews two aspects of human learning: (1) people draw inferences that appear to rely on hierarchical conceptual representations; (2) some categories are much easier to learn than others given the same number of exemplars, and some categories remain difficult despite extensive training. Both of these results are difficult to reconcile with a learning and categorization system that operates only on specific exemplars. More generally, the article argues that specifying the empirical phenomena that a radical exemplar does not predict would aid in clarifying the radical exemplar proposal.
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Lions, tigers and bears: Conveying a superordinate category without a superordinate label.
We asked whether categories expressed through lists of salient exemplars (e.g., car, truck, boat, etc.) convey the same meaning as categories expressed through conventional superordinate nouns (e.g., vehicles). We asked English speakers to list category members, with one group given superordinate labels like vehicles and the other group given only a list of salient exemplars. We found that the responses of the group given labels were more related, more typical, and less diverse than the responses of the group given exemplars. This result suggests that when people do not see a superordinate label, the categories that they infer are less well aligned across participants. In addition, categories inferred based on exemplars may be broader in general than categories given by superordinate labels.
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- Award ID(s):
- 2020969
- PAR ID:
- 10302200
- Date Published:
- Journal Name:
- Proceedings of the Annual Meeting of the Cognitive Science Society
- Volume:
- 43
- Page Range / eLocation ID:
- 2936–2942
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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