Abstract
We investigated whether the representation of relational categories is different from that of featural categories. Earlier work has suggested an extreme-value hypothesis: when a category is defined in terms of a relation, exemplars with exaggerated values along this stimulus dimension are judged as better members of the category. Featural categories, on the other hand, are not exaggerated. To test this hypothesis, we trained participants to categorize two fictional diseases defined either by a deterministic relation or a deterministic feature. After the categorization task was mastered up to a predefined learning criterion, we provided a graphical user interface that enabled participants to construct good examples of the acquired categories by adjusting the stimulus attributes. We constructed a novel index of relational exaggeration based on residual deviations from a non-exaggerated response strategy. These results supported the extreme-value hypothesis. This replicates and extends an earlier quasi-experimental study (Du et al., 2021).
Original language | English (US) |
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Pages | 3333-3338 |
Number of pages | 6 |
State | Published - 2022 |
Event | 44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022 - Toronto, Canada Duration: Jul 27 2022 → Jul 30 2022 |
Conference
Conference | 44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022 |
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Country/Territory | Canada |
City | Toronto |
Period | 7/27/22 → 7/30/22 |
Keywords
- Category learning
- extreme-value hypothesis
- relations
- typicality
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Human-Computer Interaction
- Cognitive Neuroscience