Journal article

Sample size, number of categories and sampling assumptions: Exploring some differences between categorization and generalization

Andrew T Hendrickson, Amy Perfors, Danielle J Navarro, Keith Ransom



Categorization and generalization are fundamentally related inference problems. Yet leading computational models of categorization (as exemplified by, e.g., Nosofsky, 1986) and generalization (as exemplified by, e.g., Tenenbaum and Griffiths, 2001) make qualitatively different predictions about how inference should change as a function of the number of items. Assuming all else is equal, categorization models predict that increasing the number of items in a category increases the chance of assigning a new item to that category; generalization models predict a decrease, or category tightening with additional exemplars. This paper investigates this discrepancy, showing that people do indeed per..

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Awarded by ARC

Funding Acknowledgements

DJN received salary support from ARC grant FT110100431 and AFP from ARC grant DE120102378. Research costs and salary support for ATH were funded through ARC grants DP110104949 and DP150103280. KR was supported by an Australian Government Research Training Program Scholarship. Preliminary versions of this work were presented at the 48th and 50th Annual Meeting of the Society of Mathematical Psychology. We would like to thank Robert Nosofsky and two anonymous reviewers for their helpful comments on a previous version of this article.