The role of sampling assumptions in generalization with multiple categories
Files
(Published version)
Date
2013
Authors
Vong, W.
Hendrickson, A.
Perfors, A.
Navarro, D.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of the 35th Annual Meeting of the Cognitive Science Society, 2013 / pp.3699-3704
Statement of Responsibility
Wai Keen Vong, Andrew T. Hendrickson, Amy Perfors, Daniel J. Navarro
Conference Name
Annual Meeting of the Cognitive Science Society (2013 : Berlin, Germany)
Abstract
The extent to which people learning categories generalize on the basis of observed instances should depend in part on their beliefs about how the instances were sampled from the world. Bayesian models of sampling have been successful in predicting the counter-intuitive finding that under certain situations generalization can decrease as more instances of a category are encountered. This has only been shown in tasks were instances are all from the same category, but contrasts with the predictions from most standard models of categorization (such as the Generalized Context Model) that predict when multiple categories exist, people are more likely to generalize to categories that have more instances when distances between categories is controlled. In this current work we show that in both one- and two-category scenarios, people adjust their generalization behavior based on cover story and number of instances. These patterns of generalization at an individual level for both one- and two-category scenarios were well accounted for by a Bayesian model that relies on a mixture of sampling assumptions.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
© Authors