Discovering Inductive Biases in Categorization through Iterated Learning

Kevin R. Canini, Thomas L. Griffiths, Wolf Vanpaemel, Michael L. Kalish

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Progress in studying human categorization has typically involved comparing generalization judgments made by people to those made by models for a variety of training conditions. In this paper, we explore an alternative method for understanding human category learning—iterated learning—which can directly expose the inductive biases of human learners and categorization models. Using a variety of stimulus sets, we compare the results of iterated learning experiments with human learners to results from two prominent classes of computational models: prototype models and exemplar models. Our results indicate that human learning is not perfectly captured by either type of model, lending support to the theory that people use intermediate representations between these two extremes.

Original languageEnglish (US)
Title of host publicationExpanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011
EditorsLaura Carlson, Christoph Hoelscher, Thomas F. Shipley
PublisherThe Cognitive Science Society
Pages1667-1672
Number of pages6
ISBN (Electronic)9780976831877
StatePublished - 2011
Externally publishedYes
Event33rd Annual Meeting of the Cognitive Science Society: Expanding the Space of Cognitive Science, CogSci 2011 - Boston, United States
Duration: Jul 20 2011Jul 23 2011

Publication series

NameExpanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011

Conference

Conference33rd Annual Meeting of the Cognitive Science Society: Expanding the Space of Cognitive Science, CogSci 2011
Country/TerritoryUnited States
CityBoston
Period7/20/117/23/11

Keywords

  • Bayesian methods
  • categorization
  • inductive bias
  • iterated learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

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