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Psychonomic Bulletin & Review

, Volume 14, Issue 4, pp 560–576 | Cite as

The divergent autoencoder (DIVA) model of category learning

  • Kenneth J. KurtzEmail author
Theoretical and Review Articles

Abstract

A novel theoretical approach to human category learning is proposed in which categories are represented as coordinated statistical models of the properties of the members. Key elements of the account are learning to recode inputs as task-constrained principle components and evaluating category membership in terms of model fit—that is, the fidelity of the reconstruction after recoding and decoding the stimulus. The approach is implemented as a computational model called DIVA (for DIVergent Autoencoder), an artificial neural network that uses reconstructive learning to solve N-way classification tasks. DIVA shows good qualitative fits to benchmark human learning data and provides a compelling theoretical alternative to established models.

Keywords

Principle Component Analysis Hide Node Category Structure Category Learning Training Item 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Psychonomic Society, Inc. 2007

Authors and Affiliations

  1. 1.Department of PsychologyBinghamton UniversityBinghamton

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