Similarity and discrimination in classical conditioning: A latent variable account

Aaron C. Courville, Nathaniel D. Daw, David S. Touretzky

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

16 Scopus citations

Abstract

We propose a probabilistic, generative account of configural learning phenomena in classical conditioning. Configural learning experiments probe how animals discriminate and generalize between patterns of simultaneously presented stimuli (such as tones and lights) that are differentially predictive of reinforcement. Previous models of these issues have been successful more on a phenomenological than an explanatory level: They reproduce experimental findings but, lacking formal foundations, provide scant basis for understanding why animals behave as they do. We present a theory that clarifies seemingly arbitrary aspects of previous models while also capturing a broader set of data. Key patterns of data, e.g. concerning animals' readiness to distinguish patterns with varying degrees of overlap, are shown to follow fromstatistical inference.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004
PublisherNeural information processing systems foundation
ISBN (Print)0262195348, 9780262195348
StatePublished - Jan 1 2005
Externally publishedYes
Event18th Annual Conference on Neural Information Processing Systems, NIPS 2004 - Vancouver, BC, Canada
Duration: Dec 13 2004Dec 16 2004

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other18th Annual Conference on Neural Information Processing Systems, NIPS 2004
CountryCanada
CityVancouver, BC
Period12/13/0412/16/04

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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