Evaluating contingencies by a dual system of learning the structure and the parameters of the environment

Tamas J. Madarasz, Joseph E. LeDoux, Joshua P. Johansen

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

Abstract

How does the brain identify stimuli that are relevant for predicting important events and how does it distinguish spurious relationships from truly predictive ones? We examined two contrasting theoretical frameworks: in the first, learning proceeds by considering a fixed hypothesis of the environment's statistical structure (the set of predictive and causal relationships) and adjusting strength parameters for these relationships to optimize predictions. In contrast, the second approach directly assesses ambiguity in predictive relationships by evaluating multiple hypothesis of the environment's statistical structure. We compared these frameworks in an animal model of aversive conditioning, allowing us to also manipulate the underlying brain systems. We show that when facing novel predictive stimuli, rats initially adopt a structure learning strategy, but switch to updating parameters during subsequent learning.

Original languageEnglish (US)
Title of host publicationProceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015
EditorsDavid C. Noelle, Rick Dale, Anne Warlaumont, Jeff Yoshimi, Teenie Matlock, Carolyn D. Jennings, Paul P. Maglio
PublisherThe Cognitive Science Society
Pages1464-1469
Number of pages6
ISBN (Electronic)9780991196722
StatePublished - 2015
Event37th Annual Meeting of the Cognitive Science Society: Mind, Technology, and Society, CogSci 2015 - Pasadena, United States
Duration: Jul 23 2015Jul 25 2015

Publication series

NameProceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015

Conference

Conference37th Annual Meeting of the Cognitive Science Society: Mind, Technology, and Society, CogSci 2015
Country/TerritoryUnited States
CityPasadena
Period7/23/157/25/15

Keywords

  • Animal cognition
  • Bayesian modeling
  • Causal Reasoning
  • Representation

ASJC Scopus subject areas

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

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