@inproceedings{d2cb96661a7442c6a4289d04039dee68,
title = "Evaluating contingencies by a dual system of learning the structure and the parameters of the environment",
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.",
keywords = "Animal cognition, Bayesian modeling, Causal Reasoning, Representation",
author = "Madarasz, {Tamas J.} and LeDoux, {Joseph E.} and Johansen, {Joshua P.}",
note = "Publisher Copyright: {\textcopyright} Cognitive Science Society, CogSci 2015.All rights reserved.; 37th Annual Meeting of the Cognitive Science Society: Mind, Technology, and Society, CogSci 2015 ; Conference date: 23-07-2015 Through 25-07-2015",
year = "2015",
language = "English (US)",
series = "Proceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015",
publisher = "The Cognitive Science Society",
pages = "1464--1469",
editor = "Noelle, {David C.} and Rick Dale and Anne Warlaumont and Jeff Yoshimi and Teenie Matlock and Jennings, {Carolyn D.} and Maglio, {Paul P.}",
booktitle = "Proceedings of the 37th Annual Meeting of the Cognitive Science Society, CogSci 2015",
}