Evaluation of ambiguous associations in the amygdala by learning the structure of the environment

Tamas J. Madarasz, Lorenzo Diaz-Mataix, Omar Akhand, Edgar A. Ycu, Joseph E. LeDoux, Joshua P. Johansen

Research output: Contribution to journalArticlepeer-review

Abstract

Recognizing predictive relationships is critical for survival, but an understanding of the underlying neural mechanisms remains elusive. In particular, it is unclear how the brain distinguishes predictive relationships from spurious ones when evidence about a relationship is ambiguous, or how it computes predictions given such uncertainty. To better understand this process, we introduced ambiguity into an associative learning task by presenting aversive outcomes both in the presence and in the absence of a predictive cue. Electrophysiological and optogenetic approaches revealed that amygdala neurons directly regulated and tracked the effects of ambiguity on learning. Contrary to established accounts of associative learning, however, interference from competing associations was not required to assess an ambiguous cue-outcome contingency. Instead, animals' behavior was explained by a normative account that evaluates different models of the environment's statistical structure. These findings suggest an alternative view of amygdala circuits in resolving ambiguity during aversive learning.

Original languageEnglish (US)
Pages (from-to)965-972
Number of pages8
JournalNature Neuroscience
Volume19
Issue number7
DOIs
StatePublished - Jul 1 2016

ASJC Scopus subject areas

  • Neuroscience(all)

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