Behavioral regulation and the modulation of information coding in the lateral prefrontal and cingulate cortex

Mehdi Khamassi, René Quilodran, Pierre Enel, Peter F. Dominey, Emmanuel Procyk

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

To explain the high level of flexibility in primate decision-making, theoretical models often invoke reinforcement-based mechanisms, performance monitoring functions, and core neural features within frontal cortical regions. However, the underlying biological mechanisms remain unknown. In recent models, part of the regulation of behavioral control is based on meta-learning principles, for example, driving exploratory actions by varying a meta-parameter, the inverse temperature, which regulates the contrast between competing action probabilities. Here we investigate how complementary processes between lateral prefrontal cortex (LPFC) and dorsal anterior cingulate cortex (dACC) implement decision regulation during exploratory and exploitative behaviors. Model-based analyses of unit activity recorded in these 2 areas in monkeys first revealed that adaptation of the decision function is reflected in a covariation between LPFC neural activity and the control level estimated from the animal's behavior. Second, dACC more prominently encoded a reflection of outcome uncertainty useful for control regulation based on task monitoring. Model-based analyses also revealed higher information integration before feedback in LPFC, and after feedback in dACC. Overall the data support a role of dACC in integrating reinforcement-based information to regulate decision functions in LPFC. Our results thus provide biological evidence on how prefrontal cortical subregions may cooperate to regulate decision-making.

Original languageEnglish
Pages (from-to)3197-3218
Number of pages22
JournalCerebral Cortex
Volume25
Issue number9
DOIs
StatePublished - 1 Sep 2015
Externally publishedYes

Keywords

  • Adaptation
  • Cingulate
  • Decision
  • Feedback
  • Reinforcementlearning
  • Reward

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