Valuation of uncertain and delayed rewards in primate prefrontal cortex

Soyoun Kim, Jaewon Hwang, Hyojung Seo, Daeyeol Lee

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

Humans and animals often must choose between rewards that differ in their qualities, magnitudes, immediacy, and likelihood, and must estimate these multiple reward parameters from their experience. However, the neural basis for such complex decision making is not well understood. To understand the role of the primate prefrontal cortex in determining the subjective value of delayed or uncertain reward, we examined the activity of individual prefrontal neurons during an inter-temporal choice task and a computer-simulated competitive game. Consistent with the findings from previous studies in humans and other animals, the monkey's behaviors during inter-temporal choice were well accounted for by a hyperbolic discount function. In addition, the activity of many neurons in the lateral prefrontal cortex reflected the signals related to the magnitude and delay of the reward expected from a particular action, and often encoded the difference in temporally discounted values that predicted the animal's choice. During a computerized matching pennies game, the animals approximated the optimal strategy, known as Nash equilibrium, using a reinforcement learning algorithm. We also found that many neurons in the lateral prefrontal cortex conveyed the signals related to the animal's previous choices and their outcomes, suggesting that this cortical area might play an important role in forming associations between actions and their outcomes. These results show that the primate lateral prefrontal cortex plays a central role in estimating the values of alternative actions based on multiple sources of information.

Original languageEnglish (US)
Pages (from-to)294-304
Number of pages11
JournalNeural Networks
Volume22
Issue number3
DOIs
StatePublished - Apr 2009
Externally publishedYes

Keywords

  • Game theory
  • Inter-temporal choice
  • Reinforcement learning
  • Temporal discounting
  • Utility theory

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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