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The prefrontal cortex and hybrid learning during iterative competitive games
Author(s) -
Abe Hiroshi,
Seo Hyojung,
Lee Daeyeol
Publication year - 2011
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1111/j.1749-6632.2011.06223.x
Subject(s) - prefrontal cortex , neuroscience , computer science , cognitive psychology , psychology , cognitive science , chemistry , cognition
Behavioral changes driven by reinforcement and punishment are referred to as simple or model‐free reinforcement learning. Animals can also change their behaviors by observing events that are neither appetitive nor aversive when these events provide new information about payoffs available from alternative actions. This is an example of model‐based reinforcement learning and can be accomplished by incorporating hypothetical reward signals into the value functions for specific actions. Recent neuroimaging and single‐neuron recording studies showed that the prefrontal cortex and the striatum are involved not only in reinforcement and punishment, but also in model‐based reinforcement learning. We found evidence for both types of learning, and hence hybrid learning, in monkeys during simulated competitive games. In addition, in both the dorsolateral prefrontal cortex and orbitofrontal cortex, individual neurons heterogeneously encoded signals related to actual and hypothetical outcomes from specific actions, suggesting that both areas might contribute to hybrid learning.