2011年2月21日月曜日

Ventral Striatum and Orbitofrontal Cortex Are Both Required for Model-Based, But Not Model-Free, Reinforcement Learning

The Journal of Neuroscience, February 16, 2011, 31(7):2700-2705
Michael A. McDannald, Federica Lucantonio, Kathryn A. Burke, Yael Niv, and Geoffrey Schoenbaum

ラットの損傷研究。Unblockingを用いて「vStriatumはモデル・ベースド/モデル・フリー強化学習両方に、OFC は前者のみに効いている」。エレガントだけど、model-based RLの理解が深まったとは感じられなかった…

In many cases, learning is thought to be driven by differences between the value of rewards we expect and rewards we actually receive. Yet learning can also occur when the identity of the reward we receive is not as expected, even if its value remains unchanged. Learning from changes in reward identity implies access to an internal model of the environment, from which information about the identity of the expected reward can be derived. As a result, such learning is not easily accounted for by model-free reinforcement learning theories such as temporal difference reinforcement learning (TDRL), which predicate learning on changes in reward value, but not identity. Here, we used unblocking procedures to assess learning driven by value- versus identity-based prediction errors. Rats were trained to associate distinct visual cues with different food quantities and identities. These cues were subsequently presented in compound with novel auditory cues and the reward quantity or identity was selectively changed. Unblocking was assessed by presenting the auditory cues alone in a probe test. Consistent with neural implementations of TDRL models, we found that the ventral striatum was necessary for learning in response to changes in reward value. However, this area, along with orbitofrontal cortex, was also required for learning driven by changes in reward identity. This observation requires that existing models of TDRL in the ventral striatum be modified to include information about the specific features of expected outcomes derived from model-based representations, and that the role of orbitofrontal cortex in these models be clearly delineated.

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