2015年3月12日木曜日

Learning To Minimize Efforts versus Maximizing Rewards: Computational Principles and Neural Correlates

Vasilisa Skvortsova, Stefano Palminteri, and Mathias Pessiglione
The Journal of Neuroscience, 19 November 2014, 34(47): 15621-15630

「報酬を最大化するための学習」と「手間を最大化する学習」の神経基盤。
前者には前頭前野腹内側部(vmPFC)が関与、後者には前島皮質(anterior insula)、背側前帯状皮質(dorsal ACC)といった部位が関与。

The mechanisms of reward maximization have been extensively studied at both the computational and neural levels. By contrast, little is known about how the brain learns to choose the options that minimize action cost. In principle, the brain could have evolved a general mechanism that applies the same learning rule to the different dimensions of choice options. To test this hypothesis, we scanned healthy human volunteers while they performed a probabilistic instrumental learning task that varied in both the physical effort and the monetary outcome associated with choice options. Behavioral data showed that the same computational rule, using prediction errors to update expectations, could account for both reward maximization and effort minimization. However, these learning-related variables were encoded in partially dissociable brain areas. In line with previous findings, the ventromedial prefrontal cortex was found to positively represent expected and actual rewards, regardless of effort. A separate network, encompassing the anterior insula, the dorsal anterior cingulate, and the posterior parietal cortex, correlated positively with expected and actual efforts. These findings suggest that the same computational rule is applied by distinct brain systems, depending on the choice dimension—cost or benefit—that has to be learned.

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