Bayesian Prediction and Evaluation in the Anterior Cingulate Cortex
Jaime S. Ide, Pradeep Shenoy, Angela J. Yu, and Chiang-shan R. Li
The Journal of Neuroscience, 30 January 2013, 33(5): 2039-2047; doi: 10.1523/JNEUROSCI.2201-12.2013
Stop-signal課題における被験者の行動（正解／不正解、反応時間）はベイズ予測でモデル化できる。また、dACCは「報酬予測誤差」と「報酬予測誤差の絶対値（unsigned prediction error）」の両方にモジュレートされる。
The dorsal anterior cingulate cortex (dACC) has been implicated in a variety of cognitive control functions, among them the monitoring of conflict, error, and volatility, error anticipation, reward learning, and reward prediction errors. In this work, we used a Bayesian ideal observer model, which predicts trial-by-trial probabilistic expectation of stop trials and response errors in the stop-signal task, to differentiate these proposed functions quantitatively. We found that dACC hemodynamic response, as measured by functional magnetic resonance imaging, encodes both the absolute prediction error between stimulus expectation and outcome, and the signed prediction error related to response outcome. After accounting for these factors, dACC has no residual correlation with conflict or error likelihood in the stop-signal task. Consistent with recent monkey neural recording studies, and in contrast with other neuroimaging studies, our work demonstrates that dACC reports at least two different types of prediction errors, and beyond contexts that are limited to reward processing.