A general role for medial prefrontal cortex in event prediction

作者：Alexander William H^*; Brown Joshua W

来源：Frontiers in Computational Neuroscience, 2014, 8: 69.

DOI：10.3389/fncom.2014.00069

• medial prefrontal cortex
• anterior cingulate
• cognitive control
• attention
• reinforcement learning

A recent computational neural model of medial prefrontal cortex (mPFC), namely the predicted response-outcome (PRO) model (Alexander and Brown, 2011), suggests that mPFC learns to predict the outcomes of actions. The model accounted for a wide range of data on the mPFC. Nevertheless, numerous recent findings suggest that mPFC may signal predictions and prediction errors even when the predicted outcomes are not contingent on prior actions. Here we show that the existing PRO model can learn to predict outcomes in a general sense, and not only when the outcomes are contingent on actions. A series of simulations show how this generalized PRO model can account for an even broader range of findings in them PFC, including human ERP, fMRI, and macaque single-unit data. The results suggest that them PFC learns to predict salient events in general and provides a theoretical framework that links mPFC function to model-based reinforcement learning, Bayesian learning, and theories of cognitive control.

• 出版日期2014-7-11