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 the mPFC, including human ERP, fMRI, and macaque single-unit data. The results suggest that the mPFC 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.
Keywords: anterior cingulate; attention; cognitive control; medial prefrontal cortex; reinforcement learning.