Computational models of performance monitoring and cognitive control

Abstract

The medial prefrontal cortex (mPFC) has been the subject of intense interest as a locus of cognitive control. Several computational models have been proposed to account for a range of effects, including error detection, conflict monitoring, error likelihood prediction, and numerous other effects observed with single-unit neurophysiology, fMRI, and lesion studies. Here, we review the state of computational models of cognitive control and offer a new theoretical synthesis of the mPFC as signaling response–outcome predictions. This new synthesis has two interacting components. The first component learns to predict the various possible outcomes of a planned action, and the second component detects discrepancies between the actual and intended responses; the detected discrepancies in turn update the outcome predictions. This single construct is consistent with a wide array of performance monitoring effects in mPFC and suggests a unifying account of the cognitive role of medial PFC in performance monitoring.

Figure 1

The Biased Competition model (Miller & Cohen, 2001) as applied to the Stroop task, with a conflict-monitoring loop (Botvinick et al., 2001) driving control.

Figure 2. ACC Response-Outcome model

Planned responses activate learned response-outcome predictions. These predicted outcome signals can feed back to amend or veto a planned action. Once an action is generated, the actual outcome (the movement itself or the feedback from the environment) is compared against the intended outcome, and any discrepancy leads to an update of the learned response-outcome predictions.