Hierarchical Control Over Effortful Behavior by Rodent Medial Frontal Cortex: A Computational Model

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Abstract

The anterior cingulate cortex (ACC) has been the focus of intense research interest in recent years. Although separate theories relate ACC function variously to conflict monitoring, reward processing, action selection, decision making, and more, damage to the ACC mostly spares performance on tasks that exercise these functions, indicating that they are not in fact unique to the ACC. Further, most theories do not address the most salient consequence of ACC damage: impoverished action generation in the presence of normal motor ability. In this study we develop a computational model of the rodent medial prefrontal cortex that accounts for the behavioral sequelae of ACC damage, unifies many of the cognitive functions attributed to it, and provides a solution to an outstanding question in cognitive control research—how the control system determines and motivates what tasks to perform. The theory derives from recent developments in the formal study of hierarchical control and learning that highlight computational efficiencies afforded when collections of actions are represented based on their conjoint goals. According to this position, the ACC utilizes reward information to select tasks that are then accomplished through top-down control over action selection by the striatum. Computational simulations capture animal lesion data that implicate the medial prefrontal cortex in regulating physical and cognitive effort. Overall, this theory provides a unifying theoretical framework for understanding the ACC in terms of the pivotal role it plays in the hierarchical organization of effortful behavior.

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