Cognitive control as a multivariate optimization problem

TL;DR

This paper models cognitive control allocation as a multivariate optimization problem, drawing parallels with motor control inverse problems, to better understand how the brain optimizes control signals amidst complexity.

Contribution

It introduces a normative framework for cognitive control allocation based on inverse problem-solving and optimal control theory, extending motor control principles to cognitive processes.

Findings

Control allocation modeled as an inverse problem.

Effort costs help regularize control choices.

Provides a new perspective on mental effort.

Abstract

Research has characterized the various forms cognitive control can take, including enhancement of goal-relevant information, suppression of goal-irrelevant information, and overall inhibition of potential responses, and has identified computations and neural circuits that underpin this multitude of control types. Studies have also identified a wide range of situations that elicit adjustments in control allocation (e.g., those eliciting signals indicating an error or increased processing conflict), but the rules governing when a given situation will give rise to a given control adjustment remain poorly understood. Significant progress has recently been made on this front by casting the allocation of control as a decision-making problem, and developing unifying and normative models that prescribe when and how a change in incentives and task demands will result in changes in a given form of control. Despite their successes, these models, and the experiments that have been developed to test them, have yet to face their greatest challenge: deciding how to allocate control across the multiplicity of control signals that one could engage at any given time. Here, we will lay out the complexities of the inverse problem inherent to cognitive control allocation, and their close parallels to inverse problems within motor control (e.g., choosing between redundant limb movements). We discuss existing solutions to motor control`s inverse problems drawn from optimal control theory, which have proposed that effort costs act to regularize actions and transform motor planning into a well-posed problem. These same principles may help shed light on how our brains optimize over complex control configuration, while providing a new normative perspective on the origins of mental effort.