An Information-Theoretic Approach to Reward Rate Optimization in the Tradeoff Between Controlled and Automatic Processing in Neural Network Architectures

Giovanni Petri, Sebastian Musslick, Jonathan D. Cohen
February 26, 2024


This article introduces a quantitative approach to modeling the cost of control in a neural network architecture when it is required to execute one or more simultaneous tasks, and its relationship to automaticity. We begin by formalizing two forms of cost associated with a given level of performance: an intensity cost that quantifies how much information must be added to the input to achieve the desired response for a given task, that we treat as the contribution of control ; and an interaction cost that quantifies the degree to which performance is degraded as a result of interference between processes responsible for performing two or more tasks, that we treat as inversely related to automaticity. We develop a formal expression of the relationship between these two costs, and use this to derive the optimal control policy for a desired level of performance. We use that, in turn, to quantify the tradeoff between control and automaticity, and suggest how this can be used as a normative framework for understanding how people adjudicate between the benefits of control and automaticity.

Related publications