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

Abstract

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: anintensity costthat 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 ofcontrol; and aninteraction costthat 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 toautomaticity. 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.