Neural and behavioral signatures of policy compression in cognitive control

Abstract

Making context-dependent decisions incurs cognitive costs. Cognitive control studies have investigated the nature of such costs from both computational and neural perspectives. In this paper, we offer an information-theoretic account of the costs associated with context-dependent decisions. According to this account, the brains limited capacity to store context-dependent policies necessitates "compression" of policies into internal representations with an upper bound on code length, quantified by an information-theoretic measure (policy complexity). These representations are decoded into actions by sequentially inspecting each bit, such that longer codes take more time to decode. When a response deadline is imposed, the account predicts that policy complexity should increase with the deadline. Higher policy complexity is associated with several behavioral signatures: (i) higher accuracy; (ii) lower variability; and (iii) lower perseveration. Analyzing data from a rule-based action selection task, we found evidence supporting all of these predictions. We further hypothesized that complex policies require higher neural dimensionality (which constrains the code space). Consistent with this hypothesis, we found that policy complexity correlates with a measure of neural dimensionality in a rule-based decision task. This finding brings us a step closer to understanding the neural implementation of policy compression and its implications for cognitive control.