Learning when effort matters: Neural dynamics underlying updating and adaptation to changes in performance efficacy

Abstract

AbstractTo determine how much cognitive control to invest in a task, people need to consider whether exerting control matters for obtaining rewards. In particular, they need to account for the efficacy of their performance – the degree to which rewards are determined by their performance or by independent factors. Yet it remains unclear how people learn about their performance efficacy in an environment. Here we combined computational modeling with measures of task performance and EEG, to provide a mechanistic account of how people (a) learn and dynamically update efficacy expectations in a changing environment, and (b) proactively adjust control allocation based on their current efficacy expectations. Across two studies subjects performed an incentivized cognitive control task while their performance efficacy (the likelihood that rewards are performance-contingent or random) varied over time. We show that people learn about efficacy through a neural mechanism similar to the one used to learn from rewards, and that they use this information to adjust how much control they allocate. Using a computational model we show that these control adjustments reflect changes in information processing, rather than the speed-accuracy tradeoff. These findings demonstrate the neurocomputational mechanism through which people learn how worthwhile their cognitive control is.