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

To 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 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 (i) learn and update efficacy expectations in a changing environment and (ii) proactively adjust control allocation based on current efficacy expectations. Across 2 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 update their efficacy beliefs based on prediction errors-leveraging similar neural and computational substrates as those that underpin reward learning-and adjust how much control they allocate according to these beliefs. Using computational modeling, 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.

Commentary: Building the developmental foundations of developmental computational psychiatry: reflections on Hauser et al. (2019).

There is a growing interest in applying the conceptual and analytical frameworks of computational psychiatry to developmental populations. This is motivated by appreciation that psychiatric illness needs to be understood from a neurodevelopmental perspective. The target article by Hauser and colleagues highlights progress in applying the computational psychiatry perspectives to identifying the developmental mechanisms of mental illness. We share the enthusiasm and optimism for this venture, while recognizing the substantial theoretical and pragmatic challenges associated with applying computational frameworks to developing populations. In this commentary, we highlight the ways that taking a developmental perspective in this arena stretches beyond merely identifying age differences in a computational parameter of interest. These include the need for experimental and computational frameworks to recognize that developmental changes can be quantitative or qualitative in nature, the need to consider developmental stage beyond age groupings or even numerical age, and the need for large quantities of data to model age-related changes in a reproducible manner. In doing so, we hope to stimulate progress in uncovering the mechanisms of psychiatric illness in a way that is developmentally informed.