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Abstract

Instrumental learning and decision-making rely on two parallel systems: a goal-directed and a habitual system. In the past decade, several paradigms have been developed to study these systems in animals and humans by means of e.g. overtraining, devaluation procedures and sequential decision-making. These different paradigms are thought to measure the same constructs, but cross-validation has rarely been investigated. In this study we compared two widely used paradigms that assess aspects of goal-directed and habitual behavior. We correlated parameters from a two-step sequential decision-making task that assesses model-based and model-free learning with a slips-of-action paradigm that assesses the ability to suppress cue-triggered, learnt responses when the outcome has been devalued and is therefore no longer desirable. Model-based control during the two-step task showed a very moderately positive correlation with goal-directed devaluation sensitivity, whereas model-free control did not. Interestingly, parameter estimates of model-based and goal-directed behavior in the two tasks were positively correlated with higher-order cognitive measures (e.g. visual short-term memory). These cognitive measures seemed to (at least partly) mediate the association between model-based control during sequential decision-making and goal-directed behavior after instructed devaluation. This study provides moderate support for a common framework to describe the propensity towards goal-directed behavior as measured with two frequently used tasks. However, we have to caution that the amount of shared variance between the goal-directed and model-based system in both tasks was rather low, suggesting that each task does also pick up distinct aspects of goal-directed behavior. Further investigation of the commonalities and differences between the model-free and habit systems as measured with these, and other, tasks is needed. Also, a follow-up cross-validation on the neural systems driving these constructs across different paradigms would promote the definition and operationalization of measures of instrumental learning and decision-making in humans.