Search

Considerable behavioral data indicate that operant actions can become habitual, as demonstrated by insensitivity to changes in the action-outcome contingency and in subjective outcome values. Notably, although several studies have investigated the neural substrates of habits, none has clearly differentiated the areas of the human brain that support habit formation from those that implement habitual control. We scanned participants with functional magnetic resonance imaging as they learned and performed an operant task in which the conditional structure of the environment encouraged either goal-directed encoding of the consequences of actions, or a habit-like mapping of actions to antecedent cues. Participants were also scanned during a subsequent assessment of insensitivity to outcome devaluation. We identified dissociable roles of the cerebellum and ventral striatum, across learning and test performance, in behavioral insensitivity to outcome devaluation. We also showed that the inferior parietal lobule (an area previously implicated in several aspects of goal-directed action selection, including the attribution of intent and awareness of agency) predicted sensitivity to outcome devaluation. Finally, we revealed a potential functional homology between the human subgenual cortex and rodent infralimbic cortex in the implementation of habitual control. In summary, our findings suggested a broad systems division, at the cortical and subcortical levels, between brain areas mediating the encoding and expression of action-outcome and stimulus-response associations.

Neural computational accounts of reward-learning have been dominated by the hypothesis that dopamine neurons behave like a reward-prediction error and thus facilitate reinforcement learning in striatal target neurons. While this framework is consistent with a lot of behavioral and neural evidence, this theory fails to account for a number of behavioral and neurobiological observations. In this special issue of EJN we feature a combination of theoretical and experimental papers highlighting some of the explanatory challenges faced by simple reinforcement-learning models and describing some of the ways in which the framework is being extended in order to address these challenges. [ABSTRACT FROM AUTHOR]