XXXX selectively remediates 'model-based' reward learning: a computational approach.

@entity1 @entity4207 @entity935 @entity191
UNASSIGNED: @entity1 with loss of @entity935 due to @entity191 are impaired at learning from reward. However, it remains unknown precisely which aspect of learning is impaired. In particular, learning from reward, or reinforcement learning, can be driven by two distinct computational processes. One involves habitual stamping-in of stimulus-response associations, hypothesized to arise computationally from 'model-free' learning. The other, 'model-based' learning, involves learning a model of the world that is believed to support goal-directed behaviour. Much work has pointed to a role for @entity935 in model-free learning. But recent work suggests model-based learning may also involve @entity935 modulation, raising the possibility that model-based learning may contribute to the @entity4207 in @entity191 . To directly test this, we used a two-step reward-learning task which dissociates model-free versus model-based learning. We evaluated learning in @entity1 with @entity191 tested ON versus OFF their @entity935 replacement medication and in healthy controls. Surprisingly, we found no effect of disease or medication on model-free learning. Instead, we found that @entity1 tested OFF medication showed a marked impairment in model-based learning, and that this impairment was remediated by dopaminergic medication. Moreover, model-based learning was positively correlated with a separate measure of working memory performance, raising the possibility of common neural substrates. Our results suggest that some @entity4207 in @entity191 may be related to an inability to pursue reward based on complete representations of the environment.
