Multiple associative structures created by reinforcement and incidental statistical learning mechanisms
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Description: Published in Nature Communications in 2019. Abstract: Learning the structure of the world can be driven by reinforcement but also occurs incidentally through experience. Reinforcement learning theory has provided insight into how prediction errors drive updates in beliefs but less attention has been paid to the knowledge resulting from such learning. Here we contrast associative structures formed through reinforcement and experience of task statistics. BOLD neuroimaging in human volunteers demonstrated rigid representations of rewarded sequences in temporal pole and posterior orbito-frontal cortex, which were constructed backwards from reward. By contrast, medial prefrontal cortex and a hippocampal-amygdala border region carried reward-related knowledge but also flexible statistical knowledge of the currently relevant task model. Intriguingly, ventral striatum encoded prediction error responses but not the full RL- or statistically-derived knowledge. In summary, representations of task knowledge are derived via multiple learning processes operating at different time scales that are associated with partially overlapping and partially specialised anatomical regions.