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Complex cognitive processes require sophisticated local processing but also interactions between distant brain regions. It is therefore critical to be able to study distant interactions between local computations and the neural representations they act on. Here we report two anatomically and computationally distinct learning signals in lateral orbitofrontal cortex (lOFC) and the dopaminergic ventral midbrain (VM) that predict trial-by-trial changes to a basic internal model in hippocampus. To measure local computations during learning and their interaction with neural representations, we coupled computational fMRI with trial-by-trial fMRI suppression. We find that suppression in a medial temporal lobe network changes trial-by-trial in proportion to stimulus-outcome associations. During interleaved choice trials, we identify learning signals that relate to outcome type in lOFC and to reward value in VM. These intervening choice feedback signals predicted the subsequent change to hippocampal suppression, suggesting a convergence of signals that update the flexible representation of stimulus-outcome associations.

Original publication

DOI

10.1016/j.neuron.2016.02.014

Type

Journal article

Journal

Neuron

Publication Date

16/03/2016

Volume

89

Pages

1343 - 1354

Keywords

Adult, Brain Mapping, Choice Behavior, Computer Simulation, Feedback, Female, Functional Laterality, Healthy Volunteers, Hippocampus, Humans, Learning, Linear Models, Male, Mesencephalon, Models, Biological, Neural Pathways, Oxygen, Photic Stimulation, Predictive Value of Tests, Prefrontal Cortex, Young Adult