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Poster C164 in Poster Session C - Friday, August 9, 2024, 11:15 am – 1:15 pm, Johnson Ice Rink
Learning and use of reward-related representations across cortex and time
Ai Phuong Tong1 (), Vishnu Sreekumar2, Kareem Zaghloul2, Mark Woolrich1; 1University of Oxford, 2National Institutes of Health
Reward-related representations are found distributed throughout many human subcortical and neocortical regions that support different neural processes. These representations get used at different points in time for related tasks. However, the way these representations get re-used and strengthen over time is not well understood. To investigate this, we recorded from the temporal lobe and prefrontal cortex with intracranial electrocorticography (ECoG) while human subjects learnt two-choice decisions between two scenes. Subjects were able to straightforwardly re-use knowledge only when reward contingencies stayed the same between the two scenes. Using a Bayesian learner, we inferred reward expectations from choice behavior, and then measured representations of reward expectation in ECoG data. Reward expectations were uniquely represented in distributed regions across human cortex. The representations of reward expectation in the medial temporal lobe and orbitofrontal cortex were re-used between the two scenes, only when subjects could straightforwardly transfer knowledge between the two scenes. Finally, in a separate region, the anterior temporal lobe, the strength of reward representations as measured by similarity between scenes, increased as learning increased. Our findings suggest that patterns of activity representing reward information are integrated into multiple brain regions, get re-used in similar situations, and increase in fidelity with learning.
Keywords: reward expectation reward representation human electrocorticography reversal learning