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Poster C163 in Poster Session C - Friday, August 9, 2024, 11:15 am – 1:15 pm, Johnson Ice Rink
Intracranial recordings reveal neural encoding of attention-modulated reinforcement learning in humans
Christina Maher1,2 (), Salman Qasim1,2, Lizbeth Nuñez Martinez1,4,5,6, Ignacio Saez1,4,5,6, Angela Radulescu1,3,4; 1Icahn School of Medicine at Mount Sinai, 2Friedman Brain Institute, 3Department of Psychiatry, 4Department of Neuroscience, 5Department of Neurosurgery, 6Department of Neurology
Reinforcement learning (RL) is tractable in multidimensional environments when agents maintain efficient state representations, or mental models of relevant information. Attention supports state representations in service of RL by constraining learning to relevant dimensions. However, the physiological processes supporting value updating and attentional control are unknown. To investigate the neural mechanism supporting these processes we relate attention-modulated RL models to neuronal activity recorded directly from the prefrontal cortex of neurosurgical patients playing a multidimensional decision-making task. These models revealed that participants deploy selective attention during RL. Model-estimated expected value of the chosen stimulus correlated with neuronal activity in the orbitofrontal (OFC) and lateral prefrontal cortex (LFPC), though value signals in the LPFC were additionally biased by model-estimated attention. In sum, these results provide mechanistic insight into the neuronal implementation of the computations involved in attention-modulated RL.
Keywords: reinforcement learning attention intracranial electrophysiology human prefrontal cortex