The computational and neural mechanisms linking decision-making and memory in humans

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PROJECT SUMMARY How does decision-making influence memory to leave a long-lasting impact on human behavior? Answering this question is critical to understanding how impaired decision making might lead to maladaptive memory outcomes, such as rumination on negative events, susceptibility to false memories, or memory loss. In recent years, the reinforcement learning (RL) framework has been particularly fruitful for describing impaired decision-making in psychiatric disorders as well as identifying computational mechanisms linking decision-making and memory. The overarching aim of this project is to identify the neurocomputational mechanisms that explain how the learning processes driving decision-making also influence subsequent memory. To do so, the K99 phase of the proposed study consists of a computational approach to identify how model-free reinforcement learning signals influence both hippocampal and non-hippocampal recognition memory performance in humans (Aim 1), and a neurobiolog- ical approach to identify the electrophysiological mechanisms underlying these RL-memory interactions (Aim 2); the R00 phase of the study will deploy these approaches to study the contributions of model-based reinforcement learning to these distinct memory processes (Aim 3). Specifically, Aim 1 will test how model-free RL signals such as prediction errors might interact with the perceptual features of a stimulus to enhance both immediate (non- hippocampal) memory and delayed (hippocampal) memory in healthy volunteers. Aim 2 will leverage intracranial recording obtained from humans with epilepsy monitoring electrodes to test how neural activity in the frontal cor- tex, hippocampus, and non-hippocampal medial temporal regions (such as parahippocampal gyrus) subserve the influence of model-free RL processes on memory performance. Upon completion of Aims 1-2 (K99), the candidate—a neuroscientist with a background in the neurobiology of human memory—will obtain new training in computational modeling of reinforcement learning and decision-making and have a unique, multi-disciplinary skillset to apply to Aim 3 (R00), to study how model-based RL processes influence mnemonic behavior and neural activity. The candidate’s mentors are uniquely suited to provide the training for these Aims, given their expertise in bridging computational modeling (Dr. Xiaosi Gu) and human neurophysiology (Dr. Ignacio Saez) to understand human decision-making. These skills will also facilitate the candidate’s transition into an independent researcher with the long-term goal of performing integrative behavioral, computational, and biological studies of how these human decision-making and memory processes go awry in psychiatric disorders.