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

Rutgers Biomedical And Health Sciences, Newark NJ

Investigators

Abstract

PROJECT DESCRIPTION The PI for this R00 proposal, Dr. Salman Qasim, is transitioning to a tenure-track faculty position in the Department of Neurosurgery at Rutgers Robert Wood Johnson Medical School. The original research proposed remains unaltered. Briefly, this project seeks to answer decision-making influences 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 R00 phase of the study will deploy computational and neurobiological approaches to study the contributions of model-based reinforcement learning to these distinct memory processes. Specifically, the results of the K99 project show how model-free RL signals such as prediction errors enhance memory, how this influence is weakened in a negative affective state, and how neural activity in the frontal cortex, hippocampus, and nonhippocampal medial temporal regions (such as amygdala) subserve the influence of model-free RL processes on memory performance. The PI obtained new training in computational modeling of reinforcement learning and decision-making that will enable the successful completion of the R00 research project: to study how model-based RL processes influence mnemonic behavior and neural activity.