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Cognitive and Neural Strategies for Latent Feature Inference

潜在特征推理的认知和神经策略

基本信息

批准号：
10662877
负责人：
Tahra Eissa
金额：
$ 13.04万
依托单位：
UNIVERSITY OF COLORADO
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10662877
关键词：
Accounting Area BRAIN initiative Behavior Behavioral Brain Brain region Cognition Cognitive Cognitive Science Colorado Communication Computer Models Data Data Analyses Decision Making Diagnostic Electrophysiology (science)Elements Environment Environmental Wind Epilepsy Functional disorder Future Goals Grant Hippocampus Human Individual Institution Learning Link Mathematics Measures Memory Mentors Mentorship Microelectrodes Mission Modeling Monitor Neurons Neurosciences Output Pathologic Patients Pattern Pennsylvania Persons Phase Play Population Process Psychology Recurrence Research Research Personnel Research Priority Resources Role Seasons Source Stream Structure Testing Training Universities Update Utah Work Writing career development cohort crowdsourcing data modeling experience experimental study flexibility improved individual variation information processing insight interdisciplinary approach multimodality nervous system disorder neural neural circuit neural model neuromechanism professional atmosphere programs rate of change response signal processing skills theories tool

项目摘要

PROJECT SUMMARY The world around us has a statistical structure that we can use to improve our choices. Learning the underlying structure by identifying key features, such as the rate of change, is useful for adapting and optimizing our decision-making strategies. However, learning these features requires accumulating evidence across multiple timescales: a short timescale that considers explicit evidence for the current decision, and a long timescale that supports latent environmental feature inference. In the brain, evidence accumulation across timescales necessary for flexible decision-making should therefore engage contextual memory in regions such as hippocampus (HC). This proposal aims to identify cognitive strategies and neural mechanisms humans use to accumulate evidence across timescales for adaptive decision-making. Using an interdisciplinary approach that utilizes computational modeling to develop and validate human behavioral and human electrophysiological experiments, I will 1) identify the variety of decision strategies humans use to support multi-timescale inference, 2) model plausible neural mechanisms of human cognitive strategies, and 3) define HC’s role in implementing multi-timescale inference. This work is in line with the BRAIN initiative’s mission to link behavior and function and priority research areas 5 (Theory and Data Analysis tools) and 6 (Human Neuroscience). With my outstanding mentor team, who have combined expertise in theory and experimental work, the mentored phase of this grant will provide me with 1) additional research skills in both static inference models and neural-circuit modeling and 2) career development through personalized mentorship, writing, and scientific communication training. The University of Colorado Boulder offers an ideal environment for this work, with numerous resources between the departments of Applied Math, Psychology and Institute for Cognitive Science. Additionally, with the availability of many programs and seminars online, resources at co-mentor institutions University of Pennsylvania and University of Houston are also accessible. The independent phase research will combine this additional training with my previous experience in human electrophysiology and signal processing to study the role of HC in flexible decision-making, analyzing human neural recordings from epilepsy patients while they perform a multi-timescale decision-making task recorded by my collaborators at University of Utah. My long term goals are to launch my own lab that applies a multimodal approach of theory, human behavior, and human neural electrophysiology to identifying the cognitive and neural strategies associated with flexible decision-making and the impacts that pathological disruptions have on these processes.

项目总结