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.

项目概要我们周围的世界有一个统计结构，我们可以用它来改善我们的选择。通过识别关键特征（例如变化率）来调整结构，对于适应和优化我们的然而，学习这些特征需要积累多个证据。时间尺度：考虑当前决策的明确证据的短时间尺度，以及考虑当前决策的长时间尺度支持大脑中潜在的环境特征推断，跨时间尺度的证据积累。因此，灵活决策所必需的应该利用诸如以下区域的情境记忆：该提案旨在识别人类的认知策略和神经机制。用于跨时间尺度积累证据以进行适应性决策。利用计算模型来开发和验证人类行为和人类的方法电生理实验，我将 1）识别人类用来支持的各种决策策略多时间尺度推理，2) 模拟人类认知策略的合理神经机制，以及 3) 定义 HC 在实施多时间尺度推理方面的作用符合 BRAIN 计划的使命：将行为和功能以及优先研究领域 5（理论和数据分析工具）和 6（人类与我优秀的导师团队，他们结合了理论和实验方面的专业知识。工作，这笔赠款的指导阶段将为我提供 1）静态和静态方面的额外研究技能推理模型和神经回路建模以及 2) 通过个性化指导实现职业发展，科罗拉多大学博尔德分校提供了理想的写作和科学传播培训环境。为了这项工作，应用数学、心理学和研究所之间的大量资源此外，随着许多在线课程和研讨会的可用性，资源宾夕法尼亚大学和休斯顿大学的联合导师大学机构也可访问。独立阶段研究将把这种额外的培训与我以前在人类方面的经验结合起来电生理学和信号处理研究 HC 在灵活决策中的作用，分析人类癫痫患者执行多时间尺度决策任务时的神经记录由我在犹他大学的合作者撰写，我的长期目标是建立自己的实验室，应用理论、人类行为和人类神经电生理学的多模式方法来识别与灵活决策相关的认知和神经策略以及病理学的影响这些过程受到干扰。