Cognitive and Neural Strategies for Latent Feature Inference

潜在特征推理的认知和神经策略

• 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
• 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

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.

项目摘要 我们周围的世界具有统计结构，我们可以用来改善我们的选择。学习基础 通过识别关键特征（例如变化速率）来适应和优化我们的结构 决策策略。但是，学习这些功能需要在多个 时间表：一个短时间，认为当前决定的明确证据，以及一个长时间的时间表 支持潜在的环境特征推理。在大脑中，证据跨时标积累 因此，灵活决策必要的必要 海马（HC）。该建议旨在确定人类的认知策略和神经机制 使用跨学科 利用计算建模来开发和验证人类行为和人类的方法 电生理实验，我将1）确定人类用于支持的各种决策策略 多时间尺度的推断，2）模型人类认知策略的合理神经机制，3）定义 HC在实施多时间计算中的作用。这项工作符合《大脑计划》的使命 链接行为，功能以及优先研究领域5（理论和数据分析工具）和6（人） 神经科学）。与我杰出的心理团队一起，他们在理论和实验方面结合了专家 工作，这笔赠款的修订阶段将为我提供1）两种静态的其他研究技能 推论模型和神经电路建模以及2）通过个性化的概念化事业发展， 写作和科学沟通培训。科罗拉多大学博尔德大学提供了理想的环境 对于这项工作，这项工作的应用数学，心理学和研究所之间拥有许多资源，并拥有许多资源。 认知科学。此外，借助许多程序和半货人在线，资源在 宾夕法尼亚大学和休斯顿大学的联合委员机构也可以访问。这 独立阶段研究将把这种额外的培训与我以前在人类的经验相结合 电生理学和信号处理，以研究HC在柔性决策中的作用，分析人类 癫痫患者的神经记录在执行多次刻度决策任务时 由我在犹他大学的合作者。我的长期目标是启动自己适用的实验室 理论，人类行为和人类神经生理学的多模式方法以识别 与灵活决策和病理学的影响相关的认知和神经策略 这些过程中有干扰。