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.

项目总结 我们周围的世界有一个统计结构，我们可以用它来改进我们的选择。学习基础知识 通过识别关键功能(如更改率)来构建 决策策略。然而，学习这些功能需要在多个方面积累证据 TimeScale：考虑当前决策的明确证据的短时间尺度和长时间尺度 支持潜在环境特征推理。在大脑中，证据在时间尺度上积累 因此，灵活决策所必需的应该在诸如以下区域中使用上下文记忆 海马区(HC)。这项建议旨在确定人类的认知策略和神经机制。 用于在不同时间尺度上积累证据，以进行适应性决策。使用跨学科的 一种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可能的神经机制，3)定义 HC在实现多时间尺度推理中的作用。这项工作符合大脑计划的使命，即 链接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 神经科学)。与我杰出的导师团队一起，他们将理论和实验的专业知识结合在一起 工作，这项资助的指导阶段将为我提供1)在静态和静态两方面的额外研究技能 推理模型和神经电路建模以及2)通过个性化指导实现职业发展， 写作和科学交流训练。科罗拉多大学博尔德分校提供了理想的环境 对于这项工作，在应用数学、心理学和研究所之间有大量的资源 认知科学。此外，随着许多计划和研讨会的在线提供，资源位于 宾夕法尼亚大学和休斯顿大学也是联合导师机构。这个 独立阶段的研究将把这次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 癫痫患者在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写的。我的长期目标是推出自己的实验室，将 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及其病理性影响 中断对这些过程造成了影响。