Efficiency of evidence accumulation (EEA) as a higher-order, computationally defined RDoc construct

证据积累效率 (EEA) 作为高阶、计算定义的 RDoc 构造

• 批准号: 10663601
• 负责人: Alexander Weigard
• 金额: $ 23.4万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10663601
• 关键词: Acceleration; Affect; Age; Arousal; Attention; Attention deficit hyperactivity disorder; Behavior; Biological; Brain; Cognition; Cognitive; Cognitive deficits; Complex; Computer Models; Data; Data Collection; Decision Making; Diagnostic; Dimensions; Disease; Disinhibition; Emotional; Etiology; Human; Impairment; Individual; Individual Differences; Informal Social Control; Intervention; Link; Mathematics; Measurement; Measures; Memory impairment; Mental Health Services; Mental disorders; Methods; Modeling; Neurobiology; Neurosciences; Neurosciences Research; Participant; Performance; Persons; Populations at Risk; Process; Properdin; Property; Psychometrics; Psychopathology; Research; Research Domain Criteria; Risk; Risk Factors; Role; Schizophrenia; Short-Term Memory; Sleep; Sleep disturbances; Stress; Structure; Symptoms; Testing; Variant; Work; clinical phenotype; cognitive ability; cognitive control; cognitive performance; cognitive system; cognitive task; computer framework; contextual factors; design; diagnostic criteria; disease classification; experimental study; improved; individual variation; insight; negative affect; neurobehavioral; neurobiological mechanism; neurophysiology; programs; psychologic; response; sleep regulation; smartphone application; stressor; substance use; theories; tool; trait

PROJECT SUMMARY/ABSTRACT Cognitive constructs relevant to self-regulation, including cognitive control, attention, and working memory, are a prominent focus of the Research Domain Criteria (RDoC) initiative to characterize dimensions of individual variation that convey risk for mental disorders. However, many of these constructs are limited by their vague definitions, ambiguous links to neurobiology, and evidence that putative measures of such constructs have weak psychometric properties, including poor reliability and an incoherent factor structure. Further, consistent findings that people with multiple psychiatric disorders tend to display non-specific cognitive deficits that span this array of constructs suggest that cognitive aberrations associated with psychopathology may be better- explained by a higher-order factor than by discrete functions. We propose to evaluate whether efficiency of evidence accumulation (EEA)—a cognitive construct that has been well-characterized in computational modeling and neurophysiological research but has yet to be integrated with RDoC—can overcome many of these limitations by operating as a higher-order factor within the RDoC matrix. EEA is a core mechanism of evidence accumulation models (EAMs)—a predominant mathematical framework for explaining cognitive performance— that has a precise computational definition across both psychological and neurophysiological levels of analysis, clear biological plausibility, and strong psychometric properties. Prior work has established EEA as a reliable factor that accounts for individual differences in performance across a wide variety of cognitive tasks—from simple decisions to complex cognitive control and working memory paradigms—and is impaired in multiple disorders linked to self-regulatory difficulties. We posit that EEA represents a higher-order factor that accounts for a substantial proportion of the variation across cognitive domains in the RDoC matrix and that weak EEA conveys risk for multiple psychopathologies, potentially by impairing decision making across contexts. EEA has yet to be integrated with RDoC and, although trait (between-subjects) variation in EEA is linked to psychopathology, the correlates of state (within-subjects) variation in EEA across real-world contexts are unknown. We propose to evaluate EEA’s role as a candidate higher-order factor in the RDoC framework and set the stage for a larger program of computationally rigorous research on EEA as a bridge between neurobiological mechanisms and real-world behavior by completing the following aims: 1) define the structure and boundaries of trait EEA as a higher-order cognitive domain in the RDoC matrix, 2) develop and pilot tools for daily assessment of state EEA and its relations with real-world fluctuations in contextual factors and behavior. This project has the potential to refine RDoC in a way that that better represents cognitive risk factors for psychopathology (i.e., task-general and transdiagnostic associations between cognition and psychopathology constructs) and allows it to leverage key benefits of well-established computational models to increase the precision and biological plausibility of RDoC constructs and measures.

项目摘要/摘要 与自我调节有关的认知结构，包括认知控制，注意力和工作记忆，是 研究领域标准（RDOC）倡议的突出重点是表征个体的维度 传达精神障碍风险的变化。但是，其中许多结构受到投票的限制 定义，与神经生物学的模棱两可的联系以及这种构造的假定措施的证据 心理测量特性弱，包括差的可靠性和不一致的因素结构。此外，一致 患有多种精神疾病的人倾向于表现出非特异性认知定义的发现 这些结构阵列表明，与精神病理学相关的认知畸变可能更好 - 用高阶因子解释，而不是离散功能。我们建议评估效率是否 证据积累（EEA） - 一种在计算中已经充分表征的认知结构 建模和神经生理学研究，但尚未与RDOC整合 - 可以克服许多 这些局限性通过作为RDOC矩阵内的高阶因子运行而限制。 EEA是 证据积累模型（EAMS） - 用于解释认知的主要数学框架 性能 - 在心理学和神经生理学中都具有精确的计算定义 分析水平，清晰的生物合理性和强大的心理测量特性。先前的工作已经建立 EEA是一个可靠的因素，说明了各种各样的表现的个体差异 认知任务（从简单的决定到复杂的认知控制和工作记忆范式）是 与自我调节难度有关的多种疾病受损。我们肯定EEA代表一个高阶 占RDOC矩阵中认知域变异的很大一部分的因素 EEA薄弱的EEA传达了多种心理病理学的风险，可能会损害决策 跨环境。 EEA尚未与RDOC集成，尽管性状（受试者间）变化 EEA与心理病理学有关，在现实世界中，EEA的状态（受试者内）变化 上下文未知。我们建议评估EEA作为RDOC中候选高阶因素的作用 框架并为对EEA作为桥梁的更大计算严格研究计划奠定了基础 通过完成以下目的，在神经生物学机制和现实世界行为之间：1）定义 特质EEA作为RDOC矩阵中高阶认知领域的结构和边界，2） 用于日常评估国家EEA及其与现实世界波动的关系的开发和试点工具 在上下文因素和行为中。该项目有可能以更好的方式来完善RDOC 代表心理病理学的认知危险因素（即任务将军和转诊协会 在认知和心理病理学结构之间），并允许其利用良好的关键好处 计算模型，以提高RDOC构建体和措施的精度和生物学合理性。