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.

项目概要/摘要 与自我调节相关的认知结构，包括认知控制、注意力和工作记忆， 研究领域标准 (RDoC) 计划的一个突出重点是描述个人的维度 然而，这些概念中的许多都因其模糊性而受到限制。 定义、与神经生物学的模糊联系以及此类结构的推定测量的证据 心理测量特性较弱，包括可靠性差和因素结构不连贯。 研究发现，患有多种精神疾病的人往往会表现出非特异性认知缺陷 这一系列的构造表明，与精神病理学相关的认知失常可能更好—— 我们建议用高阶因子解释而不是用离散函数解释。 证据积累（EEA）——一种在计算领域得到充分表征的认知结构 建模和神经生理学研究，但尚未与 RDoC 集成——可以克服许多问题 这些限制通过作为 RDoC 矩阵中的高阶因素来实现，是 EEA 的核心机制。 证据积累模型（EAM）——解释认知的主要数学框架 性能——在心理和神经生理学方面都有精确的计算定义 先前的工作已经确立了分析水平、清晰的生物学合理性和强大的心理测量特性。 EEA 是一个可靠的因素，可以解释各种绩效的个体差异 认知任务——从简单的决策到复杂的认知控制和工作记忆范例—— 我们认为 EEA 代表了一种更高阶的疾病。 占 RDoC 矩阵中认知领域差异很大比例的因素 疲弱的欧洲经济区可能会损害决策，从而带来多种精神病理学的风险 EEA 尚未与 RDoC 整合，尽管特征（受试者之间）存在差异。 EEA 与精神病理学相关，即现实世界中 EEA 状态（受试者内）变化的相关性 我们建议评估 EEA 作为 RDoC 候选高阶因素的作用。 框架，并为以 EEA 为桥梁的更大规模计算严格研究计划奠定基础 通过完成以下目标来区分神经生物学机制和现实世界行为：1）定义 作为 RDoC 矩阵中高阶认知域的特征 EEA 的结构和边界，2) 开发和试点工具，用于每日评估国家欧洲经济区及其与现实世界波动的关系 该项目有可能以更好的方式改进 RDoC。 代表精神病理学的认知危险因素（即一般任务和跨诊断关联 认知和精神病理学结构之间）并使其能够利用成熟的关键优势 计算模型，以提高 RDoC 构建和测量的精度和生物学合理性。