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
基本信息
- 批准号:10663601
- 负责人:
- 金额:$ 23.4万
- 依托单位:
- 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:2023-04-15 至 2025-03-31
- 项目状态:未结题
- 来源:
- 关键词:AccelerationAffectAgeArousalAttentionAttention deficit hyperactivity disorderBehaviorBiologicalBrainCognitionCognitiveCognitive deficitsComplexComputer ModelsDataData CollectionDecision MakingDiagnosticDimensionsDiseaseDisinhibitionEmotionalEtiologyHumanImpairmentIndividualIndividual DifferencesInformal Social ControlInterventionLinkMathematicsMeasurementMeasuresMemory impairmentMental Health ServicesMental disordersMethodsModelingNeurobiologyNeurosciencesNeurosciences ResearchParticipantPerformancePersonsPopulations at RiskProcessProperdinPropertyPsychometricsPsychopathologyResearchResearch Domain CriteriaRiskRisk FactorsRoleSchizophreniaShort-Term MemorySleepSleep disturbancesStressStructureSymptomsTestingVariantWorkclinical phenotypecognitive abilitycognitive controlcognitive performancecognitive systemcognitive taskcomputer frameworkcontextual factorsdesigndiagnostic criteriadisease classificationexperimental studyimprovedindividual variationinsightnegative affectneurobehavioralneurobiological mechanismneurophysiologyprogramspsychologicresponsesleep regulationsmartphone applicationstressorsubstance usetheoriestooltrait
项目摘要
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
与自我调节相关的认知结构,包括认知控制、注意力和工作记忆,有
研究领域标准(RDoC)倡议的一个突出重点,以表征个人的维度
传递精神障碍风险的变异。然而,这些构造中的许多都受到其模糊的限制
定义,与神经生物学的模棱两可的联系,以及这种结构的假定测量具有
较弱的心理测量学特性,包括信度差和因素结构不连贯。更进一步,始终如一
研究发现,患有多种精神障碍的人倾向于表现出跨度跨度的非特异性认知缺陷
这一系列的概念表明,与精神病理学相关的认知偏差可能更好-
用高阶因子而不是离散函数来解释的。我们建议评估是否有效率
证据积累(EEA)--一种认知结构,已经在计算中得到了很好的表征
建模和神经生理学研究,但尚未与RDoC集成-可以克服许多
这些限制是作为RDoC矩阵中的高阶系数运行的。EEA是一种核心机制
证据积累模型(EAMS)--解释认知的主要数学框架
表现--在心理和神经生理学方面都有精确的计算定义
分析水平,清楚的生物学合理性,以及强大的心理测量学特性。先前的工作已经确定
EEA是一个可靠的因素,可以解释各种不同类型的
认知任务--从简单的决定到复杂的认知控制和工作记忆范例--而且是
患有与自我调节困难有关的多种障碍。我们假设EEA代表一个更高的阶数
在RDoC矩阵中占认知领域差异的很大比例的因素
而且,疲软的EEA可能会损害决策,从而传递多种精神病态的风险
跨环境。EEA尚未与RDoC集成,尽管特征(受试者之间)在
EEA与精神病理学有关,即EEA在现实世界中状态(在受试者内)变化的相关性
上下文未知。我们建议评估EEA在RDoC中作为候选高阶因子的作用
框架,并为将EEA作为桥梁进行计算严谨研究的更大计划奠定了基础
通过完成以下目标,在神经生物学机制和现实世界行为之间建立联系:1)定义
RDoC矩阵中特质EEA作为高阶认知域的结构和边界
开发和试验用于状态EEA及其与真实世界波动关系的日常评估工具
在语境因素和行为上。这个项目有可能以一种更好的方式来完善RDoC
代表心理病理学的认知风险因素(即一般任务和跨诊断关联
认知和精神病理学之间的关系),并使其能够利用已建立的
提高RDoC结构和措施的精确度和生物学可信度的计算模型。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Alexander Weigard其他文献
Alexander Weigard的其他文献
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{{ truncateString('Alexander Weigard', 18)}}的其他基金
Leveraging computational models of neurocognition to improve predictions about individual youths' risk for substance use disorders
利用神经认知的计算模型来改进对青少年个体物质使用障碍风险的预测
- 批准号:
10213907 - 财政年份:2021
- 资助金额:
$ 23.4万 - 项目类别:
Leveraging computational models of neurocognition to improve predictions about individual youths' risk for substance use disorders
利用神经认知的计算模型来改进对青少年个体物质使用障碍风险的预测
- 批准号:
10382322 - 财政年份:2021
- 资助金额:
$ 23.4万 - 项目类别:
Leveraging computational models of neurocognition to improve predictions about individual youths' risk for substance use disorders
利用神经认知的计算模型来改进对青少年个体物质使用障碍风险的预测
- 批准号:
10609805 - 财政年份:2021
- 资助金额:
$ 23.4万 - 项目类别:
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