Elucidating the relationship between decision-making under second-order uncertainty and dimensions of negative affect using computational modeling

使用计算模型阐明二阶不确定性下的决策与负面影响维度之间的关系

• 批准号: 10557177
• 负责人: Sonia Jane Bishop
• 金额: $ 43.72万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-02 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10557177
• 关键词: Address; Anxiety; Anxiety Disorders; Bayesian Modeling; Behavior; Choice Behavior; Cognitive; Computer Models; Decision Making; Depressed mood; Depressive disorder; Development; Dimensions; Factor Analysis; Impairment; Individual; Intervention; Investigation; Learning; Life; Link; Literature; Manic; Measures; Mechanics; Mental Depression; Methodology; Modeling; Occupational; Outcome; Participant; Patient Self-Report; Patients; Performance; Probability; Properdin; Psychopathology; Questionnaires; Reporting; Research; Rewards; Risk; Shock; Social Interaction; Symptoms; Testing; Time; Uncertainty; Update; Volatilization; Work; anxious; comorbidity; diagnostic biomarker; experimental study; hypomania; interest; negative affect; psychoeducation; psychoeducational; psychoeducational intervention; remediation; response; social; success; symptomatology

Project Abstract Computational modeling can help us formalize how choice behaviors can be optimally adapted to different situations and investigate the ways in which individuals deviate from optimal behavior. Both anxious and depressed individuals report difficulties with decision-making; these difficulties have consequences for social interactions and occupational function. Understanding whether anxiety and depression are associated with common or unique deficits in decision-making has been hampered by studies focusing on either anxiety or depression alone and overlooking issues of comorbidity. This is important to address to better identify which aspects of decision-making should be targets for intervention in different patient groups. The separate investigation of anxiety- and depression-related deficits in decision-making has also led to a lack of equivalence of tasks and limited use of both reward-related and aversive outcomes within the same study. In the proposed research, we will conduct bifactor analysis of item-level responses to anxiety and depression questionnaires and use participant scores on the dimensions obtained to interrogate whether deficits in decision- making under second-order uncertainty are common to both anxiety and depression or unique to one or the other. We focus upon second-order uncertainty as this characterizes many of the situations we encounter in every-day life but there has been limited investigation of whether anxiety or depression are linked to deficits in adjusting decision-making to second-order uncertainty. Second-order uncertainty arises both when the probability of our actions resulting in certain outcomes changes across time (volatility) and when information needed to estimate how likely a given action is to lead to a given outcome is not fully available (ambiguity). In the proposed studies, we will use volatility and ambiguity manipulations to examine whether deficits in decision-making under second-order uncertainty are common to both anxiety and depression or unique to one or other and whether such deficits are domain general or domain specific (vary by outcome type: aversive, reward gain or reward loss). On-line studies will be used to conduct replication work and to examine if impaired decision-making under second-order uncertainty is primarily linked to internalizing symptomatology or common to a broader range of psychopathology. These online studies will also enable us to test exploratory hypotheses pertaining to other dimensions of psychopathology. Understanding the extent to which alterations in decision- making under second order uncertainty are unique to anxiety or depression, common to both anxiety and depression (i.e. a transdiagnostic marker of Internalizing psychopathology), or associated with psychopathology more broadly is important to clarify so that we can better tailor cognitive and psycho- educational interventions to different patient groups. It may also help clarify whether existing interventions developed in relation to anxiety (e.g. CBT focusing on ambiguity aversion) might valuably be applied to other forms of psychopathology.

项目摘要 计算建模可以帮助我们形式化地确定选择行为是如何以最佳方式适应不同的 并调查个体偏离最佳行为的方式。既焦虑又焦虑 抑郁的人报告在决策方面有困难；这些困难会对社会产生影响 相互作用和职业功能。了解焦虑和抑郁是否与 决策中常见或独特的缺陷一直受到关注焦虑或焦虑的研究的阻碍 仅仅是抑郁症，而忽略了共病的问题。解决这一问题很重要，以便更好地确定 决策的各个方面应该成为不同患者群体干预的目标。不同的 对决策中与焦虑和抑郁相关的缺陷的调查也导致缺乏 在同一项研究中，任务的等价性以及奖励相关和厌恶结果的有限使用。 在拟议的研究中，我们将对焦虑和抑郁的项目水平反应进行双因素分析 调查问卷，并使用参与者在所获得的维度上的分数来询问决策缺陷是否存在- 在二阶不确定性下，焦虑和抑郁都是常见的，或者是一个人或一个人特有的 其他的。我们将重点放在二阶不确定性上，因为这是我们在 但关于焦虑或抑郁是否与缺陷有关的调查有限 调整决策以适应二阶不确定性。二阶不确定性在两种情况下都出现 我们的行动导致特定结果的概率随时间(波动性)和信息的变化而变化 估计某一行动导致某一结果的可能性有多大所需的信息不是完全可用的(模棱两可)。 在拟议的研究中，我们将使用波动性和模棱两可的操作来检查赤字在 二阶不确定性下的决策是焦虑和抑郁的共同之处，或者是一个人所特有的 或其他以及这种缺陷是领域通用的还是领域特定的(因结果类型而异：厌恶， 奖励获得或奖励损失)。将使用在线研究进行复制工作并检查是否受损 二阶不确定性下的决策主要与内化症状或常见症状有关 涉及到更广泛的精神变态。这些在线研究还将使我们能够测试探索性假设 与精神病理学的其他方面有关的。了解决策变化的程度- 二阶不确定性是焦虑或抑郁所特有的，焦虑和抑郁都是常见的。 抑郁症(即内化精神病的跨诊断标记物)，或与 更广泛地说，精神病理学是重要的澄清，以便我们可以更好地定制认知和心理- 针对不同患者群体的教育干预。它还可能有助于澄清现有的干预措施 与焦虑有关的发展(例如，关注歧义厌恶的CBT)可以有价值地应用于 其他形式的精神变态。