Using Real-World, Personally Meaningful Events to build Computational Models of Emotion

使用现实世界中对个人有意义的事件来构建情感计算模型

• 批准号: 10303449
• 负责人: AARON S HELLER
• 金额: $ 22.58万
• 依托单位: UNIVERSITY OF MIAMI CORAL GABLES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10303449
• 关键词: Address; Adolescence; Affective; Affective Symptoms; Anhedonia; Area; Basic Science; Behavior; Caring; Chemistry; Clinical; Cognitive Therapy; Computer Models; Consensus; Depressed mood; Disease; Ecological momentary assessment; Emotional; Emotions; Enrollment; Ensure; Environment; Etiology; Evaluation; Event; Factor Analysis; Feedback; Fright; Grant; Hour; Individual; Individual Differences; Intervention; Knowledge; Life; Link; Mathematics; Measures; Mental Depression; Mental Health; Modeling; Mood Disorders; Moods; Outcome; Panic; Participant; Pattern; Persons; Positioning Attribute; Post-Traumatic Stress Disorders; Procedures; Process; Properdin; Psychiatry; Reporting; Research; Running; Sampling; Speed; Standardization; Stimulus; Students; Symptoms; Testing; Time; Translations; Validation; Work; anxiety symptoms; base; clinically relevant; comorbidity; depressive symptoms; eating pathology; experience; follow-up; improved; insight; negative affect; novel; predicting response; psychiatric symptom; response; social; social anxiety; substance use; success; trigger point; undergraduate student; virtual

PROJECT SUMMARY There is an emerging consensus that using computational modeling to mathematically operationalize and identify the drivers of behavior such as emotional response is critical to better account for individual differences. The hope is that operationalizing the drivers of emotion (and other psychiatrically relevant processes) will standardize how these psychiatrically relevant processes are defined and this will speed the progress of mental health research. However, despite the promise for computational modeling to better account for and parse the timecourse of emotional responses, there have thus far been few clinically relevant insights. One reason for the lack of translation is that computational modeling of psychiatrically relevant processes rarely employ ecologically meaingful paradigms. With few exceptions, there are virtually no studies that focally measure emotional responses precisely timed to when personally relevant and meaningful events occur. Alongside needing to measure emotional responses after personally meaningful events is the need to measure the timecourse of such responses–which, in the case of personally meaningful events unfold over hours, not on the timescale of seconds as is often assessed in the lab. In this proposal, we build on our initial work using ecological momentary assessment (EMA) of positive and negative emotion in an unselected undergraduate sample using exam grade feedback as a personally meaningful event; students in General Chemistry care deeply about their grades in the course. We build computational models to predict the timecourse of emotion and find that when we time-lock EMAs once individuals first see their exam grades that both the grade prediction error (PE; the difference between the grade they report they think they will receive [after taking the exam but before exam feedback]) and the grade itself are necessary to account for the timecourse of the emotional response; further, the grade PE has a significantly larger effect on the timecourse of the emotional response than the grade itself. This R21 proposal advances this work toward building a fundamental, basic-science understanding of the drivers of emotion using valid, reliable, and comprehensive computational models in convenience samples. We will (1) expand the set of predictors in the model to improve our computational characterization of EMA-assessed emotional response to real-life outcomes, including, prediction confidence, social comparison, and perceived control; (2) determine which parameters from this computational model most strongly impact the PA and NA timecourse; and (3) test whether individual model parameters are linked to depression and anxiety symptoms. This project will position us for a follow-up R01 focused on how these mechanisms go awry in individuals suffering from affective disorders.

项目摘要 有一个新兴共识，使用计算建模来数学运行和 确定行为驱动因素（例如情感反应）对于更好地说明个人至关重要 差异。希望是操作情绪的驱动力（以及其他具有精神上相关的驱动力 过程）将标准化这些精神上相关的过程的定义，这将加快 心理健康研究的进展。但是，尽管有望更好地建模 解释并解析情感反应的时间，到目前为止很少有临床相关 见解。缺乏翻译的原因之一是对精神上相关的计算建模 流程很少采用生态上的范式。除少数例外，几乎没有研究 该焦点衡量的情绪反应准确地定于个人相关和有意义的事件时 在个人有意义的事件之后，还需要衡量情绪反应是需要 衡量此类回应的时间 - 在个人有意义的事件的情况下进行 小时，而不是在实验室中经常评估的秒数的时间表。在此提案中，我们建立在最初的基础上 在未选中的积极和负面情绪的生态瞬时评估（EMA）的工作 使用考试成绩反馈作为个人有意义的事件的本科样本；总体学生 化学对课程中的成绩非常关注。我们建立了计算模型来预测 情感时期，发现当我们有时间锁定EMA时，一旦个人首先看到他们的考试成绩 等级预测错误都 [参加考试后，但在考试反馈之前]）和成绩本身对于说明 情感反应的时间；此外，PE级对时代的影响明显更大 情感反应比等级本身。这项R21提案将这项工作朝着建造 使用有效，可靠和 便利样本中的全面计算模型。我们将（1）在 改善我们对现实生活中EMA评估情感反应的计算表征的模型 结果，包括预测信心，社会比较和感知的控制； （2）确定哪个 来自该计算模型的参数最强烈影响PA和NA时间； （3）测试 单个模型参数是否与抑郁症和焦虑症状有关。这个项目将定位 我们进行后续R01的重点是这些机制如何在患有情感的人中出现问题 疾病。