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 有一个正在形成的共识，即使用计算建模来数学地操作化， 确定行为的驱动因素，如情绪反应，对于更好地解释个人行为至关重要。 差异希望是，将情感（以及其他精神病学相关的）驱动因素操作化， 过程）将标准化如何定义这些精神病学相关的过程，这将加快 心理健康研究的进展。然而，尽管计算机建模的承诺，以更好地 解释和解析情绪反应的时间过程，迄今为止，很少有临床相关的 见解.缺乏翻译的一个原因是精神病学相关的计算建模 过程很少采用生态意义的范例。除了极少数例外，几乎没有任何研究 当个人相关和有意义的事件发生时， 发生.除了需要测量个人有意义的事件后的情绪反应外，还需要 衡量这些反应的时间进程，在个人有意义的事件发生的情况下， 小时，而不是像实验室中经常评估的那样以秒为单位的时间尺度。在本提案中，我们建立在我们最初的 工作使用生态瞬时评估（EMA）的积极和消极的情绪，在一个 本科生样本使用考试成绩反馈作为个人有意义的事件;一般学生 化学专业学生非常关心自己在这门课上的成绩。我们建立计算模型来预测 情绪的时间进程，并发现，当我们锁定EMA一旦个人第一次看到他们的考试成绩， 成绩预测误差（PE;他们报告的成绩与他们认为他们将获得的成绩之间的差异 [在参加考试之后，但在考试之前的反馈]）和分数本身是必要的，以说明 情绪反应的时程;此外，年级体育对时程的影响显著更大 比分数本身更重要这项R21提案将这项工作推向了建立一个 基本的，基本的科学理解的驱动因素的情绪使用有效的，可靠的， 方便样本中的综合计算模型。我们将（1）扩展预测器的集合， 该模型可以改善我们对现实生活中EMA评估的情绪反应的计算特征， 结果，包括，预测信心，社会比较，和感知控制;（2）确定哪些 来自该计算模型的参数对PA和NA时程的影响最大;以及（3）测试 个体模型参数是否与抑郁和焦虑症状有关。该项目将定位 我们进行了后续的R 01研究，重点是这些机制在情感障碍患者中是如何出错的。 紊乱