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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

基本信息

批准号：
10459593
负责人：
AARON S HELLER
金额：
$ 18.81万
依托单位：
UNIVERSITY OF MIAMI CORAL GABLES
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10459593
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 translational potential trigger point undergraduate student virtual

项目摘要

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.

项目总结一个正在形成的共识是，使用计算建模来在数学上操作和确定情绪反应等行为的驱动因素对于更好地解释个人是至关重要的不同之处。希望将情绪的驱动力(和其他精神病学上的相关因素)付诸实施过程)将标准化这些精神病学相关过程的定义，这将加快心理健康研究进展。然而，尽管计算建模有望更好地解释和解析情绪反应的时间进程，到目前为止，很少有临床相关的研究洞察力。缺乏翻译的一个原因是精神病学上相关的计算模型流程很少使用生态意义上微薄的范例。除了极少数例外，几乎没有任何研究集中地测量情绪反应，精确地计时到与个人相关和有意义的事件发生。除了需要测量对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--在发生对个人有意义的事件的情况下小时，而不是通常在实验室中评估的秒时间刻度。在这个提案中，我们在最初的基础上使用生态瞬时评估(EMA)对未被选中的人的积极和消极情绪进行工作使用考试成绩反馈作为个人有意义的事件的本科生样本；一般学生化学非常关心他们在这门课上的成绩。我们建立计算模型来预测情绪的时间进程，并发现当我们对EMA进行时间锁定时，当人们第一次看到他们的考试成绩时，成绩预测误差(PE；他们所报告的成绩与他们认为将获得的成绩之间的差异 [参加考试后，但在考试反馈之前])和分数本身是必要的，以说明情绪反应的时间进程；此外，体育年级对时间进程的影响显著更大比分数本身更重要的情感反应。R21提案将这项工作推向了构建用有效、可靠和科学的方法理解情绪的驱动因素在方便的样本中提供全面的计算模型。我们将(1)扩展中的预测值集合改进EMA评估的情绪反应对现实生活的计算表征的模型结果，包括预测信心、社会比较和感知控制；(2)决定哪些此计算模型中的参数对PA和NA时间进程的影响最大；以及(3)测试个体模型参数是否与抑郁和焦虑症状有关。该项目将定位于美国的后续R01集中于这些机制是如何在患有情感性精神障碍的个体中出错的精神错乱。