Affect Dynamics in Context: A Hierarchical Bayesian AR Model for a Lab-Based Paradigm.
Affect Dynamics in Context: A Hierarchical Bayesian AR Model for a Lab-Based Paradigm.
复制标题
影响上下文中的动力学：基于实验室范式的分层贝叶斯 AR 模型。
DOI：
10.1080/00273171.2022.2160298
复制
发表时间：
2023
影响因子：
3.8
通讯作者：
Wood,JulieK
中科院分区：
文献类型：
--
作者：
Wood,JulieK
情感动力学作为一个领域，在过去的三十年里得到了极大的发展--测量快速变化的情感和对复杂过程建模的方法也是如此。尽管如此，该领域作为一个整体在很大程度上依赖于将影响动力学定义为时间序列的简单汇总统计，而不是将影响时间序列的建模作为一个过程。面向过程的建模(即，通常围绕基于过去状态的系统变化的时序模型)可以一次解释多个动态特征，并且可以根据产生动态的过程的理论特征来解释参数。例如，自回归(AR)模型是情感动态建模的一种常见选择，它量化了情感基线(截距)、情感惯性(自回归参数)以及一个人的情感系统中的反应性/可变性程度(创新/过程噪声的方差)。情感动力学文献中的另一个空白是理解情感动态与唤起它们的特定背景之间的关系。大多数关于情感动态的研究都是通过生态瞬时评估(EMA)或每日日记进行的，这些日记观察动态如何在几天或几个小时的时间尺度上展开，捕捉引起情感变化的日常生活背景的能力有限。然而，基于实验室的范式特别适合于考察不同背景下影响动态的个体差异。在这项研究中，我们使用了来自社区样本(N=73)的数据，参与者使用操纵杆按照情感平衡的提示对他们的“实时”情感感觉进行评级。六个刺激类别(其中每个有六个试验)分别是重大/次要负面事件、重大/次要正面事件、
Affect dynamics, as a field, has grown immensely in the past three decades—as have methods for measuring fast-changing affect and modeling complex processes. Still, the field as a whole is largely reliant on defining affect dynamics as simple summary statistics of timeseries, rather than modeling affect timeseries as a process. Process-oriented modeling (ie, timeordered models, usually framed around change in the system based on past states) can account for multiple dynamic features at once, and parameters can be interpreted in terms of theoretical features of the processes which produce dynamics. For example, autoregressive (AR) models, one common choice for affect dynamic modeling, quantify affective baselines (the intercept), affect inertia (autoregressive parameter), and the degree of reactivity/variability (variance of the innovation/process noise) in one’s affect system.Another gap in the affect dynamics literature is understanding the relationship between affect dynamics and particular contexts which evoke them. Most studies of affect dynamics are conducted via ecological momentary assessment (EMA) or daily diaries, which observe how dynamics unfold over the timescale of days or hours, with limited ability to capture the contexts of daily life which evoked changes in affect. Lab-based paradigms, however, are particularly well-suited to examine individual differences in affect dynamics across contexts. In this study, we use data from a community-based sample (N= 73), wherein participants rated their “real-time” affective feelings using a joystick, following affectively-valanced prompts. The six stimulus categories (within which there were six trials each) were Major/Minor Negative events, Major/Minor Positive Events,