Putting affective dynamics into context: Towards a realistic and reliable modeling approach

最近的情感研究的目的是将情感现象理解为不断变化的人内过程。通过解开这些过程背后的动力，人们希望对情感功能和影响监管有更深入的了解。为此，该领域依赖于密集的纵向数据，以及动态的，特别是向量自回归(VAR)的时间序列建模。新的研究范式因其所谓的潜力而被许多人接受--但它也面临着不小的挑战。其中两个，开发现实的(即足够复杂的)情感功能建模方法，以及确保在给定通常有限的数据的情况下获得的建模解决方案的足够可靠性，形成了一个有趣的权衡。在这里提出的研究项目中，我的目标是解决这两个挑战，并解决由此产生的平衡行为。这产生了以下两个研究目标。首先，对于现实的建模方法，我借鉴了监管灵活性的概念，认为情感动态取决于并因此随背景而变化。为了经验性地研究这一点，VAR模型需要包括调节其参数的上下文变量，以及可能随着时间的推移而关联的情感动态。固定缓和时间序列分析(fmTSA；Adolf等人，2017)通过将观察到的上下文变量纳入作为调节变量来实现这种扩展的VAR模型。在这里，我建议沿着两条重要的路线进一步发展这种方法。一种是结合不同形式的主持人，以便能够识别不同模式或形式的依赖于上下文的情感功能。其次，研究了fmTSA在典型纵向数据条件下的可靠性。因此，可靠性是根据建模解决方案的精度(即估计的模型参数)以及根据参数估计得出的预测的精度来评估的。因此，我将介绍该方法的解释性和预测性性能。除了建立和实施回溯性可靠性诊断程序外，我还旨在进行前瞻性可靠性评估和改进。也就是说，对于预期参数值，我将先验地确定可靠性如何作为数据条件的函数变化。关于这些关联的知识可以用来优化纵向研究的设计，从而产生保证可靠的fmTSA解决方案的数据。综上所述，这里提出的研究项目应该最终形成一种高度可用和可信的建模方法，将情感动态置于背景中。

The aim of recent affect research is to understand affective phenomena as continuously changing within-person processes. By unraveling the dynamics that underlie these processes, one hopes to gain deeper insights into affective functioning and affect regulation. To this end, the field relies on intense longitudinal data, and dynamic, especially vector autoregressive (VAR), time series modeling. The new research paradigm has been embraced by many due to its supposed potential – but it also bears non-trivial challenges. Two of them, developing realistic (i.e., sufficiently complex) modeling approaches to affective functioning, and ensuring adequate reliability of the obtained modeling solutions given typically limited data, form an intriguing trade-off. In the here proposed research project, I aim to address both challenges, and tackle the resulting balancing act. This yields the following twofold research objective.First, for a realistic modeling approach, I draw upon notions of regulatory flexibility, suggesting that affective dynamics depend on and thus change with context. To empirically study this, VAR models are required to include contextual variables that moderate their parameters, and the presumably associated affective dynamics, over time. Fixed moderated time series analysis (fmTSA; Adolf et al., 2017) implements such an extended VAR model by incorporating observed contextual variables as moderators. Here, I propose to further develop this method along two important lines. One concerns the incorporation of moderators of different formats to enable the identification of different patterns or forms of context-dependent affective functioning. The other concerns the rigorous handling of measurement intervals of unequal length, which could otherwise lead to biased modeling solutions.Second, I propose to investigate the reliability of fmTSA under typical longitudinal data conditions. Reliability is thereby evaluated in terms of the precision of modeling solutions (i.e., estimated model parameters), and in terms of the precision of predictions derived from parameter estimates. I thus cover the explanatory as well as the predictive performance of the method. Besides establishing and implementing routines for retrospective reliability diagnosis, I also aim at prospective reliability assessment and improvement. That is, for expected parameter values, I will a-priori determine how reliability varies as a function of data conditions. The knowledge about these associations can then be used to optimize the design of longitudinal studies, and thus produce data that guarantee reliable fmTSA solutions.Taken together, the here proposed research project should culminate in a highly usable and credible modeling approach to putting affective dynamics into context.