Barradas Isabel, Tschiesner Reinhard, Peer Angelika
Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, South Tyrol, Italy.
Faculty of Education, Free University of Bozen-Bolzano, Brixen, South Tyrol, Italy.
PLoS One. 2025 Jan 24;20(1):e0315929. doi: 10.1371/journal.pone.0315929. eCollection 2025.
Appraisal models, such as the Scherer's Component Process Model (CPM), represent an elegant framework for the interpretation of emotion processes, advocating for computational models that capture emotion dynamics. Today's emotion recognition research, however, typically classifies discrete qualities or categorised dimensions, neglecting the dynamic nature of emotional processes and thus limiting interpretability based on appraisal theory. In our research, we estimate emotion intensity from multiple physiological features associated to the CPM's neurophysiological component using dynamical models with the aim of bringing insights into the relationship between physiological dynamics and perceived emotion intensity. To this end, we employ nonlinear autoregressive exogeneous (NARX) models, as their parameters can be interpreted within the CPM. In our experiment, emotions of varying intensities are induced for three distinct qualities while physiological signals are measured, and participants assess their subjective feeling in real time. Using data-extracted physiological features, we train intrasubject and intersubject intensity models using a genetic algorithm, which outperform traditional sliding-window linear regression, providing a robust basis for interpretation. The NARX model parameters obtained, interpreted by appraisal theory, indicate consistent heart rate parameters in the intersubject models, suggesting a large temporal contribution that aligns with the CPM-predicted changes.
评估模型，如谢勒的成分过程模型（CPM），为情感过程的解释提供了一个精妙的框架，提倡采用能够捕捉情感动态的计算模型。然而，当今的情感识别研究通常对离散特质或分类维度进行分类，忽视了情感过程的动态本质，从而限制了基于评估理论的可解释性。在我们的研究中，我们使用动态模型从与CPM的神经生理成分相关的多个生理特征来估计情感强度，目的是深入了解生理动态与感知到的情感强度之间的关系。为此，我们采用非线性自回归外生（NARX）模型，因为其参数可以在CPM中得到解释。在我们的实验中，在测量生理信号的同时，针对三种不同特质诱发不同强度的情感，并且参与者实时评估他们的主观感受。利用从数据中提取的生理特征，我们使用遗传算法训练个体内和个体间强度模型，其性能优于传统的滑动窗口线性回归，为解释提供了坚实的基础。通过评估理论解释所获得的NARX模型参数，表明个体间模型中的心率参数一致，这表明与CPM预测变化相符的较大时间贡献。
