Emerging psychopathology studies are showing that patterns of changes in emotional state -- emotion dynamics -- are associated with overall well-being and mental health. More recently, there has been some work in tracking emotion dynamics through one's utterances, allowing for data to be collected on a larger scale across time and people. However, several questions about how emotion dynamics change with age, especially in children, and when determined through children's writing, remain unanswered. In this work, we use both a lexicon and a machine learning based approach to quantify characteristics of emotion dynamics determined from poems written by children of various ages. We show that both approaches point to similar trends: consistent increasing intensities for some emotions (e.g., anger, fear, joy, sadness, arousal, and dominance) with age and a consistent decreasing valence with age. We also find increasing emotional variability, rise rates (i.e., emotional reactivity), and recovery rates (i.e., emotional regulation) with age. These results act as a useful baselines for further research in how patterns of emotions expressed by children change with age, and their association with mental health.

翻译：新兴的精神病理学研究表明，情绪状态的变化模式——即情感动态——与整体幸福感和心理健康密切相关。近年来，已有一些研究通过追踪人们话语中的情感动态，从而能够在更大规模上跨时间和人群收集数据。然而，关于情感动态如何随年龄变化（尤其是在儿童中），以及如何通过儿童写作进行评估等问题，仍待解答。在本研究中，我们采用基于词典和机器学习的方法，量化了不同年龄儿童所写诗歌中情感动态的特征。我们发现，两种方法均指向相似趋势：一些情绪（如愤怒、恐惧、愉悦、悲伤、唤醒度和支配性）的强度随年龄持续增加，而情感效价则持续降低。我们还观察到，随着年龄增长，情绪变异性、上升速率（即情绪反应性）和恢复速率（即情绪调节能力）均有所增加。这些结果为进一步研究儿童表达情绪模式随年龄变化及其与心理健康的关联提供了有价值的基线。