A Dynamic, Person-Centered Approach to Emotions in Complex Skill Learning: Antecedent and Performance Outcomes of Trajectory Profiles

Abstract

In modern workplace environments where dynamic performance demands are becoming commonplace, adaptive performance is critical for employee and organization success. Although emotions and shifts in emotion are recognized as important factors that relate to task performance, there is limited understanding regarding the dynamic interplay between emotions and adaptive performance. To investigate research questions surrounding emotion-performance relationships, researchers have primarily relied on variable-centered approaches, which seek to explain the relationship between variables at a population-level (Howard & Hoffman, 2018). However, these approaches do not account for nuanced experiences of emotion and assume affect patterns that are homogeneous across the population, leading to critical oversights in the scholarly literature. This dissertation builds theory on emotion-performance relationships by utilizing person-centered approaches, which can handle heterogeneity in multiple variables simultaneously to identify clusters of individuals with shared characteristics within a population (Howard & Hoffman, 2018; Woo et al., 2018). Emotion scores were collected repeatedly from participants across two samples of college undergraduates (N1 = 303, 100% male; N2 = 615, 52% male) while they engaged with a complex video game used in prior research on skill acquisition and adaptive performance (Hardy et al., 2014, 2024; Hughes et al., 2013). Growth mixture modeling, antecedent correlate analysis, and repeated measures ANOVA were used to identify and examine (a) profiles of emotion experiences that emerged during skill acquisition and adaptive performance, (b) factors that influence profile membership, and (c) the relationship between profile membership and performance. Distinct profiles with varied emotion trajectories were identified and characterized by their experience of greater positive emotions (e.g., happy, at ease), negative emotions (e.g., angry, anxious), or deactivating emotions (e.g., bored, calm, discouraged), with profiles that reported greater positive emotions performing best and profiles that reported greater negative emotions generally performing worst. Importantly, the person-centered approach leveraged in this study revealed a substantial proportion of individuals (over 60% across samples) experienced low emotional arousal following increases in task complexity, paired with moderate performance levels in adaptation relative to other individuals—a result variable-centered approaches have not uncovered. This work challenges simplistic valence-based models of emotion and provides novel insights regarding the dynamic and nuanced role of emotions in complex, high-demand performance environments. My findings advance emotion theory in performance contexts, particularly by clarifying the role of activation potential, how emotion trajectories inform emotion-performance relationship theory, and affective differences between acquisition and adaptation.