Affective states during environmental experiences are dynamic, yet often studied as static outcomes. This study develops a computational framework to model mood evolution in human-environment interactions while accounting for individual heterogeneity. In an experiment with 213 adults viewing simulated urban walking environments, we applied a nonlinear mood updating model and latent class modeling to examine temporal dynamics along the Pleasure–Arousal–Dominance dimensions. Results indicated moderately stable updating for pleasure, limited change for arousal, and comparatively higher reactivity for dominance. Baseline mood remained the strongest predictor of final mood states, while personality traits, gender, and environmental familiarity accounted for additional individual differences. These findings advance understanding of context-dependent affective dynamics and their links to personal characteristics in everyday environmental experiences.
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