Predicting emotional responses in interactive art using Random Forests: a model grounded in enactive aesthetics

These differential results underscore the relative tractability of modeling reflective and agentic emotional states in contrast to those rooted in sensorimotor or affective processes. Moreover, the model's consistent performance across all evaluation phases affirms its suitability as an exploratory tool for investigating emotion in interactive art contexts. This study contributes to the evolving convergence of affective computing, human-computer interaction (HCI), and empirical aesthetics. The proposed framework yields actionable insights for the design of emotionally adaptive systems. Future research should consider the integration of multimodal and temporally granular data, and the ethical dimensions associated with affective adaptivity in artistic and public-facing environments.