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

ABSTRACT:

Introduction

Interactive installation art offers immersive and participatory environments that elicit complex and multidimensional emotional experiences-encompassing sensorimotor engagement, affective resonance, and cognitive reflection. However, these emotional responses' inherently dynamic, subjective, and often pre-reflective nature poses significant challenges to their systematic prediction and computational modeling.

Methods

To address these challenges, the present study introduces an interpretable machine learning framework grounded in the Random Forest (RF) algorithm, which provides a balanced trade-off between predictive performance and model transparency, thereby aligning with the needs of theory-driven emotion research. Based on 390 valid questionnaire responses, e

SUBMITTER: Chen X 

PROVIDER: S-EPMC12360417 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature