September 10, 2025Frontiers in PsychologyOpen Access

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

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Machine learning approach predicts emotional responses in interactive art, highlighting the role of cognitive reflection and affective computing.

The Random Forest model achieved the highest accuracy in predicting cognitive reflection, suggesting a strong connection between cognition and emotion.
Model evaluation indicated effective classification for active personalization, with an F1 score of 0.673 and accuracy of 0.705.
Bodily responses were identified as less predictable, potentially due to their non-verbal and embodied nature impacting measurement reliability.
Results suggest the model's usefulness for exploring emotion in interactive art, with implications for affective computing and HCI research.

Chen et al. (2025) studied this question.

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Machine learning approach predicts emotional responses in interactive art, highlighting the role of cognitive reflection and affective computing.