Pupil-Based Prediction of Affect in VR: A Machine Learning Approach
Year: 2025
Type: conference-paper
Abstract: Adapting VR scenarios in real time based on users’ emotional responses is a promising advancement for developers. However, most VR environments are either "blind" to emotions or depend on intrusive, contact-based methods. This study introduces a novel, noninvasive approach for predicting arousal and valence using pupil reactivity and gaze features. Data from 105 participants (ages 18–30) viewing 120 emotional images via a VR headset with eye tracking informed machine learning models. These models, leveraging features like mean and standard deviation of pupil data, achieved F1 scores of 0.64 (valence) and 0.73 (arousal), highlighting potential real-world applications in VR development.
Institutions: Jagiellonian University, AGH University of Krakow
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4409761748