Abstract

The stimuli-task paradigm plays a pivotal role in detecting attentional patterns, which form the foundation of user experience (UX) research for designing neuro-adaptive applications using eye-tracking sensors. Grounded in attentional bias theories, we propose and evaluate a stimuli-task paradigm, along with corresponding eye-movement metrics, to detect anxiety using only eye-tracking data. Our predictive model, developed for testing this paradigm, achieved an 83.3% accuracy in identifying the likelihood of anxiety presence. These results demonstrate the efficacy of our proposed paradigm and the potential of eye-movement data to reveal distinct attentional patterns between individuals with and without anxiety. Furthermore, the eye-movement metrics used in this study could be broadly applicable in detecting user engagement.

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