Eyes on Attention: AI-Based Webcam Assessment of Sustained Attention

This study examined the feasibility of an artificial intelligence system using webcam eye tracking to estimate sustained attention performance. In a controlled laboratory study, 129 participants (mean age: 25.98 ± 2.07 years; 83.7% female) completed the AX Continuous Performance Task. Performance was operationalized as a reciprocal-log index of correct-response reaction time, with higher values indicating faster responses. Six visual scanning features were captured, and five composite models were compared: one literature-based and four derived from experimental data. All showed significant in-sample associations with performance. The weighted composite incorporating all six features provided the best in-sample fit, but participant-level split-half cross-validation yielded a modest out-of-sample association (r = 0.19). Univariate analyses linked better performance to faster jumps and lower length and speed irregularity. These findings provide preliminary evidence for webcam-derived gaze features in cognitive assessment under laboratory conditions, with further validation needed before integration into human–computer interaction applications.