Gaze Prediction as a Function of Eye Movement Type and Individual Differences
This work addresses variability in gaze prediction for eye-tracking systems, but it is incremental as it focuses on reporting and reducing inter-subject variation without introducing new methods.
The study analyzed individual differences in gaze prediction performance across three models and eye-movement types, finding that fixation noise and higher saccade velocities are associated with poorer prediction.
Eye movement prediction is a promising area of research with the potential to improve performance and the user experience of systems based on eye-tracking technology. In this study, we analyze individual differences in gaze prediction performance. We use three fundamentally different models within the analysis: the lightweight Long Short-Term Memory network (LSTM), the transformer-based network for multivariate time series representation learning (TST), and the Oculomotor Plant Mathematical Model wrapped in the Kalman Filter framework (OPKF). Each solution was assessed on different eye-movement types. We show important subject-to-subject variation for all models and eye-movement types. We found that fixation noise is associated with poorer gaze prediction in fixation. For saccades, higher velocities are associated with poorer gaze prediction performance. We think these individual differences are important and propose that future research should report statistics related to inter-subject variation. We also propose that future models should be designed to reduce subject-to-subject variation.