LAUSR

This paper proposes a novel evaluation framework, termed "critical evaluation periods," for evaluating continuous gaze prediction models. This framework emphasizes prediction performance when it is most critical for gaze prediction to be accurate relative to user perception. Based on perceptual characteristics of the human visual system such as saccadic suppression, this framework provides a more practical assessment of gaze prediction performance for gaze-contingent rendering compared to the dominant sample-by-sample evaluation strategy employed in literature, which overemphasizes performance during easy-to-predict periods of fixation. Using a case study with a lightweight deep learning gaze prediction model, we observe a significant discrepancy in the reported prediction accuracy between the proposed critical evaluation periods and the dominant evaluation strategy employed in literature. Based on our findings, we suggest that the proposed framework is more suitable for evaluating the performance of continuous gaze prediction models intended for gaze-contingent rendering applications.