Journal of Eye Movement Research

Optokinetic nystagmus (OKN) is an involuntary sawtooth eye movement that occurs in the presence of a drifting stimulus. Our experience is that low-amplitude/short-duration OKN can challenge the limits of our commercially available Pupil Neon eye-tracker, leading to false negative OKN detection results. We sought to investigate whether such instances could be remediated. We compared automated OKN detection using: (1) the gaze signal from the Pupil Neon (OKN-G), (2) centroid tracking (OKN-C), and (3) an image-phase-based “motion microscopy” technique (OKN-MMIC). The OKN-C and OKN-MMIC methods were also tested as a remediated step after a negative OKN-G result (OKN-C-STEP, OKN-MMIC-STEP). To validate the approaches adults (n = 22) with normal visual acuity was measured whilst viewing trials of an OKN induction stimulus shown at four levels of visibility. Confusion matrices and performance measures were determined for a “main” dataset that included all methods, and a “retest” set, which contained instances where centroid tracking failed. For the main set, all tested methods improved upon OKN-G by Matthew’s correlation coefficient (0.80–0.85 vs. 0.76), sensitivity (0.89–0.95 vs. 0.85), and accuracy (0.91–0.93 vs. 0.88); but only OKN-C yielded better specificity (0.90–0.96 vs. 0.95). For the retest set, MMIC and MMIC-STEP methods consistently improved upon the performance of OKN-G across all measures.

Visual search behavior, influenced by expertise, prior knowledge, training, and visual fatigue, is crucial in ophthalmic diagnostics. This study investigates differences in eye-tracking strategies between novice and experienced eye care practitioners during fundus image interpretation. Forty-seven participants, including 37 novices (first- to fourth-year optometry students) and 10 experienced optometrists (≥2 years of experience), viewed 20 fundus images (10 normal, 10 abnormal) while their eye movements were recorded using an Eyelink1000 Plus gaze tracker (2000 Hz). Diagnostic and laterality accuracy were assessed, and statistical analyses were conducted using Sigma Plot 12.0. Results showed that experienced practitioners had significantly higher diagnostic accuracy (83 ± 6.3%) than novices (70 ± 12.9%, p < 0.005). Significant differences in oculomotor behavior were observed, including median latency (p < 0.001), while no significant differences were found in median peak velocity (p = 0.11) or laterality accuracy (p = 0.97). Diagnostic accuracy correlated with fixation count in novices (r = 0.54, p < 0.001), while laterality accuracy correlated with total dwelling time (r = −0.62, p < 0.005). The experienced practitioners demonstrated systematic and focused visual search patterns, whereas the novices exhibited unorganized scan paths. Enhancing training with visual feedback could improve fundus image analysis accuracy in novice clinicians.

Eye movements are important indicators of problem-solving or solution strategies and are recorded using eye-tracking technologies. As they reveal how viewers interact with presented information during task processing, their analysis is crucial for educational research. Traditional methods for analyzing saccades, such as histograms or polar diagrams, are limited in capturing patterns in direction and amplitude. To address this, we propose a kernel density estimation approach that explicitly accounts for the data structure: for the circular distribution of saccade direction, we use the von Mises kernel, and for saccade amplitude, a Gaussian kernel. This yields continuous probability distributions that not only improve accuracy of representations but also model the underlying distribution of eye movements. This method enables the identification of strategies used during task processing and reveals the connections to the underlying cognitive processes. It allows for a deeper understanding of information processing during learning. By applying our new method to an empirical dataset, we uncovered differences in solution strategies that conventional techniques could not reveal. The insights gained can contribute to the development of more effective teaching methods, better tailored to the individual needs of learners, thereby enhancing their academic success.