Journal of Eye Movement Research

White noise has been proposed to enhance cognitive performance in children with ADHD, but findings are inconsistent, and benefits vary across tasks and individuals. Such variability suggests that diagnostic comparisons may overlook meaningful developmental differences. This exploratory study examined whether developmental characteristics and subjective evaluations of auditory and visual white noise predicted performance changes in two eye-movement tasks: Prolonged Fixation (PF) and Memory-Guided Saccades (MGS). Children with varying degrees of ADHD symptoms completed both tasks under noise and no-noise conditions, and noise benefit scores were calculated as the performance difference between conditions. Overall, white-noise effects were small and dependent on noise modality and task. In the PF task, large parent-rated perceptual difficulties and high visual noise discomfort were associated with improved performance under noise. In the MGS task, poor motor skills predicted visual noise benefit, whereas large visual noise discomfort predicted reduced noise benefit. These findings suggest that beneficial effects of white noise are influenced by developmental characteristics and subjective perception in task-dependent ways. The results highlight the need for individualized, transdiagnostic approaches in future noise research and challenge the notion of white noise as categorically beneficial for ADHD.

Eye movement classification, particularly the identification of fixations and saccades, plays a vital role in advancing our understanding of neurological functions and cognitive processing. Conventional modalities of data, such as RGB webcams, often face limitations such as motion blur, latency and susceptibility to noise. Neuromorphic Vision Sensors, also known as event cameras (ECs), capture pixel-level changes asynchronously and at a high temporal resolution, making them well suited for detecting the swift transitions inherent to eye movements. However, the resulting data are sparse, which makes them less well suited for use with conventional algorithms. Spiking Neural Networks (SNNs) are gaining attention due to their discrete spatio-temporal spike mechanism ideally suited for sparse data. These networks offer a biologically inspired computational paradigm capable of modeling the temporal dynamics captured by event cameras. This study validates the use of Spiking Neural Networks (SNNs) with event cameras for efficient eye movement classification. We manually annotated the EV-Eye dataset, the largest publicly available event-based eye-tracking benchmark, into sequences of saccades and fixations, and we propose a convolutional SNN architecture operating directly on spike streams. Our model achieves an accuracy of 94% and a precision of 0.92 across annotated data from 10 users. As the first work to apply SNNs to eye movement classification using event data, we benchmark our approach against spiking baselines such as SpikingVGG and SpikingDenseNet, and additionally provide a detailed computational complexity comparison between SNN and ANN counterparts. Our results highlight the efficiency and robustness of SNNs for event-based vision tasks, with over one order of magnitude improvement in computational efficiency, with implications for fast and low-power neurocognitive diagnostic systems.

Fluent word reading is a key literacy skill, yet the full extent of the oculomotor underpinnings in developing readers remains unknown. Rapid automatized naming (RAN) is a useful clinical measure that has been shown to predict word reading fluency. Here we use RAN scores to predict early, mid, and late local stages of word reading as measured by eye tracking in children who are at a critical time in their literacy development. Thirty-three children participated in two RAN tasks (rapid letter naming (RLN) and rapid digit naming (RDN)) and an eye-tracking task, which included sentence-level reading with an embedded target word. The eye-tracking measures of first fixation duration, regression path duration, and total word reading time were used as early, mid, and late local measures, respectively. RLN and RDN significantly predicted only the mid-stage of the reading process (regression path duration). Faster RLN and RDN times were associated with briefer regressions from target words. Preliminary results link behavioral RAN performance to a mid-stage oculomotor variable, indicating that children with slower RAN times may exhibit longer regressions during reading, suggesting possible difficulties with the integration of phonological processing skills.