ABSTRACT
We investigate the development of visuospatial and oculomotor reading skills in a cohort of elementary school children. Employing a longitudinal methodology, the study applies the Topological serial digit Rapid Automated Naming (Top-RAN) battery, which evaluates visuospatial reading skills leveraging metrics addressing crowding, distractors, and voluntary attention orientation. The participant pool comprises 142 students (66 males, 76 females), including 46 non-native speakers (21 males, 25 females), representing a diverse range of ethnic backgrounds. The Top-RAN dataset encompasses performance, error, and self-correction metrics for each subtest and student, underscoring the significance of these factors in the process of reading acquisition. Analytical methods include dimensionality reduction, clustering, and classification algorithms, consolidated into a Python package to facilitate reproducible results. Our results indicate that visuospatial reading abilities vary according to the task and demonstrate a marked evolution over time, as seen in the progressive decrease in execution times, errors, and self-corrections. This pattern supports the hypothesis that the growth of oculomotor, attentional, and executive skills is primarily fostered by educational experiences and maturation. This investigation provides valuable insights into the dynamic nature of these skills during pivotal educational stages.
ABSTRACT
Understanding of animal collectives is limited by the ability to track each individual. We describe an algorithm and software that extract all trajectories from video, with high identification accuracy for collectives of up to 100 individuals. idtracker.ai uses two convolutional networks: one that detects when animals touch or cross and another for animal identification. The tool is trained with a protocol that adapts to video conditions and tracking difficulty.