2026-10-14T06:59:47Zhttps://www.radar-service.eu/oai/OAIHandler

10.48606/rbfa7tt6aez1pjtj2026-10-01T03:00:15Z

https://dx.doi.org/10.48606/rbfa7tt6aez1pjtj Garza, Roberto Bahl, Armin Behavioral tracking data and source code for data analysis and simulation of zebrafish random-dot-motion integration, and drift-diffusion explorer – related to Garza et al. 2026 University of Konstanz 2026 Biology dataset Python code for data analysis and simulation Software info:eu-repo/semantics/openAccess https://opensource.org/licenses/MIT El Hady, Ahmed Animals integrate information over time and maintain persistent internal representations of cues to guide decision-making. How the underlying behavioral algorithms of individual animals depend on factors such as experience, developmental stage, or genotype remains poorly understood. Drift-diffusion models provide a powerful theoretical framework to describe and predict performance metrics across a wide range of species. The stochastic nature of these models and the typically limited throughput of most experimental designs challenge the automatic inference of latent variables. Here, we combine high-throughput behavioral assays in larval zebrafish with drift-diffusion modeling, revealing that larvae progressively develop more persistent, self-reinforcing integration dynamics during early development. This effect is reduced in fish carrying mutations in genes linked to human epilepsy and schizophrenia. Our results show that behavior-based drift-diffusion modeling can offer a scalable, automated approach to generate experimentally testable hypotheses about the algorithmic implementation of sensorimotor integration in health and disease. larval zebrafish random-dot-motion integration drift-difffusion model Bayesian optimization sensorimotor decision-making virtual environment autism, epilepsy, and schizophrenia El Hady, Ahmed eng 10.64898/2026.03.01.708829 https://ror.org/018mejw64 https://ror.org/00k4n6c32 https://ror.org/018mejw64 https://doi.org/10.13039/501100024848 https://ror.org/026stee22 https://ror.org/05h1kgg64 https://ror.org/018mejw64 application/x-tar