Developmental and genetic modulation of evidence integration dynamics in zebrafish sensorimotor decision-making
Abstract
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.
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BibTeX
@article{garza2026developmental,
title = {{Developmental and genetic modulation of evidence integration dynamics in zebrafish sensorimotor decision-making}},
author = {Roberto Garza and Ahmed El Hady and Armin Bahl},
journal = {Communications Biology},
year = {2026},
month = oct,
doi = {10.1038/s42003-026-11264-y},
url = {https://www.nature.com/articles/s42003-026-11264-y},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Developmental and genetic modulation of evidence integration dynamics in zebrafish sensorimotor decision-making
AU - Roberto Garza
AU - Ahmed El Hady
AU - Armin Bahl
PY - 2026
DA - 2026/10/29
JO - Communications Biology
DO - 10.1038/s42003-026-11264-y
UR - https://www.nature.com/articles/s42003-026-11264-y
AB - 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.
SN - 2399-3642
ER -