Developmental and genetic modulation of evidence integration dynamics in zebrafish sensorimotor decision-making

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Abstract
SUMMARY: 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 typical 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. LINK TO VIDEOS: https://cloud.uni-konstanz.de/index.php/s/qx9DSFciAey3aD7 TEASER: Evidence-integration dynamics in zebrafish mature during development and are selectively altered by disease-associated mutations.
Funding Information
  • Deutsche Forschungsgemeinschaft (BA 5923/1-1)
  • Deutsche Forschungsgemeinschaft (EXC 2117 – 422037984)
  • ERC starting Grant (101075541 – CollectiveDecisions)
  • Zukunftskolleg, Universität Konstanz
  • NIH (U19NS104653)
  • Deutsche Forschungsgemeinschaft (455622343)

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