Attentional switching in larval zebrafish

Decision-making strategies in the face of conflicting or uncertain sensory input have been successfully described in many species. We analyze large behavioral datasets of larval zebrafish engaged in a "coherent dot" optomotor assay and find that animal performance is bimodal. Performance can be separated into two "states"-an engaged (attentive) state with high performance, where fish consistently turn in the direction of coherent motion, and a second, disengaged (inattentive) state, where performance drops to chance. A hidden Markov model is sufficient to model these transitions and can be incorporated into a drift-diffusion model framework that has previously been used to model perceptual decision-making in larval zebrafish. Furthermore, we fit a mixture model of performance distributions and extract two latent variables termed "focus" and "competence" that are largely influenced by parents and environmental context, respectively. This quantitative framework can potentially help to identify a genetic basis and a neural mechanism for attention that extends across organisms.

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Publication Details

Journal
Science Advances
Published
2025-10-03
DOI
https://doi.org/10.1126/sciadv.ads4994
Citations
4
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
5.72

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article

Attentional switching in larval zebrafish

Hanna Zwaka, Kumaresh Krishnan, Akila Muthukumar, Mark C. Fishman et al.
4 citations
Science Advances
Neural dynamics and brain function
5.72
article

Attentional switching in larval zebrafish

Hanna Zwaka, Kumaresh Krishnan, Akila Muthukumar, Mark C. Fishman, Paula Pflitsch, Adrienne L. Fairhall, Armin Bahl, Florian Engert, Scott C. Sterrett
article en
4 citations

Abstract

Decision-making strategies in the face of conflicting or uncertain sensory input have been successfully described in many species. We analyze large behavioral datasets of larval zebrafish engaged in a "coherent dot" optomotor assay and find that animal performance is bimodal. Performance can be separated into two "states"-an engaged (attentive) state with high performance, where fish consistently turn in the direction of coherent motion, and a second, disengaged (inattentive) state, where performance drops to chance. A hidden Markov model is sufficient to model these transitions and can be incorporated into a drift-diffusion model framework that has previously been used to model perceptual decision-making in larval zebrafish. Furthermore, we fit a mixture model of performance distributions and extract two latent variables termed "focus" and "competence" that are largely influenced by parents and environmental context, respectively. This quantitative framework can potentially help to identify a genetic basis and a neural mechanism for attention that extends across organisms.

Science AdvancesVol. 11(40)
Harvard University (US), University of Konstanz (DE), University of Washington (US), Leibniz Institute for Neurobiology (DE)
Fidelity Biosciences
Openalex Percentile: Top 4%
Neural dynamics and brain function
5.72
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