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Kumaresh Krishnan1, Akila Muthukumar2, Scott Sterrett3

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Larval zebrafish exhibit bimodal performance in decision-making tasks, switching between attentive and inattentive states. This study quantitatively models attention using focus and competence variables, potentially revealing genetic and neural underpinnings across species.

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Area of Science:

  • Neuroscience
  • Behavioral Biology
  • Computational Biology

Background:

  • Decision-making under sensory uncertainty is crucial for survival.
  • Larval zebrafish are a powerful model for studying perceptual decision-making.

Purpose of the Study:

  • To quantitatively model attentional states in larval zebrafish during a visual task.
  • To identify factors influencing these attentional states.

Main Methods:

  • Analysis of large behavioral datasets from larval zebrafish in a coherent dot optomotor assay.
  • Application of a hidden Markov model and a drift-diffusion model.
  • Fitting a mixture model to extract latent variables: 'focus' and 'competence'.

Main Results:

  • Larval zebrafish performance in the task is bimodal, characterized by engaged (high performance) and disengaged (chance performance) states.
  • Transitions between these states can be modeled using a hidden Markov model.
  • 'Focus' and 'competence' variables, influenced by parental and environmental factors respectively, explain performance variations.

Conclusions:

  • A quantitative framework for modeling attention in larval zebrafish has been developed.
  • This framework can help elucidate the genetic and neural basis of attention.
  • The findings suggest potential cross-species relevance for understanding attention mechanisms.