Evidence map›Paper›PMID 31769753›Full record

ArticleeLife2019

Elements of a stochastic 3D prediction engine in larval zebrafish prey capture.

Andrew D Bolton, Martin Haesemeyer, Josua Jordi, Ulrich Schaechtle, Feras A Saad, Vikash K Mansinghka, Joshua B Tenenbaum, Florian Engert

Abstract read
In one paragraph

Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed.

  1. Article
  2. Article
  3. Larval zebrafish minimize energy consumption during hunting via adaptive movement selection.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  4. Dissecting Larval Zebrafish Hunting using Deep Reinforcement Learning Trained RNN Agents.Proceedings of the ... Conference on Cognitive Computational Neuroscience · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Neurocircuitry of Predatory Hunting.Neuroscience bulletin · 2023
    Review
  15. Article
  16. Article
  17. Whole-brain imaging of freely-moving zebrafish.Frontiers in neuroscience · 2023
    Review
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Andrew D BoltonCenter for Brain Science, Harvard University, Cambridge, United States.ORCID 0000-0003-3059-7226
Martin HaesemeyerCenter for Brain Science, Harvard University, Cambridge, United States.ORCID 0000-0003-2704-3601
Josua JordiCenter for Brain Science, Harvard University, Cambridge, United States.
Ulrich SchaechtleBrain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States.
Feras A SaadBrain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States.
Vikash K MansinghkaBrain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States.
Joshua B TenenbaumBrain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States.
Florian EngertCenter for Brain Science, Harvard University, Cambridge, United States.

Funding

Zooming into the fish's brain-What is really going on! Connectomics analysis of larval zebrafish.U19NS104653 · NINDS · HARVARD UNIVERSITY · PI JOSHUA T VOGELSTEIN · 2017 to 2026
$39.4M
NIH HHS U19NS104653NINDS NIH HHS U19 NS104653
6 · The paper itself

Abstract

The computational principles underlying predictive capabilities in animals are poorly understood. Here, we wondered whether predictive models mediating prey capture could be reduced to a simple set of sensorimotor rules performed by a primitive organism. For this task, we chose the larval zebrafish, a tractable vertebrate that pursues and captures swimming microbes. Using a novel naturalistic 3D setup, we show that the zebrafish combines position and velocity perception to construct a future positional estimate of its prey, indicating an ability to project trajectories forward in time. Importantly, the stochasticity in the fish's sensorimotor transformations provides a considerable advantage over equivalent noise-free strategies. This surprising result coalesces with recent findings that illustrate the benefits of biological stochasticity to adaptive behavior. In sum, our study reveals that zebrafish are equipped with a recursive prey capture algorithm, built up from simple stochastic rules, that embodies an implicit predictive model of the world.

Indexed as

Models, NeurologicalPredatory BehaviorAnimalsLarvaSensorimotor CortexVisual PerceptionZebrafishanimal cognitionbiological stochasticitycomputationneurosciencephysical modelsphysics of living systemspredictionprey capturezebrafish

Identifiers

PMID31769753
PMCPMC6930116

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.