Evidence map›Paper›PMID 32176680›Full record

ArticlePLoS computational biology2020

Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish.

Liu Lei, Ramón Escobedo, Clément Sire, Guy Theraulaz

Abstract read
In one paragraph

Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Survival Strategies and Color Preferences of Mandarin Fish (Animals : an open access journal from MDPI · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Selective social interactions and speed-induced leadership in schooling fish.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
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  19. Article
  20. A data-driven method for reconstructing and modelling social interactions in moving animal groups.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020
    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

4 authors.

Liu LeiUniversity of Shanghai for Science and Technology, Shanghai, China.ORCID 0000-0002-8489-275X
Ramón EscobedoCentre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, Centre National de la Recherche Scientifique (CNRS), Université de Toulouse-Paul Sabatier (UPS), Toulouse, France.ORCID 0000-0002-9945-0460
Clément SireLaboratoire de Physique Théorique, CNRS and Université de Toulouse - Paul Sabatier, Toulouse, France.ORCID 0000-0003-4089-4013
Guy TheraulazCentre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, Centre National de la Recherche Scientifique (CNRS), Université de Toulouse-Paul Sabatier (UPS), Toulouse, France.ORCID 0000-0002-3062-4215

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coordinated motion and collective decision-making in fish schools result from complex interactions by which individuals integrate information about the behavior of their neighbors. However, little is known about how individuals integrate this information to take decisions and control their motion. Here, we combine experiments with computational and robotic approaches to investigate the impact of different strategies for a fish to interact with its neighbors on collective swimming in groups of rummy-nose tetra (Hemigrammus rhodostomus). By means of a data-based agent model describing the interactions between pairs of H. rhodostomus (Calovi et al., 2018), we show that the simple addition of the pairwise interactions with two neighbors quantitatively reproduces the collective behavior observed in groups of five fish. Increasing the number of interacting neighbors does not significantly improve the simulation results. Remarkably, and even without confinement, we find that groups remain cohesive and polarized when each agent interacts with only one of its neighbors: the one that has the strongest contribution to the heading variation of the focal agent, dubbed as the "most influential neighbor". However, group cohesion is lost when each agent only interacts with its nearest neighbor. We then investigate by means of a robotic platform the collective motion in groups of five robots. Our platform combines the implementation of the fish behavioral model and a control system to deal with real-world physical constraints. A better agreement with experimental results for fish is obtained for groups of robots only interacting with their most influential neighbor, than for robots interacting with one or even two nearest neighbors. Finally, we discuss the biological and cognitive relevance of the notion of "most influential neighbors". Overall, our results suggest that fish have to acquire only a minimal amount of information about their environment to coordinate their movements when swimming in groups.

Indexed as

Decision Making, SharedAnimalsBehavior, AnimalCharacidaeComputational BiologyFishesInterpersonal RelationsModels, BiologicalMovementRoboticsSocial BehaviorSoftwareSwimming

Identifiers

PMID32176680
PMCPMC7098660

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.