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Record W4410329347 · doi:10.1017/flo.2025.14

Disentangling coexisting sensory pathways of interaction in schooling fish

2025· article· en· W4410329347 on OpenAlexaff
Rishita Das, Sean D. Peterson, Maurizio Porfiri

Bibliographic record

VenueFlow · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFish <Actinopterygii>Sensory systemBiologyPsychologyEvolutionary biologyNeuroscienceFishery

Abstract

fetched live from OpenAlex

Abstract Fish swimming together in schools interact via multiple sensory pathways, including vision, acoustics and hydrodynamics, to coordinate their movements. Disentangling the specific role of each sensory pathway is an open and important question. Here, we propose an information-theoretic approach to dissect interactions between swimming fish based on their movement and the flow velocity at selected measurement points in the environment. We test the approach in a controlled mechanical system constituted by an actively pitching airfoil and a compliant flag that simulates the behaviour of two fish swimming in line. The system consists of two distinct types of interactions – hydrodynamic and electromechanical. By using transfer entropy of the measured time series, we unveil a strong causal influence of the airfoil pitching on the flag undulation with an accurate estimate of the time delay between the two. By conditioning the computation on the flow-speed information, recorded by laser Doppler velocimetry, we discover a significant reduction in transfer entropy, correctly implying the presence of a hydrodynamic pathway of interaction. Similarly, the electromechanical pathway of interaction is identified accurately when present. The study supports the potential use of information-theoretic methods to decipher the existence of different pathways of interaction between schooling fish.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.155

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.256
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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