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Record W4412418527 · doi:10.1016/j.anbehav.2025.123246

The influence of social rank on learning in a group-living fish

2025· article· en· W4412418527 on OpenAlexafffund
Elias Latchem, Culum Brown, Sigal Balshine

Bibliographic record

VenueAnimal Behaviour · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Ontario
KeywordsFish <Actinopterygii>Group (periodic table)Rank (graph theory)Group livingPsychologyFisheryBiologyEcologyMathematicsCombinatoricsPhysics

Abstract

fetched live from OpenAlex

Dominance hierarchies are found in many group-living species, and an individual’s social rank can influence their access to resources, behaviours and physiology. However, the effect of rank on learning capability has not been well studied. Here, we examined how rank influences learning in the group-living cichlid fish Neolamprologus pulcher . We tested learning in both dominant and subordinate fish and investigated whether rank is related to the capacity to learn independently as well as from others. Fish learned to move coloured discs to access a food reward, either by trial and error on their own, or by watching a trained demonstrator. We found no differences between ranks in the individual associative learning task, but subordinates were faster at changing their behaviour when we changed the reward rules (during the reversal learning phase). We also found no differences in the number of trials it took dominants and subordinates to socially learn the task (from watching demonstrators), but individuals learned the task faster when they could observe others. Our results indicate that some aspects of cognition can be influenced by social rank, but rank does not appear to affect general learning ability.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.267

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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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 designObservational
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 routes2
Has abstractyes

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