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Record W2074391931 · doi:10.1098/rsbl.2010.0247

What do fish make of mirror images?

2010· article· en· W2074391931 on OpenAlexfundno aff
Julie K. Desjardins, Russell D. Fernald

Bibliographic record

VenueBiology Letters · 2010
Typearticle
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsAmygdalaFish <Actinopterygii>BiologyHippocampusMirror imageNeuroscienceMirror neuronCognitionPsychologyCognitive psychologyPhysicsOptics

Abstract

fetched live from OpenAlex

Fish act aggressively towards their mirror image suggesting that they consider it another individual, whereas in some mammals behavioural response to mirrors may be an evidence of self-recognition. Since fish cannot self-recognize, we asked whether they could distinguish between fighting a mirror image and fighting a real fish. We compared molecular, physiological and behavioural responses in each condition and found large differences in brain gene expression levels. Although neither levels of aggressive behaviour nor circulating androgens differed between these conditions, males fighting a mirror image had higher immediate early gene (IEG) expression in brain areas homologous to the amygdala and hippocampus than controls. Since amygdalar responses are associated with fear and fear conditioning in other species, higher levels of brain activation when fighting a mirror suggest fish experience fear in response to fights with a mirror image. Clearly, the fish recognize something unusual about the mirror image and the differential brain response may reflect a cognitive distinction.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.336
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), 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

Citations151
Published2010
Admission routes1
Has abstractyes

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