Signaling to the Enemy? Body Pattern Expression and Its Response to External Cues During Hunting in the Cuttlefish <i>Sepia officinalis</i> (Cephalopoda)
Bibliographic record
Abstract
Cuttlefish can rapidly alter their appearance by using neurally controlled chromatophore organs. This ability may provide a window into their cognitive capacity. We test whether the changes in body pattern that occur during hunting depend on context. If they do, then it may be possible to use these changes to study cephalopod cognition while the animal is engaged in ecologically relevant tasks. We found consistent individual differences in the tendency of cuttlefish to hunt with the first two arms raised. We also found that cuttlefish usually darken their skin after they seize a prey item. This darkening is observed regardless of the identity of the prey (fish, crab, or shrimp), prey context (buried in sand, in a bare tank, or on top of a rock pile), or the presence of a sudden stimulus. The sudden stimulus was created by presenting an overhead model bird to the cuttlefish. The model induced components of the Deimatic Display, which is a form of antipredator behavior, suggesting that the model was perceived as a potential threat. Passing Cloud displays and the Darkening of the arms were significantly reduced after exposure to the model bird. The effect of a potential predator on body pattern expression during hunting suggests it may be possible to use these changes as a sensitive indicator of ecologically relevant learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".