Intensity contrast drives background choice in cephalopods
Bibliographic record
Abstract
For camouflage to be effective, animals must integrate their phenotype into the environment, with background selection providing a behavioural means of doing this. At present, there is limited knowledge of what cues animals capable of dynamic colour change attend to when selecting backgrounds. Recent empirical data show that a predator’s search task is more challenging in visually complex environments, suggesting that animals capable of matching many backgrounds through adaptive colour change may use visual complexity to govern background choices. We designed a binary choice paradigm to assess whether three species of cephalopod (Sepia plangon, Sepioteuthis lessoniana, Euprymna tasmanica) prefer more visually complex environments, with complexity quantified in terms of intensity contrast. Tracking data revealed a consistent preference for the high complexity background in S. lessoniana and E. tasmanica, with a similar but weaker trend in S. plangon. Granularity analysis showed that this preference was not explained by the ability to better match one background over the other, supporting the interpretation that cephalopods were selecting for visual complexity itself. This suggests that background complexity, by means of the range of intensity contrast, may be an important cue guiding background selection in animals capable of adaptive camouflage.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".