I see your false colours: how artificial stimuli appear to different animal viewers
Bibliographic record
Abstract
The use of artificially coloured stimuli, especially to test hypotheses about sexual selection and anti-predator defence, has been common in behavioural ecology since the pioneering work of Tinbergen. To investigate the effects of colour on animal behaviour, many researchers use paints, markers and dyes to modify existing colours or to add colour to synthetic models. Because colour perception varies widely across species, it is critical to account for the signal receiver's vision when performing colour manipulations. To explore this, we applied 26 typical coloration products to different types of avian feathers. Next, we measured the artificially coloured feathers using two complementary techniques-spectrophotometry and digital ultraviolet--visible photography-and modelled their appearance to mammalian dichromats (ferret, dog), trichromats (honeybee, human) and avian tetrachromats (hummingbird, blue tit). Overall, artificial colours can have dramatic and sometimes unexpected effects on the reflectance properties of feathers, often differing based on feather type. The degree to which an artificial colour differs from the original colour greatly depends on an animal's visual system. 'White' paint to a human is not 'white' to a honeybee or blue tit. Based on our analysis, we offer practical guidelines for reducing the risk of introducing unintended effects when using artificial colours in behavioural experiments.
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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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".