Context-dependent consequences of color biases in a social fish
Bibliographic record
Abstract
Abstract Colorful visual signals can provide receivers with valuable information about food, danger, and the quality of social partners. However, the value of the information that color provides varies depending on the situation, and color may even act as a sensory trap where signals that evolved under one context are exploited in another. Despite some elegant early work on color as a sensory trap, few empirical studies have examined how color biases may vary depending on context and under which situations biases can be overridden. Here, using Neolamprologus pulcher, a highly social cichlid fish from Lake Tanganyika, we conducted a series of experiments to determine color biases and investigate the effects of these biases under different contexts. We found that N. pulcher interacted the most with yellow items and the least with blue items. These biases were maintained during a foraging-based associative learning assay, with fish trained using yellow stimuli performing better than those trained using blue stimuli. However, these differences in learning performance did not extend to reversal learning; fish were equally capable of forming new associations regardless of the color they were initially trained on. Finally, in a social choice assay, N. pulcher did not display a stronger preference for conspecifics whose yellow facial markings had been artificially enhanced. Together, these findings suggest that the influence of color biases varies under different contexts and supports the situational dependency of color functions.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".