Colour biases in learned foraging preferences in Trinidadian guppies
Bibliographic record
Abstract
Abstract Learning allows animals to adaptively adjust their behaviour in response to variable but predictable environments. Stable aspects of the environment may result in evolved or developmental biases in the systems impacting learning, allowing for improved learning performance according to local ecological conditions. Guppies ( Poecilia reticulata ), like many animals, show striking colour preferences in foraging and mating contexts and guppy artificial selection experiments have found that the form and progress of evolved responses to coloured stimuli differ depending on stimulus colour. Blue colouration is thought to typically be a relatively unimportant food cue in guppies. This raises the possibility that learned foraging associations with blue objects are formed less readily than with other colours. Here, guppies were rewarded for foraging at green or blue objects in two experiments. Guppies readily foraged from these objects, but learning performance differed with rewarded object colour. With equal amounts of training, the preference for green objects became stronger than the preference for blue objects. These differences in performance were not attributable to differences in initial preferences or to foraging more on one colour during training. These findings suggest that associative pairings within a single sensory modality that do not have a historic relevancy can be more difficult for animals to learn even when there is no clear initial bias present.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".