Attraction to and learning from social cues in fruitfly larvae
Bibliographic record
Abstract
We examined the use of social information in fruitfly larvae, which represent an ideal model system owing to their robust learning abilities, small number of neurons and well-studied neurogenetics. Focal larvae showed attraction to the distinct odour emanating from food occupied by other larvae. In controlled learning experiments, focal larvae preferred novel odours previously paired with food occupied by other larvae over novel odours previously paired with unoccupied food. When we gave groups of larvae a choice between food patches differing in quality, more larvae aggregated on the higher-quality food, suggesting that attraction to and learning about cues associated with other larvae can be beneficial. Furthermore, larvae were more likely to find the best available food patch in trials when that food patch was occupied by other larvae than in trials when that food patch was unoccupied. Our data suggest, however, that the benefits from joining others may be at least partially offset by the fitness costs of increased competition, because larvae reared in isolation did as well as or better than larvae reared in groups on three key fitness parameters: developmental rate, survival rate and adult dry body mass. Our work establishes fruitfly larvae as a highly tractable model species for further research on the mechanisms that modulate behaviour and learning in a social context.
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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".