Experienced Social Partners Hinder Learning Performance in Naive Clonal Fish
Bibliographic record
Abstract
Abstract Social learning is widely assumed to enhance individual learning efficiency, particularly when naive observers have access to skilled demonstrators, yet the conditions under which this assumption holds remain poorly understood. Here, we investigated how individual learning is influenced by the skill level of social partners. We predicted that naive individuals would benefit from observing experienced conspecifics, yet found the opposite: trained partners significantly impaired naive individuals’ learning performance, while trained individuals remained unaffected by their partner’s skill level. We conducted experiments in near-identical individuals, using the all-female clonal Amazon molly ( Poecilia formosa ) to test whether these fish can learn an operant foraging task, whether individuals differ consistently in learning ability, and whether partner skill level influences learning performance. Using an operant conditioning paradigm over five days, half of the fish were trained to locate food inside a cylinder, whereas the remaining individuals received food randomly dispersed within their tank. Trained individuals subsequently visited the cylinder more frequently than randomly fed individuals and exhibited consistent individual differences in learning performance. In a second phase, fish were allowed to observe a conspecific while individual training was either continued (for trained individuals) or initiated (for naive individuals). We found that trained individuals did not benefit from the presence of a partner, regardless of the partner’s proficiency, but consistently outperformed naive individuals. In contrast, naive individuals showed reduced learning performance when paired with experienced partners, but not with naive partners. Together, our results indicate that Amazon mollies achieve this foraging task through individual learning and exhibit stable differences in learning ability. Moreover, social learning depends on both the learner’s own skill level and that of the social partner, such that observing an experienced conspecific may, in some cases, impede rather than enhance learning.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".