Learning performance is influenced by the social environment in cichlid fishes.
Bibliographic record
Abstract
It has been hypothesised that some specialised cognitive abilities may have evolved because of the challenges of living in complex social environments. Therefore, more-social species might be able to learn faster than less-social species. The aim of this study was to develop a learning framework to test how more- and less-social Lamprologine cichlid fishes perform across associative learning tasks. These cichlids are a group of closely related species with similar ecologies and life histories but varying degrees of sociality, making them an ideal group for comparative learning studies. We found that three nongrouping cichlids (Telmatochromis temporalis, Lamprologus meleagris, and Neolamprologus tretocephalus) outperformed three closely related highly social, cooperatively breeding cichlids (N. pulcher, N. multifasciatus, and Julidochromis dickfeldi) on an associative learning task based on food rewards. However, we hypothesised that these differences may be caused by the social environment during testing and might not reflect true cognitive differences. Indeed, when we drilled down and compared just two species across four different social conditions, we found that the social environment during learning trials affected the performance of the highly social N. pulcher and the less-social T. temporalis differently. We then performed further experiments with both N. pulcher and T. temporalis under more natural social settings. Under these more natural social conditions, we found that N. pulcher learned to differentiate accessible and inaccessible shelters faster than T. temporalis. These findings highlight the potential for expanding comparative experiments investigating the relationship between sociality and cognition and emphasise the crucial role social environment plays in learning outcomes. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".