Persuasive companions can be wrong: the use of misleading social information in nutmeg mannikins
Bibliographic record
Abstract
Animals sample their surrounding environment to collect information, which can be obtained personally or by tracking the behavior of others (i.e., social information). Although social information appears to be generally advantageous, it can also be detrimental and may even conflict with personal information. We tested the effect that the strength of social information, and ultimately its persuasiveness, can have on an animal’s decision to use it or not by conducting an experiment using single nutmeg mannikins (Lonchura punctulata), which were offered a foraging choice after observation of videos of feeding or nonfeeding conspecifics. The persuasiveness of social information was amplified by increasing the number and changing the behavior of conspecifics that had previously been seen feeding at 1 of 2 feeders. In addition, we modulated the certainty of an individual’s personal information. Some birds had prior experience of a marked feeder always containing easily accessible food, whereas other birds experienced that this was only the case in half of the trials. Our results show that animals provided with sufficiently persuasive social information will tend to reduce the weight of even highly reliable personal information. This provides the first experimental evidence consistent with the propagation of informational cascades in nonhuman animals, which have been invoked to explain market crashes in economics or panic rushes in human crowds.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".