Individual consistency and flexibility in human social information use
Bibliographic record
Abstract
Copying others appears to be a cost-effective way of obtaining adaptive information, particularly when flexibly employed. However, adult humans differ considerably in their propensity to use information from others, even when this 'social information' is beneficial, raising the possibility that stable individual differences constrain flexibility in social information use. We used two dissimilar decision-making computer games to investigate whether individuals flexibly adjusted their use of social information to current conditions or whether they valued social information similarly in both games. Participants also completed established personality questionnaires. We found that participants demonstrated considerable flexibility, adjusting social information use to current conditions. In particular, individuals employed a 'copy-when-uncertain' social learning strategy, supporting a core, but untested, assumption of influential theoretical models of cultural transmission. Moreover, participants adjusted the amount invested in their decision based on the perceived reliability of personally gathered information combined with the available social information. However, despite this strategic flexibility, participants also exhibited consistent individual differences in their propensities to use and value social information. Moreover, individuals who favoured social information self-reported as more collectivist than others. We discuss the implications of our results for social information use and cultural transmission.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".