Automatic imitation does not predict levels of prosocial behaviour in a modified dictator game
Bibliographic record
Abstract
Automatic imitation refers to the automatic tendency to imitate observed actions. Previous research on automatic imitation has linked it to a wide variety of social cognitive processes and functions, although the evidence is mixed and suggestive. However, no study to date has looked at the downstream behavioural effects of automatic imitation. The current research addresses this gap in the literature by exploring the possible relationship between trait-levels of automatic imitation, as measured by the automatic imitation task (AIT), and explicit prosocial behaviours, as measured by a modified dictator game (DG). Contrary to our expectations, AIT effects did not correlate with DG scores. This conclusion is supported by both equivalence tests and Bayesian analysis. However, we discuss a number of alternative explanations for our results, and caution against strong interpretations from a single study. We further discuss the implications of this finding in relation to the widespread notion that automatic imitation, and self-other control more generally, underlie social cognitive functions.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".