“I wanna be like you”: testing the link between social affiliation and overimitation in infancy
Bibliographic record
Abstract
Introduction One way children learn is through imitation, an important ability to gain new skills and to share cultural knowledge. Overimitation, or the tendency to copy irrelevant actions to achieve a goal, is one specific type of imitation which may be particularly related to social motivations. Various theoretical accounts have been developed to explain this construct, including that one overimitates to affiliate with the demonstrator. However, it is still unclear what mechanisms underlie overimitation and how early it develops. The goal of the current experiment was to examine the emergence of overimitation in infancy and its link with social affiliation and other forms of imitation. Methods We administered to 16- to 21-month-olds an overimitation task adapted for infants, an elicited imitation task, an unfulfilled intentions imitation task, and an in-group preference task, used as a measure of social affiliation. We expected an association between the performance on the different imitation tasks, but a weaker link with overimitation. It was also predicted that performance on the in-group preference task would be more strongly related to the overimitation task than to the other imitation tasks. Results and discussion Results showed a significant association between the elicited and unfulfilled intentions imitation tasks, but no link between overimitation and in-group preference. To our knowledge, this is one of the first experiments to study overimitation in infancy and to attempt to find an association with other forms of imitation and with a separate and direct measure of in-group preference as a proxy for social affiliation.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".