Automatic Imitation and the Correspondence Problem of Imitation: A Brief Historical Overview of Theoretical Positions
Bibliographic record
Abstract
Abstract The main aim of the current chapter is to situate automatic imitation in the broader historical context of research on the functional mechanisms underlying imitation. Intuitively, imitation is a very simple act: you do what you see. However, on closer inspection, the question arises as to how a perceptual representation of a movement can be transformed into a corresponding motor program (the so-called correspondence problem of imitation). Research on the correspondence problem has a long history in psychology. Three major theoretical approaches to solve the problem have been proposed: first, imitation has been conceptualized as an innate mechanism; second, from a learning theoretical perspective, imitation has been understood as the result of simple learning mechanism, equating imitation with any other learned behaviour. Finally, ideomotor theory has conceptualized imitation as the result of ideomotor learning distinguishing it from other learned behaviour. While automatic imitation was originally developed in the context of ideomotor theory, it has also been used to support learning theoretical approaches to the correspondence problem. Almost 25 years of research on automatic imitation have not solved the correspondence problem but have provided cognitive psychology with a valuable tool to experimentally investigate imitative behaviour and to bridge the gap between cognitive and social-psychological approaches to imitation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".