What is modelled during observational learning?
Bibliographic record
Abstract
In this article, we examine the question of what information is processed during observational learning by evaluating a variety of methods, theories, and empirical data. Initially, we review work involving neuroimaging techniques and infant imitation. We then evaluate data from behavioural experiments involving adults, wherein a variety of attempts have been made to isolate the critical or minimal information constraining the acquisition of coordination. This body of research has included comparisons between video and point-light displays, manipulations to the amount and type of information presented in the display, the collection of point-of-gaze data, and manipulations to the task context in terms of outcome goals. We conclude that observational learning is governed by specific features of the model's action (i.e. motions of the end effector) and the task (i.e. outcome constraints) and, in contrast with traditional theoretical modelling, more global aspects of a model (i.e. the relative motions within and between joints) do not appear to be the primary method for constraining action execution.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".