What we imagine learning from watching others: how motor imagery modulates competency perceptions resulting from the repeated observation of a juggling action
Bibliographic record
Abstract
Although motor learning can occur from observing others perform a motor skill (action observation; AO), observers’ confidence in their own ability to perform the skill can be falsely increased compared to their actual ability. This illusion of motor competence (i.e., ‘over-confidence’) may arise because the learner does not gain access to sensory feedback about their own performance – a source of information that can help individuals understand their veridical motor capabilities. Unlike AO, motor imagery (MI; the mental rehearsal of a motor skill) is thought to be linked to an understanding of movement consequences and kinaesthetic information. MI may thus provide the learner with movement-related diagnostic information, leading to greater accuracy in assessing ability. The present study was designed to evaluate the effects of MI when paired with AO in assessments of one’s own motor capabilities in an online observation task. Two groups rated their confidence in performing a juggling task following repeated observations of the action without MI (OBS group; n=45) or with MI following observation (OBS+MI; n=39). As predicted, confidence increased with repeated observation for both groups, yet increased to a greater extent in the OBS relative to the OBS+MI group. The addition of MI appeared to reduce confidence that resulted from repeated AO alone. Data support the hypothesis that AO and MI are separable and that MI allows better access to sensory information than AO. However, further research is required to assess changes in confidence that result from MI alone and motor execution. This registration pertains to a manuscript submission (Psy Res: Submission ID 86305a27-e58a-4d05-b9c4-12a3f639047b).
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".