Learning motor actions via imagery—perceptual or motor learning?
Bibliographic record
Abstract
It is well accepted that repeatedly imagining oneself acting without any overt behavior can lead to learning. The prominent theory accounting for why imagery practice is effective, motor simulation theory, posits that imagined action and overt action are functionally equivalent, the exception being activation of the end effector. If, as motor simulation theory states, one can compile the goal, plan, motor program and outcome of an action during imagined action similar to overt action, then learning of novel skills via imagery should proceed in a manner equivalent to that of overt action. While the evidence on motor simulation theory is both plentiful and diverse, it does not explicitly account for differences in neural and behavioural findings between imagined and overt action. In this position paper, we briefly review theoretical accounts to date and present a perceptual-cognitive theory that accounts for often observed outcomes of imagery practice. We suggest that learning by way of imagery reflects perceptual-cognitive scaffolding, and that this 'perceptual' learning transfers into 'motor' learning (or not) depending on various factors. Based on this theory, we characterize consistently reported learning effects that occur with imagery practice, against the background of well-known physical practice effects and show that perceptual-cognitive scaffolding is well-suited to explain what is being learnt during imagery practice.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".