Editorial: Mental practice: clinical and experimental research in imagery and action observation
Bibliographic record
Abstract
First paragraph: This editorial accompanies 18 articles as part of aFrontiersresearch topic. The aim of this research topic was to clarify the underlying mechanisms involved in mental practice of action, bringing together evidence from a range of disciplines including cognitive neuroscience, experimental neuropsychology, sport and movement science, clinical neuropsychology and clinical neurology. The need to clarify the underlying mechanisms of mental practice is a pressing one. Mental practice of action has been explored in sport psychology for several decades, with the aim to use mental practice to improve sport performance. However, following the discovery of the mirror neuron system (see for example,Rizzolatti and Craighero, 2004), the perspective of mental practice has changed to a rationale based on neuroscience and to research focussed on understanding the neural processes of mental practice. Evidence that the brain simulates action has resulted in a common understanding of “functional equivalence” (Jeannerod, 1994): the idea that thementalrepresentation of an action or percept in the person's mind is the neural “equivalent” to thephysicalaction oractualpercept. This ability to mentally represent action using the motor system allows for action simulation, providing conscious mental rehearsal of movement (imagery), but also allows for a common percept when observing the movements of others. Finally, in recent years, the disciplines of clinical neuropsychology and neurology have begun to use mental rehearsal of action, ormental practice, to produce improvements normally attributed to practicing actual movements.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".