Action representations in perception, motor control and learning: implications for medical education
Bibliographic record
Abstract
OBJECTIVES: the motor behaviours or 'actions' that provide the basis for precision limb control, including the performance of complex medical procedures, are represented at different levels in the central nervous system. This review focuses on how these representations influence the way people perceive, execute and learn goal-directed movements. PERCEPTION AND ATTENTION: the neural processes associated with paying attention to an object are part and particle of the same processes engaged to physically interact with that object. The automatic way in which specific actions are engaged makes it important that we structure perceptual motor environments in a manner that facilitates goal actions and minimises the likelihood of unwanted actions. MOTOR CONTROL: most actions are organised to optimise speed, accuracy and energy expenditure while avoiding worst-case outcomes. To achieve a good outcome on movements, the performer must have the opportunity to experiment with the way specific actions are executed. Early in the discovery process, errors are necessary if the performer is to determine his or her performance boundaries. motor learning: as learning progresses, representations of action become predictive. For example, if rapid corrective processes are to operate, the performer needs to anticipate sensorimotor consequences of movement. Thus, practice should be specific to the conditions under which actions are performed, and the performer. Although nothing can replace physical practice, complex representations of action can develop by observing both expert performers and learners. In many cases, practice scenarios that include both physical practice and observations of other learners can be the most efficient use of time and resources. CONCLUSIONS: although most of the experiments reviewed here involved laboratory tasks such as rapid aiming and movement sequencing, the majority of the principles apply to motor control and learning in more complex situations. Thus, they should be considered when developing methods to train medical personnel to perform perceptual motor procedures with precision.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".