Gaze‐down endoscopic practise leads to better novice performance on gaze‐up displays
Bibliographic record
Abstract
OBJECTIVES: It is well known that precision skills are best learned when they are practised in the sensorimotor context that is present when performance is most important. However, a particular skill may vary with respect to the sensorimotor context in which it is performed. Certain sensorimotor variations can make a task more or less complex than others. Recent accounts of skill learning describe how task difficulty can be manipulated to provide optimised challenges to progress learners beyond their current level of expertise. This study tests the idea that simplified practise contexts lead novice learners to acquire skill proficiency that is more generalisable to new contexts. METHODS: We present a learning experiment in which the performances of novices who acquired a set level of proficiency in the endoscopic pots-and-beans task through performance-based practise using a gaze-up endoscopic monitor arrangement were compared against the performances of novices who acquired an equivalent level of proficiency using a simplified gaze-down arrangement. Participants returned after 1 week for retention and transfer testing. RESULTS: Time and accuracy analyses revealed that participants in both training groups improved significantly over the practise protocol and maintained this performance after a period of retention. However, the comparisons of the visual display transfer performances (i.e. on the gaze-up arrangement) of the gaze-down trainees against the retention performances (i.e. also on the gaze-up arrangement) of their gaze-up counterparts and vice versa revealed that gaze-down trainees made fewer errors in both performance contexts (F(1,16) = 7.97, p = 0.01 and F(1,16) = 57.05, p = 0.04, respectively). CONCLUSIONS: These findings highlight the benefits associated with using simplified sensorimotor practise contexts for novice learners. Beginners will learn best from simplified practise because it allows them to develop good movement strategies for dealing with potential error without being overwhelmed by task complexity.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".