Skill acquisition processes in a visual occlusion motor task
Bibliographic record
Abstract
Prediction motion tasks (PMT) require the estimation of an object’s temporal and spatial position after its trajectory has been visually occluded (Tresilian, 1995). The purpose of this experiment was to investigate motor learning in PMT. Ten participants (mean age: 21.8 years) responded to a visual display (E-Prime, PST, Pittsburg, PA) using a mouse. The display depicted a ball moving towards a target region with the path being occluded at the mid-point. The goal was for the participant to move the cursor so that the occluded ball and the cursor coincided within the target region. Participants completed five blocks of 30 randomized trials, which included three speeds (fast/medium/slow) with visual feedback after each trial. The difference between the ball and cursor’s location was recorded to calculate the dependent measures (CE/VE). Ten minutes and 60 minutes after completion participants performed 30 randomized trials of the three speeds without feedback. The acquisition data were analyzed using a 3-Speed by 5-Block repeated measures ANOVA. Results for CE showed main effects for Speed and post-hoc test (Tukey HSD, p<0.05) showed slow different than fast/medium. Analysis for Block demonstrated 1 as having greater error than 2-5. Results for VE showed a main effect for Block (1 more variable than 2-5). The 10-minute (Retention 10) and 60-minute (Retention 60) retention data were analyzed along with Acquisition Blocks 1 and 5 using a 3-Speed by 4-Time repeated measures ANOVA. Results for CE revealed main effects for Speed and Time. Post-hoc analyses showed the slow condition with more error than fast/medium and Acquisition 1 different than Retention 10, which was not different than Retention 60. Overall, there was a distinct improvement as a function of time (decreasing CE). These results will be discussed in relation to skill acquisition processes (eg. contextual interference) and its specific implications for PMT.Acknowledgments: We would like to acknowledge the support of the Sport Science Association of Alberta.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".