Using spatial occlusion to explore the control strategies used in rapid interceptive actions: Predictive or prospective control?
Bibliographic record
Abstract
Interceptive actions require individuals to time their movements with an external event. To meet the intense spatial-temporal demands needed for successful interception, a tight coupling and coordination between perceptual and motor processes is required. The control strategy that underlies successful performance is a matter of debate. On the one hand, a predictive control strategy assumes that advanced information is used for response selection and the movement is carried out faithfully without modification. In contrast, a prospective control strategy assumes that the movement response is continuously specified through to the point of interception. Using the rapid interceptive timing task of ice hockey goaltending, we explored the effects of progressively removing predictive visual information from the shooter on the gaze behaviours and motor responses of elite goaltenders. Results showed that the goaltenders used a prospective reversal strategy on 18 of 79 glove trials (22.8% of glove saves; 4.5% of total shots). When a reversal was used, the goaltenders were more successful (saved 11/18 reversals). The gaze behaviour that corresponded to both of these strategies was the quiet eye, which was the final fixation before the onset of the saving motion. The optimal location and duration of the quiet eye was an important factor for successful interception of the puck.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".