Time's up: When is it too late to implement online limb-target regulation processes?
Bibliographic record
Abstract
Elliott et al.'s (2010) multiple processes model has provided a framework for different stages of online visual feedback utilization. Tremblay and colleagues (2013; 2017) assessed visual feedback utilization processes using a target-jump a paradigm, to specifically evaluate limb-target regulation. While results have provided evidence for optimal limb-target regulation early during fast reaching movements (i.e., ~350 ms), they failed to replicate these results for slower movements (i.e., ~700 ms: Crainic et al., 2017). Collectively, these results could indicate that it may be a time to movement end (i.e., approx. 290 ms) that represents a critical criterion to utilize vision for limb-target regulation processes. As such, the current study further investigated limb-target regulation during slower movements (i.e., ~700ms), while a brief window of visual feedback was provided at 0.3 m/s and 0.7 m/s before and after peak velocity. The latter window occurred later than in Crainic et al. (2017) and less than 290 ms before movement end. During the windows, participants either viewed the original target (30 cm) or a target closer to the start position (i.e., 27 cm: target jump). Endpoint accuracy data indicated that participants could use the window of vision to correct for the target jump in all but the last window condition. These results provide further evidence that time from movement end (e.g., Beggs & Howarth, 1972) could better explain visual feedback utilization strategies, than limb velocity (e.g., Tremblay et al., 2013) or other kinematic criterions (e.g., Elliott et al., 2010).Acknowledgments: University of Toronto, Ontario Research Fund (ORF), Canada Foundation for Innovation (CFI), Natural Sciences and Engineering Research Council of Canada (NSERC)
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".