A visual perceptual sweet spot for endpoint accuracy judgments during slower actions
Bibliographic record
Abstract
When performing rapid voluntary actions (i.e., < 375 ms), brief visual samples provided at approximately 1.0 m/s, prior to peak limb velocity (PV), can yield more accurate endpoint accuracy judgments and online corrections than when provided earlier or later during the limb trajectory (Tremblay et al., 2017). However, it is not known if the optimal window to gather online visual feedback extends to slower actions (cf. sweet spot due to a limited opportunity to utilize online vision during rapid actions). The current study tested for the presence of a perceptual visual sweet spot for slower movements (i.e., 550-650 ms). One of three visual windows of 20 ms was provided when real-time limb velocity reached 0.3, 0.54, or 0.7 m/s, before PV, which were intended to corresponded to comparable proportions of PV than in Tremblay et al. (2017: i.e., approx. 30, 60 and 80% of PV). The results of a forced-choice endpoint judgment task (i.e., undershoot or overshoot) that followed each trial were contrasted with the actual endpoint locations. Using a correlational rank analysis, 55.56% of the participants exhibited their best endpoint bias judgments in the 0.3 m/s (cf. 22.22% participants in each 0.54 and 0.7 m/s window). The results provided some evidence for the generalizability of the sweet spot for slower movements. Also, gathering online visual feedback may take place at an optimal velocity prior to PV (i.e., 30% of PV) during slower movements, at least when making predictions about endpoint accuracy.Acknowledgments: Natural Sciences and Engineering Research Council of Canada (NSERC), University of Toronto (UofT), Canada Foundation for Innovation (CFI), Ontario Research Fund (ORF).
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".