Using online visual feedback as a function of limb velocity: A replication using multiple targets
Bibliographic record
Abstract
Building on Elliott et al.'s (2010) Multiple Processes framework, Tremblay et al. (under review) demonstrated that vision provided only when the limb travelled above 0.8 m/s (VHigh) was as effective as full vision (FV) in controlling endpoint precision (i.e., variable error). Also, these FV and VHigh conditions yielded better endpoint precision than no vision (NV) and vision below 0.8 m/s (VLow) conditions, which did not differ. However, one could argue the use of a single target (i.e., one amplitude) did not promote much use of online feedback and explain why participants only used online vision above 0.8 m/s. In this study, ten participants completed aiming movements towards 9 targets (26, 30 and 34 cm amplitudes) under randomly presented vision conditions (FV, VHigh & VLow with a fixed 0.8 m/s cutoff) as well as two sets of blocked no vision (NV) trials. As one could anticipate, increasing movement amplitude yielded longer movement times and higher peak limb velocities. Also, we largely replicated the results of Tremblay et al. as NV and VLow yielded comparable and worse endpoint precision than FV and VHigh. However, there was no interaction between movement amplitude and vision condition (i.e., VHigh yielded better endpoint precision than VLow for all amplitudes despite using a single velocity cutoff). These results indicate that online visual feedback utilization is confined to a finite portion of the trajectory that may be associated with a range of limb velocities.Acknowledgments: Natural Sciences and Engineering Research Council of Canada (NSERC); Canada Foundation for Innovation (CFI); and 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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".