Movement planning of video and of manual aiming movements
Bibliographic record
Abstract
We studied aiming performance of adults for video- and manual aiming tasks when they had visual information about the location of the starting base or when they had not. In video-aiming, foveating the starting base and then the target prior to movement initiation (Foveation) resulted in less aiming bias and variability than when the starting base was not visible (PNV), or visible without the participants foveating it prior to movement initiation (PSV). In manual aiming, Foveation and PSV procedures resulted in identical results but reduced aiming bias and variability in comparison to the PNV procedures. The results indicate that participants had difficulty in transforming the locations of the starting base and of the target when seen on a vertical screen into an appropriate movement trajectory. Successive foveation of the starting base and of the target facilitated this transformation, resulting in direction variability being reduced by more than half in comparison to the PNV and PSV conditions. This suggests that in video-aiming the efference copy of the saccade can be used by the CNS to approximate the hand trajectory in the workspace and/or in joint coordinates (Jouffrais and Boussaoud, 1999). Hand trajectory could be readily available in manual aiming if the target location can be recoded directly in hand-coordinates as recently suggested by Buneo et al. (2002).
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".