Processing Of Static Visuospatial Information For Direct And Indirect Reaching Movements
Bibliographic record
Abstract
The multljointklnematlcs of dkectreachlng to a target and indirectreaching movements around obstacles to a target wre studled in ml and Parkinsonian humans.The reaching task was desQned so that the coordinates dthe target and the obstacles we acquired simultaneously with the cue to move. Indirect movements required an average of40 msec longer km cue to movement Initiation than direct movements which may represent the increased processing necessary to plan a trajectay appropriate to avoid the obstacles. Although saccadic eye movements from a cenlral fixation point to the target alwys occurred concurrently with thereachingmovement,visual~veationofthetargetorthe obstacles was notrequired forthe the reaching movements. The movements had single velocity maxima which decreased by 11% on average for indirect reaching relative to direct reaching. als performed highly reproducible trajectories for a given targebbsbcle confiuatin and were able to execute variable hpctories for dflerent configurations. Parklnsonians demonslrated a comparatively limited subset of trajectories In a manner suggesting an inabllky to abequakly integrate visuospatial and kinemati sensory information.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".