Reach endpoints do not vary with starting position and movement path of the proprioceptive target
Bibliographic record
Abstract
Does varying the start location of the left hand affect reaches to the felt (proprioceptive) or felt and seen (visual-proprioceptive) left hand? A robot manipulandum guided the left hand (actively) from one of 6 (start) sites to one of 5 remaining (target) sites. Participants reached with their right hand to the current felt or felt and seen (visible for 1 sec) location of the left hand or to remembered visual targets. Participants were fairly accurate and precise when localizing the left hand, although less so than for visual targets (Mean horizontal error = 0.88cm, SD = 0.87cm; mean sagittal error = 1.24cm, SD = 0.96cm). Accuracy and precision of reach endpoints varied with target type. In the proprioceptive task, horizontal errors were deviated to the right. In both proprioceptive tasks sagittal errors were deviated towards the body, suggesting that participants felt their left hand to be closer to their body than its actual position. Proprioceptive reaches were also less precise (ellipse area = 3.96cm2) than visual-proprioceptive reaches (ellipse area = 1.39cm2) and visual reaches (ellipse area = 1.75cm2). There was no difference in precision for reaches to visual and visual-proprioceptive targets. These changes in accuracy and precision across target type do not vary with starting position of the left hand-target. We are currently assessing whether visual and proprioceptive information are optimally integrated within this task, and if integration varies with movement path of the hand-target.
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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 | 0.001 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.001 |
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".