Planning and Online Movement Guidance to visual and non-visual target locations
Bibliographic record
Abstract
Prior work has explored the spatial and temporal characteristics of adjusting movements to visual tactile and somatosensory target positions. This experiment aimed to explore movement corrections across all three modalities. Twelve participants made reaching movements to an LED (visual target), a brush touching the non-reaching finger (tactile target), or the non-reaching finger (somatosensory target). On some trials the target was displaced either 3-cm away or toward the participant, prior to, or 200 ms following movement onset. Participants were instructed to adjust their trajectories towards the new target location. Overall, participants exhibited a larger magnitude of correction to somatosensory target perturbations, followed by tactile target perturbations. Participants also exhibited shorter correction latencies in the somatosensory than the vision and tactile conditions, with no differences between the vision and tactile conditions. These findings support previous work showing that moving the target hand (i.e., somatosensory target) yields earlier and larger corrections than moving to a visual or tactile target. This work provides evidence that corrections to non-visual targets may be different depending on the sensory modality used to detect changes in target location.
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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".