Investigating the Speed and Accuracy of Human Movement Corrections to Visual, Somatosensory, and Tactile Perturbations: Evidence for Distinct Sensorimotor Processes
Bibliographic record
Abstract
Humans can adapt their movements in response to expected and unexpected perturbations. The speed and accuracy of these movement corrections may depend on the type of sensory information driving the perception of these perturbations. While previous research has indicated that corrections based on somatosensory information, comprised of proprioceptive and tactile inputs, are faster than corrections based on visual information, other studies have found comparable correction speeds in response to visual and tactile inputs. The purpose of this study was to systematically investigate the latencies (how fast) and magnitudes (how large) of movement corrections in response to perturbations of external visual targets, as well as somatosensory (proprioceptive and tactile) and tactile targets on the non-reaching limb. Participants performed reaching movements to a light-emitting diode (i.e., visual target), the felt position of a brush touching the index finger of the non-reaching hand (i.e., a tactile target), and the index finger of their non-reaching hand (somatosensory target). During some trials, the target was displaced 3 cm away or toward the participant either before or after the movement onset. Participants demonstrated faster and larger corrections to somatosensory target perturbations than to visual or tactile target perturbations. However, corrections to visual targets were more accurate than corrections to tactile targets. These findings support the hypothesis that distinct sensorimotor processes may underlie the adjustments made in response to somatosensory information versus those made in response to visual and tactile 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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".