Vision facilitates tactile perception when grasping an object
Bibliographic record
Abstract
Tactile sensitivity measured on the hand is significantly decreased for a moving (MH), as opposed to a resting hand (RH). This process (i.e., tactile suppression) is affected by the availability of visual information during goal-directed action. However, the timing of the contribution of visual information is currently unclear for reach-to-grasp movements, especially in the period before the digits land on the object to grasp it. Here participants reached for, grasped, and lifted an object placed in front of them in conditions of full/limited vision. Tactile perception was assessed by measures of signal detection theory (d' & c'). Electro-cutaneous stimulation could be delivered/not at the MH/RH, either during movement preparation, execution, before grasping, or while lifting the object. Results confirm tactile gating at the MH. This result is accompanied by a significant conservative criterion shift at the MH for the latter movement stages. Importantly, visual information enhances MH sensitivity just before grasping the object, but also improves RH sensitivity, during object lift. These findings reveal that tactile suppression is shaped by visual inputs at critical action stages. Further, they indicate that such a time-dependent modulation from vision to touch extends beyond the MH, suggesting a dynamic monitoring of the grasp space.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".