The availability of vision and tactile gating in a reaching and grasping task
Bibliographic record
Abstract
A multitude of events bombard our sensory systems at every moment of our lives. Thus, it is important for the sensory cortex to gate unimportant events. Tactile suppression is a well-known phenomenon defined as a reduced ability to detect tactile events on the skin before and during movement. Previous experiments (Buckingham et al., 2010; Colino et al., 2014) found detection rates decrease just prior to and during finger abduction, and decrease according to the proximity of the moving effector. However, what effect does vision have on tactile gating? There is ample evidence (see Serino & Haggard, 2010) observing increased tactile acuity when participants see their limbs. The present study examined how tactile detection changes in response to visual condition (vision/no-vision). Ten human participants used their right hand to reach and grasp a cylinder. Tactors were attached to the index finger and the forearm of both the right and left arm and vibrated at various epochs relative to a “go” tone. Results replicate previous findings from our laboratory (Colino et al., 2014). Also, tactile acuity decreased when participants did not have vision. These results indicate that the vision affects the somatosensation via inputs from parietal areas (Konen & Haggard, 2014) but does so in a reach-to-grasp context.Acknowledgments: NSERC, CFI
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".