Tactile suppression during goal-directed action
Bibliographic record
Abstract
A multitude of sensory events bombard our sensory systems at every moment of our lives. Thus, it is important for the sensory cortex to gate unimportant sensory events. Similarly, tactile suppression is a well-known phenomenon (Rushton et al., 1981; Buckingham et al., 2010). Tactile gating is a reduced ability to detect tactile events on the skin before and during movement. Previous experiments (Chapman et al., 1987) found detection rates decrease just before and during finger abduction and decrease according to the proximity of the moving effector. The present study examined the changes in tactile detection that occur during a reach and grasp. Participants were recruited (n=14) to perform reach and grasp movements to a cylinder (2.5 cm) that randomly changed location. Custom-built micro-motors were taped to the dorsal surfaces of the proximal phalanges of the index finger, the fifth digit and to the forearm. This arrangement was repeated on the left limb. A motor vibrated per trial relative to a "go" tone. The left limb remained at rest. Detection rates at the right fifth digit and forearm decrease dramatically before movement onset (no reduction at the index finger). These results indicate that the task affects gating dynamics (Williams & Chapman, 2002). Importantly, the CNS is able to modify input gating independently at multiple sites and does so before movement onset. Therefore, this indicates feed-forward mechanisms at work in sensorimotor networks.Acknowledgments: BCKDF, CFI, NSERC
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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".