Time Course of Tactile Gating in a Reach-to-Grasp and Lift Task
Bibliographic record
Abstract
Humans' sensory systems are bombarded by myriad events every moment of our lives. Thus, it is crucial for sensory systems to choose and process critical sensory events deemed important for a given task and, indeed, those that affect survival. Tactile gating is well known, and defined as a reduced ability to detect and discriminate tactile events before and during movement. Also, different locations of the effector exhibit different magnitudes of sensitivity changes. The authors examined that time course of tactile gating in a reaching and grasping movement to characterize its behavior. Tactile stimulators were attached to the right and left mid-forearms and the right index finger and fifth digit. When participants performed reach-to-grasp and lift targets, tactile acuity decreased at the right forearm before movement onset (F. L. Colino, G. Buckingham, D. T. Cheng, P. van Donkelaar, & G. Binsted, 2014 ). However, tactile sensitivity at the right index finger decreased by nearly 20% contrary to expectations. This result reflecting that there may be an additional source acting to reduce inhibition related to tactile gating. Additionally, sensitivity improved as movement end approached. Collectively, the present results indicate that predictive and postdictive mechanisms strongly influence tactile gating.
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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 | 0.003 |
| 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 | 0.001 |
| 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".