Active tactile perception modulates motor output and pre planning
Bibliographic record
Abstract
Perceptual decision-making requires sensory detection and cognitive processing. Subsequent motor response preparation and execution reflect the decision made (Hegner, Lindner & Braun, 2015). Active touch yields tactile information through mechanical deformation of the skin, and is the conscious surface exploration of to-be-detected stimuli (Gibson, 1962). Perceptual decision-making via active touch is a skill practiced by clinicians who deliver manual therapies. Tactile features are extracted from the patient, and perceived by the clinician. A decision is made, and the clinician prepares a motor response that is executed with therapeutic intent. The purpose of this study was to determine how tactile perception influences manual therapist motor output. Experienced clinicians (N=10), in a within-participants design, palpated four low-fidelity models pressurized to 10, 15, 20 and 25psi respectively. Participants used tactile perception to prepare and deliver a spinal manipulative (SM) thrust motor response 12 times per model, yielding 48 total trials in a randomized order. Signals acquired from a force sensitive load cell and triaxial accelerometer were synchronized with a 3D motion analysis system and recorded for 5s at 300Hz. Dependent variables included preload force, thrust force, resultant displacement and resultant peak acceleration. Analysis of dependent measures utilized one-way repeated measures ANOVA models. Significant findings were compared using Tukey's HSD. We found that as model pressure increased preload force increased, while displacement and peak acceleration of the SM thrust hand decreased. In conclusion, manual therapists rely on active tactile perception to modulate appropriate regional contact tension, and for pre planning their motor output.Acknowledgments: Funding for this project was provided by a Manitoba Health Research Council (now Research Manitoba) establishment grant.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".