Two-point touch discrimination depends on the perceived length of the arm
Bibliographic record
Abstract
Two-point discrimination threshold depends on the number and size of receptive fields between the touches. But what determines the size of the receptive fields? Are they anatomically fixed? Or are they related to perceived body size? To answer this question we manipulated perceived arm length using the Pinocchio illusion. The test arm was held at the wrist and the holding arm was made to feel perceptually more extended than it was by applying vibration to the tendon of the biceps (cf. de Vignemont et al ., 2005). For control trials the holding arm was vibrated elsewhere. An array of tactors, separated by 3 cm, was placed on the upper surface of the arm and covered with a cloth. Vibro-tactile stimulation was applied to either one or two tactors in two periods. Subjects identified which period contained two stimuli. A psychometric function was drawn through the probability of correct response as a function of tactor separation to determine the threshold distance. In a separate experiment, subjects estimated the perceived location of each tactor against a scale laid on top of the cloth. The estimated locations of the tactors on the tested arm were displaced by tendon vibration of the holding arm compatible with a perceptual lengthening of the arm. The threshold for two-touch discrimination was significantly increased from 4.5 (±0.6) cm with no tendon stimulation to 5.7 (±0.5) cm when the arm was perceptually extended. We conclude that two-point touch discrimination depends on the size of central receptive fields that become larger when the arm is perceptually lengthened.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".