Objective Measurement of Tactile Mislocalization
Bibliographic record
Abstract
Stimulating the skin with intensities close to the sensory threshold causes erroneous localization of the site of stimulation. Previous studies using manual methods for applying faint tactile stimuli have shown that localization errors obey a somatotopic principle in which tactile stimuli are preferentially mislocalized to sites adjacent to the stimulated skin region. However, manual testing of mislocalization is time consuming and only partially objective because results depend on the skills of the tester. To improve the testing procedure, an automated apparatus was developed. The procedure adjusted stimulus intensity adaptively during testing to remain near the individual subject's sensory threshold, so that mislocalizations occurred often enough to assess somatotopic organization. The new method was applied to 12 healthy subjects. In each subject, the five digits of the right hand were stimulated singly in random order. Localization errors were distributed preferentially to fingers close to the stimulated finger rather than to distant fingers. The profile of mislocalization differed significantly from that expected on the basis of response bias or guessing behavior. The present results replicate previous findings obtained for manual testing with improved sensitivity and indicate that the new technique is a useful tool for the study of somatosensory processing on a perceptual level.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".