Transfer of tactile perceptual learning to untrained neighboring fingers reflects natural use relationships
Bibliographic record
Abstract
Tactile learning transfers from trained to untrained fingers in a pattern that reflects overlap between the representations of fingers in the somatosensory system (e.g., neurons with multifinger receptive fields). While physical proximity on the body is known to determine the topography of somatosensory representations, tactile coactivation is also an established organizing principle of somatosensory topography. In this study we investigated whether tactile coactivation, induced by habitual inter-finger cooperative use (use pattern), shapes inter-finger overlap. To this end, we used psychophysics to compare the transfer of tactile learning from the middle finger to its adjacent fingers. This allowed us to compare transfer to two fingers that are both physically and cortically adjacent to the middle finger but have differing use patterns. Specifically, the middle finger is used more frequently with the ring than with the index finger. We predicted this should lead to greater representational overlap between the former than the latter pair. Furthermore, this difference in overlap should be reflected in differential learning transfer from the middle to index vs. ring fingers. Subsequently, we predicted temporary learning-related changes in the middle finger's representation (e.g., cortical magnification) would cause transient interference in perceptual thresholds of the ring, but not the index, finger. Supporting this, longitudinal analysis revealed a divergence where learning transfer was fast to the index finger but relatively delayed to the ring finger. Our results support the theory that tactile coactivation patterns between digits affect their topographic relationships. Our findings emphasize how action shapes perception and somatosensory organization.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".