A Physical Constraint on Perceptual Learning: Tactile Spatial Acuity Improves with Training to a Limit Set by Finger Size
Bibliographic record
Abstract
In touch as in vision, perceptual acuity improves with training to an extent that differs greatly across people; even individuals with similar initial acuity may undergo markedly different improvement with training. What accounts for this variability in perceptual learning? We hypothesized that a simple physical characteristic, fingertip surface area, might constrain tactile learning, because previous research suggests that larger fingers have more widely spaced mechanoreceptors. To test our hypothesis, we trained 10 human participants intensively on a tactile spatial acuity task. During 4 d, participants completed 1900 training trials (38 50-trial blocks) in which they discriminated the orientation of square-wave gratings pressed onto the stationary index or ring finger, with auditory feedback provided to signal correct and incorrect responses. We progressively increased task difficulty by shifting to thinner groove widths whenever participants achieved ≥90% correct block performance. We took optical scans to measure surface area from the distal interphalangeal crease to the tip of the finger. Participants' acuity improved markedly on the trained finger and to a lesser extent on the untrained finger. Crucially, we found that participants' tactile spatial acuity improved toward a theoretical optimum set by their finger size; participants with worse initial performance relative to their finger size improved more with training, and posttraining performance was better correlated than pretraining performance with finger size. These results strongly support the hypothesis that tactile perceptual learning is limited by finger size. We suspect that analogous physical constraints on perceptual learning will be found in other sensory modalities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".