Perception of Simulated Local Shapes Using Active and Passive Touch
Bibliographic record
Abstract
This study reexamined the perceptual equivalence of active and passive touch using a computer-controlled force-feedback device. Nine subjects explored a 6 x 10-cm workspace, with the index finger resting on a mobile flat plate, and experienced simulated Gaussian ridges and troughs (width, 15 mm; amplitude, 0.5 to 4.5 mm). The device simulated shapes by modulating either lateral resistance with no vertical movement or by vertical movement with no lateral forces, as a function of the digit position in the horizontal workspace. The force profiles and displacements recorded during active touch were played back to the stationary finger in the passive condition, ensuring that stimulation conditions were identical. For the passive condition, shapes simulated by vertical displacements of the finger had lower categorization thresholds and higher magnitude estimates compared with those of active touch. In contrast, the results with the lateral force fields showed that with passive touch, subjects recognized that a stimulus was present but were unable to correctly categorize its shape as convex or concave. This result suggests that feedback from the motor command can play an important role in processing sensory inputs during tactile exploration. Finally, subjects were administered a ring-block anesthesia of the digital nerves of the index finger and subsequently retested. Removing skin sensation significantly increased the categorization threshold for the perception of shapes generated by lateral force fields, but not for those generated by displacement fields.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".