Integration of the Senses of Vision and Touch in Perceiving Object Softness
Bibliographic record
Abstract
Based upon virtual reality (VR) technologies, a challenging issue for surgical planning is to permit surgeons intuitive and accurate interaction using their sense of vision and touch (e.g. to distinguish the softness of tissues). Since a viewing angle (VA) can influence apparent visual deformation of objects, we hypothesize that the VA would affect the perception of object softness. We conducted an experiment to test this hypothesis and to investigate the mechanisms of integrating the senses of vision and touch. Using a desktop VR setup, we tested 15 human participants for perceiving object softness under 3 conditions: (a) both visual and touch (haptic) information available; (b) only haptic information available; and (c) only visual information available. In each trial, participants had to select the harder object among two deformable balls of same softness but placed in different VAs. Our results showed that the VA affected the perception of object softness (within-subject ANOVA; F = 8.62, p < 0.001) -the larger the VA was the harder the ball was perceived. When two VAs differed at least 15deg, we found a significant difference in perceiving object softness (post-hoc Tukey test, p < 0.05). We applied the method of maximum likelihood estimate to compute the individual and combined weights of visual and haptic information during perceiving object softness. The computation revealed that the visual information was predominant when the VA was at -15deg, whereas the haptic information was prevailing when the VA was +15deg. We also discovered that the variance of both visual and haptic information lay between the individual variances of visual and haptic information, indicating the dependency between visual and haptic information. In conclusion, the VA should never be greater than 15deg to eliminate perceptual illusions. The visual and haptic information depends upon each other, disapproving the assumption of independency in early studies.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".