The role of viewing angle in integrating the senses of vision and touch for perception of object softness
Bibliographic record
Abstract
Enabling surgeons to interact intuitively and accurately with virtual reality (VR)-based environments using their senses of vision and touch (e.g., to distinguish the softness of tissues) is a challenging issue for surgical planning. This paper presents the results of two experiments that were conducted to determine how viewing angle affects the perception of object softness and to investigate the mechanisms for integrating the senses of vision and touch. The two experiments used virtual reality setups with different locations of a haptic device. In each experiment, 15 human subjects were tested in cases where both visual and touch (haptic) information was available and again when only visual or haptic information was available. In each trial, subjects were asked to select the harder object among two deformable balls placed at different viewing angles. The results of both experiments showed that viewing angle affects the perception of object softness: the larger the viewing angle, the harder the ball was perceived to be. When two viewing angles differed by at least 15°, there was a significant difference in perceived object softness. By computing the individual and combined weights of visual and haptic information, it was determined that visual information and haptic information depend upon each other, contradicting the assumption of independence employed in other studies. Comparison of the two experiments revealed that the location of the haptic device also affects the perception of object softness.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".