Difference of perceiving object softness during palpation through single-node and multi-node contacts
Bibliographic record
Abstract
Virtual Reality (VR) simulators can offer alternatives for training procedures in the medical field. Most current VR simulators consider single-node contact for interacting with an object to convey displacement and force on a discrete mesh. However, a single-node contact does not closely simulate palpation, which requires a surface made of a multi-node contact to touch a soft object. Thus, we hypothesize that the softness of a deformable object (such as a virtual breast phantom) palpated through a single-node contact would be perceived differently from that of the same phantom palpated through a multi-node contact with various force arrays. We conducted a study to investigate this hypothesis. Using a co-located VR setup that aligns visual and haptic stimuli onto a spatial location, we tested 15 human participants under conditions of both visual and haptic stimuli available and only visual (or haptic) stimulus available. In a trial, each participant palpated and discriminated two virtual breast phantoms of same softness through different contacts with varying force arrays. The results of this study revealed that virtual breast phantoms palpated through a single-node contact were constantly perceived harder than their counterparts palpated through a multi-node contact with varying force arrays, when visual stimuli were available. These results imply a constraint for developing a VR system of training palpation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".