Body Image and Anti-Fat Attitudes: An Experimental Study Using a Haptic Virtual Reality Environment to Replicate Human Touch
Bibliographic record
Abstract
It is well documented that anti-fat attitudes influence the interactions individuals have with overweight people. However, testing attitudes through self-report measures is challenging. In the present study, we explore the use of a haptic virtual reality environment to physically interact with overweight virtual human (VH). We verify the hypothesis that duration and strength of virtual touch vary according to the characteristics of VH in ways similar to those encountered from interaction with real people in anti-fat attitude studies. A group of 61 participants were randomly assigned to one of the experimental conditions involving giving a virtual hug to a female or a male VH of either normal or overweight. We found significant associations between body image satisfaction and anti-fat attitudes and sex differences on these measures. We also found a significant interaction effect of the sex of the participants, sex of the VH, and the body size of the VH. Female participants hugged longer the overweight female VH than overweight male VH. Male participants hugged longer the normal-weight VH than the overweight VH. We conclude that virtual touch is a promising method of measuring attitudes, emotion and social interactions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".