“I Like the Way You Move”: Validating the Use of Point-Light Display Animations in Virtual Reality as a Methodology for Manipulating Levels of Sexualization in the Study of Sexual Objectification
Bibliographic record
Abstract
Sexual objectification of others has seen a growing research interest in recent years. While promising, the field lacks standardized stimuli, resulting in a confusion between sexualization and sexual objectification, which limits the interpretability of published results. In this study, we propose to use point-light display (PLD) as a novel methodology for manipulating sexualization levels as a first step toward isolating movement from other visual cues (e.g., clothing or physical appearance) for studying effects of sexual objectification of others. To do so, we first developed 8 virtual reality animations varying on 3 dimensions: 1) nature of movement (dance vs. walk), 2) level of sexualization (low vs. high), and 3) animation speed (slow and fast). Then, we validated these stimuli with perception ratings from 211 participants via an online survey. Using mixed linear regression models, we found evidence that our manipulation was successful: while participants took longer, were less accurate, and less confident in their response when confronted with a dancing, sexualized PLD, they also rated it as significantly more sexualized. This latter effect was stronger for participants perceiving a woman dancing compared to participants who perceived other genders. Overall, participants who reported more frequent sexual objectification behaviors also perceived the animations as more sexualized. Taken together, these results suggest that sexual suggestiveness can be manipulated by rather simple movement cues, thus validating the use of PLD as a stepping stone to systematically study processes of sexual objectification. From there, it is now possible to manipulate other variables more precisely during immersions in virtual reality, whether by adding a skin to the animated skeleton, by situating the PLD into different context, by varying the amplitude and the nature of the movements, or by modifying the context of the virtual environment.
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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 | 0.007 | 0.026 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".