Body Perception and the Sexualized-Body-Inversion-Hypothesis
Bibliographic record
Abstract
According to Bernard et al.'s (2012) Sexualized Body Inversion Hypothesis (SBIH), female bodies are viewed as objects, and processed as such by the visual system, while male bodies are processed as social objects. Their hypothesis is supported by a greater inversion effect for male than for female body images in a discrimination task. However, there are physical differences between the male and female image sets that might account for the reported differences in the inversion effect. The current study investigated how much of this sex difference can be accounted for by discriminability of images, and whether the SBIH is still supported. We replicated Bernard et al.'s study presenting participants with a target image, followed by a blank screen, then the target image presented alongside its mirror-image as a 2AFC recognition task. We found a significant Orientation by Target Sex Interaction (F (1,47) = 9.29, p < 0.003). While participants recognized upright and inverted images of females equally well (t (46) = 1.82, p > 0.05), they performed better for upright than for inverted images of males (t (46) = 5.56, p < 0.0001), consistent with Bernard et al. We conducted an ideal observer analysis to quantify the discriminability of the images, obtaining 100 thresholds for each of the 48 images (12 upright males, 12 inverted males, 12 upright females, 12 inverted females). A simple linear regression showed that discriminability predicted human performance (t (1,46) = -2.818, p < 0.000, adj. R² = 0.1287), suggesting that physical characteristics of the images account for some of the differences in the inversion effect. However, after accounting for discriminability, there was still a reliable residual difference in the inversion effects across stimulus sets as shown by a significant Target Sex by Orientation Interaction (F(1,43) = 4.99, p < 0.031), supporting the SBIH. Meeting abstract presented at VSS 2016
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".