Perceived size of the face and arm depends on visual orientation
Bibliographic record
Abstract
The perception of body size has traditionally been studied using subjective, qualitative measures that assess only one type of body representation - the conscious body image. Previous research has typically focused on measuring the perceived size of the entire body rather than individual body parts, such as the face and arms. Here, we present a novel psychophysical method for determining perceived body size that taps into the implicit body representation. Using a two-alternative forced choice (2AFC) design, participants were sequentially shown two life-size images of either their own arm or their own face. In one interval either the horizontal or vertical dimension of the image was varied using an adaptive staircase, while the other interval contained the full-size, undistorted image. Participants reported which image most closely matched their own perceived size. The staircase honed in on the distorted image that was equally likely to be judged as matching their perception as the accurate image, from which the perceived size could be calculated. The visual orientation of the image was varied to compare performance for familiar and unfamiliar views. When the face was viewed upright or upside down, the width was overestimated and length underestimated whereas perception was accurate for the on-side view. Arm length was significantly overestimated when shown in either horizontal or vertical orientations. These results indicate that participants' representation of their face is wider and shorter than actual size and that they represent their arms as longer than actual size, although of accurate width. The method reveals distortions of the implicit body representation independent of the conscious body image. Meeting abstract presented at VSS 2016
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".