Face Contour is Crucial to the Fat Face Illusion
Bibliographic record
Abstract
In 2010 Thompson reported a "fat face thin" illusion that, when next to an inverted face, an upright face looks "fatter". Sun et al (2012 Perception 41 117-120) observed that one of the faces need not be inverted for the illusion to emerge: when two identical faces are presented one above the other, the face at the bottom appears "fatter" than the top one. Neither inverted faces nor clocks induced the illusion. Here we conducted three experiments probing the role that face contour plays in producing the fat face illusion. In experiment 1 line drawing faces were found to induce the illusion, suggesting that face contour is important for producing the illusion. In experiment 2 line drawing faces with scrambled internal features and empty line drawing faces devoid of internal features were found to induce the illusion. In experiment 3 internal face features arranged in their canonical face layout, but not in a scrambled layout, were found to induce the illusion. However, the magnitude of the effect was significantly weaker than the effect obtained for empty face contour in experiment 2. Collectively, these results suggest that a fat face illusion is obtained when there is sufficient information in the stimulus to activate an internal face schema.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| 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".