The Eye-Size Illusion: Psychophysical Characteristics, Generality, and Relation to Holistic Face Processing
Bibliographic record
Abstract
Rakover [(2011). In Y. H. Zhang (Ed.), Advances in face image analysis: Techniques and technologies (pp. 316-333). Hershey, PA: IGI Global] observed a novel eye-size illusion: when increasing the size of a face but keeping the size of its eyes unchanged, the eyes are perceived to be smaller than in the original face. Here, we systematically manipulated the face size and found that the magnitude of this illusion linearly changed as a function of the face frame size (experiment 1). Additionally, the same magnitude of an illusion was observed for the perception of the size of the mouth when we changed the face frame but kept the mouth size constant (experiment 2). Further, when the faces and eyes were presented upside down, the magnitude of the illusion was significantly reduced in both Chinese participants (experiment 3) and Caucasian participants (experiment 4). The results suggest that the perception of eye or mouth size occurs in the relational context of the whole face; and when the face is inverted, thereby disrupting holistic processing, the magnitude of the illusion is reduced. We therefore suggest that holistic processing is involved in producing the illusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Open science | 0.000 | 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".