The eye-size illusion in the face composite task: Evidence for a direct role of holistic processing
Bibliographic record
Abstract
The eye-size illusion refers to the fact that when the size of a face’s frame is changed but the eye size is unchanged, observers judge the eye size to be different from that in the original face frame (Rakover, 2011; Xiao et al., 2014). Indirect evidence suggests holistic processing to be involved in engendering this illusion (Xiao et al., 2014). To test directly the role of holistic processing in the eye-size illusion, we used the composite face paradigm (Young et al., 1987), a gold standard test of holistic processing. Participants (n=30) judged which eyes were bigger in two conditions (Figure 1). In the original-composite face condition (Figure 1a & c), one face is the original face and another is the composite face (the top is the same as the original but the bottom is a different person’s). In the composite-composite condition (Figure 1b & d), two faces are identical composite faces. In each condition, the faces were either aligned (Figure 1a & b) or misaligned (Figure 1c & d). The face sizes differed from the original by 6%, 10%, or 14% with the eye size unchanged. Participants’ eye-size illusion was greater with a smaller face frame, replicating the robust eye-size illusion, F(2, 28) = 11.59, p < .001, η2 = .45. Their eye-size illusion was greater in the aligned than misaligned faces, suggesting the involvement of holistic processing, F(1, 29) = 8.62, p < .01, η2 = .23. Participants also showed a greater eye-size illusion in the original-composite condition than in the composite-composite condition, F(1, 29) = 18.88, p < .001, η2 = .39, further supporting the role of holistic processing. These results together suggest that holistic face processing plays an important in the perception of the eye size illusion. Meeting abstract presented at VSS 2015
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".