Testing a Two-Component Model of Face Identification: Effects of Inversion, Contrast Reversal, and Direction of Lighting
Bibliographic record
Abstract
Enns and Shore (1997 Perception & Psychophysics 59 23-31) found additive effects of test orientation (upright or inverted) and direction of lighting (brow or chin lit) when they studied the inversion effect on face identification. A two-stage model was inferred in which inversion was processed by an orientation-sensitive component after which chin-lighting was processed by a lighting-sensitive component. Face identification is also strongly influenced by contrast reversal. A study is reported which aimed to (i) determine if contrast reversal interacts with lighting direction or orientation, findings that would support Enns and Shore's model; and (ii) to test their assumption that holistic encoding is prerequisite for their model by inducing featural encoding through training names to inverted faces. Names for unfamiliar brow-lit positive-contrast faces were trained with the faces upright or inverted. Identification accuracy was measured with combinations of orientation, lighting, and contrast. Consistent with their model, test orientation and direction of lighting were additive after training on upright faces and lighting and contrast reversal interacted. When holistic encoding was prevented following training on inverted faces, test orientation and lighting direction interacted for positive-contrast faces. Negative faces showed only an effect of direction of lighting. These results support Enns and Shore's two-stage model and their interpretation that orientation and direction of lighting interact following featural encoding of faces.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".