Does perceptual integration efficiency predict face identification skills?
Bibliographic record
Abstract
Classical theories of face perception propose that the ability to identify a face is not simply explained by an analysis of their constituent parts but rather by a holistic coding of the relationships between these parts. Using a method that explicitly measures perceptual integration efficiency for multiple facial features, it was shown that face identification is no better than what is predicted by efficiency for isolated parts (Gold & al., 2010). Interestingly, face inversion still significantly decreased perceptual integration, which may suggest that expertise for upright faces comes from the ability to process multiple parts at once. The purpose of the present study was to test whether individual differences in face recognition is better explained by integrative processing, or simply by feature processing efficiency. Sixty-four participants were recruited. To measure their face and object recognition skill, they completed the CFMT, CFPT, GFMT2, and VET. To establish ability at processing isolated facial features, as well as an integration index of these features, we measured for each participant the image contrast level necessary to reach 75% accuracy for each feature (i.e., left eye, right eye, nose, mouth) individually, and also when presented simultaneously. A three-predictor multiple linear regression model was tested and shown to predict a significant proportion of variance in face identification skills, R = 0.652 (R2 = 0.426). However, among the tested predictors, only isolated face part recognition ability explained a significant part of the variance (βparts = -0.579, p < 0.001); the integration index and object recognition skill did not (βintegration = -0.004, p = 0.966; βobject = 0.121, p = 0.321). Our results indicate that individual differences are best explained by the ability to process isolated face parts, not integrative processing or object processing.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".