Multiple indices of holistic processing are uncorrelated with each other and with face identification
Bibliographic record
Abstract
What role does holistic processing play in face recognition? Konar et al. (Psych Sci 2010; VSS2010) showed that the standard composite-face-effect (CFE) was not correlated with face identification. Gauthier et al. (VSS2010) suggested, however, that face identification may be related to other measures of holistic processing. Here we examine this suggestion directly by determining the relationships among several commonly used measures of holistic processing and face recognition. Eighty participants completed 4 tasks: CFE, the whole-part-effect (WPE), the face-inversion-effect (FIE), and the Cambridge Face Memory Test (CFMT; Duchaine & Nakayama, 2006). Participants were tested on versions of the WPE task that used masked stimuli (no hair, ears, or necks) or with stimuli embedded in a generic face outline (including hair, ears, and neck). We found significant CFEs and FIEs. The WPE based on internal features of faces was not significant, whereas the WPE based on whole faces was, suggesting that the WPE may not be driven exclusively by internal (relevant) features. Analyses of correlations among purportedly holistic/configural indices revealed that none of the effects (CFE, WPE, or FIE) were related significantly with one another, with the CFMT, or with upright face identification accuracy. The CFMT and upright face identification were correlated. These results suggest that holistic processing is not a unified concept: different tasks reflect different aspects of processing, with some tasks influenced by irrelevant features outside the face. Finally, although there may be a role for holistic processing in face identification of some special populations (Konar VSS2009), none of these standard holistic measures predicts performance for typical young observers on face identification using standard perception- or memory-based tasks. These results challenge the notion that holistic processing is a unified concept and ubiquitous for face perception.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".