Face identification and the evaluation of holistic indexes: CFE and the whole-part task
Bibliographic record
Abstract
Konar, Bennett and Sekuler (Psychological Science, in press) showed that performance in a standard measure of holistic processing, the composite-face-effect task (CFE), was highly variable across observers, and did not correlate with accuracy on a face identification task. This result suggests that the influence of holistic processing on face identification may not be as significant, or automatic, as commonly assumed. Of course, holistic processing can be measured in more than one way, and, although it is typically assumed that the measures are tapping into a single mechanism, that assumption is not typically tested. Here we examine the reliability of and relations between face identification, the CFE, and another measure of holistic processing, the whole-part task (e.g., Tanaka & Farah, 1993). Our whole-part task was modelled after Leder and Carbon's (2005) second experiment: subjects learned associations between names and whole faces or face parts, and then were tested with whole faces and parts in upright and inverted conditions. Our face set removed external features (hair, chin, and ears) to ensure that discrimination was based on internal facial features. Consistent with Konar et al., measures of the CFE and identification accuracy exhibited moderate-to-high reliability, but were uncorrelated with each other. Like other researchers have found, there was a whole-face superiority effect on the whole-part task: performance was better on whole-face trials regardless of learning or orientation, and the effect had high within-observer reliability. Notably, however, there were no significant correlations between performance in the whole-part task and either the CFE or face identification accuracy. These results, based on 10 observers, suggest that different holistic tasks may, in fact, be tapping into distinct perceptual mechanisms, neither of which is predictive of our face identification task.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".