Does holistic processing predict face identification? The effect of aging
Bibliographic record
Abstract
Although most young observers exhibit holistic processing, the extent of holistic processing does not predict face identification (Konar et al., JOV 2008). Here we examined the effects of aging on holistic face processing, as indexed by the Composite Face Effect (CFE), and the role holistic processes play in face identification. The CFE was measured in two groups of younger (mean=22.0 and 19.3 years; n=24 and 52) and older (mean=69.9 and 68.1 years; n=26 and 24) adults. All groups had significant CFEs. There was an age difference in the CFE based on measures of response time in both experiments, with old adults showing higher CFEs (ts ≥2.4, ps ≤0.03). There was no age difference in the CFE based on accuracy (ts ≥−0.5, ps ≤0.63). Therefore there was no evidence that holistic processing, as indexed by the CFE, declines with age, consistent with previous suggestions that holistic processing is spared in aging (Boutet & Faubert, 2006). We then correlated the CFE with face identification accuracy measured in a 10 AFC task that used an unlimited viewing time, and a 4 AFC task that used a short stimulus duration that was identical to the one used in the CFE task. Accuracy in the 10 AFC task did not differ between age groups, but accuracy was lower in older subjects in the 4 AFC task. As reported previously, CFE and face identification were not correlated in younger subjects. In older subjects, CFEs were not correlated with accuracy in the 10 AFC identification task, but CFEs based on response accuracy, not reaction time, approached a significant correlation with face identification accuracy in the 4 AFC task (rho=0.36). The results suggest that holistic processing is maintained throughout our adult lives, and suggest that such processing may place greater constraints on face identification as we age.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".