An online replication of the association between face perception abilities and the amount of visual information required to identify a face
Bibliographic record
Abstract
The role of holistic vs. featural information in face processing and the importance of replication are recurrent topics in the field. While some support the importance of holistic processing for individual differences in face identification, others reveal the central role of face parts. Royer and colleagues (2015) found a strong negative correlation between the amount of information required for identification and individual abilities. They conclude that the main mechanism underlying individual differences in face processing ability does not rely on the whole face. Since the original study involved a small, homogeneous sample (35 French Canadians), and considering that this result may seem surprising, an online replication was attempted using Pack&Go, an online platform enabling real-time modifications to stimuli. Online replication allowed for a more diverse sample in terms of socioeconomic level and country of origin. Participants (N=115) completed the same paradigm in which they had to match a target face (front-view or side-view) with one of two front-view faces partially revealed with randomly positioned small Gaussian apertures (Bubbles; Gosselin & Schyns, 2002). The number of bubbles was controlled using QUEST so that each participant achieved a target performance of 75%. Participants also completed a face matching task (GFMT2), which represented a measure of their processing abilities with whole faces. We observed two significant negative correlations between face processing ability and the last number of bubbles in each condition (rhoside = -0.52, pside < 0.001, rhofront = -0.48, pfront < 0.001), indicating that individuals with superior face processing skills require less facial information for accurate task performance. These correlations are of a similar magnitude to the one reported in the original article. These results not only provide further evidence for the pivotal role of part-based processing during face identification, challenging conventional theories, but also showcase the flexibility of online testing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.011 | 0.002 |
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".