Part-based processing, but not holistic processing, predicts individual differences in face recognition abilities
Bibliographic record
Abstract
This study aimed to assess the roles of part-based and holistic processing for face processing ability (FPA). A psychophysical paradigm in which the efficiency at recognizing isolated or combined facial parts was used ( N = 64), and holistic processing was defined as the perceptual integration from multiple parts. FPA and object processing ability were measured using a battery of tasks. A multiple linear regression including three predictors, namely perceptual integration, part-based efficiency, and object processing, explained 40 % of the variance in FPA. Most importantly, our results reveal a strong predictive relationship between part-based efficiency and FPA, a small predictive relationship between object processing ability and FPA, and no predictive relationship between perceptual integration and FPA. This result was obtained despite considerable variance in perceptual integration skills–with some participants exhibiting a highly efficient integration. These results indicate that part-based processing plays a pivotal role in FPA, whereas holistic processing does not. • Holistic processing is not associated with better face recognition skills. • Features processing efficacy predicts best individual differences in face recognition. • The whole is not greater than the sum of its parts.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".