Why does selective attention to parts fail in face processing?
Bibliographic record
Abstract
One hallmark of holistic face processing is an inability to selectively attend to 1 face part while ignoring information in another part. In 3 sequential matching experiments, the authors tested perceptual and decisional accounts of holistic processing by measuring congruency effects between cued and uncued composite face halves shown in spatially aligned or disjointed configurations. The authors found congruency effects when the top and bottom halves of the study face were spatially aligned, misaligned (Experiment 1), or adjacent to one another (Experiment 2). However, at test, congruency effects were reduced by misalignment and abolished for adjacent configurations. This suggests that manipulations at test are more influential than manipulations at study, consistent with a decisional account of holistic processing. When encoding demands for study and test faces were equated (Experiment 3), the authors observed effects of study configuration suggesting that, consistent with a perceptual explanation, encoding does influence the magnitude of holistic processing. Together, these results cannot be accounted for by current perceptual or decisional accounts of holistic processing and suggest the existence of an attention-dependent mechanism that can integrate spatially separated face 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".