Why does selective attention to parts fail in face processing?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 it