Holistic facial representation is required for some but not all face processing: Evidence from event-related potentials
Bibliographic record
Abstract
Familiarity with a face modifies perceptual and semantic processing as revealed in event-related potential (ERP) repetition effects (i.e., modulation of amplitude due to successive repetition of the same stimulus) at putative face-related components. Changes in perceptual processing are reflected in the N170 and N250 components; familiarity eliminates N170 repetition effects and enhances N250 repetition effects. Changes in semantic processing are reflected in the N400: familiar faces produce larger repetition effects than unfamiliar faces. It is unclear whether a holistic facial representation or partial information of the face (e.g. features) is required to engage these component processes. We created 40 composite faces, aligning the top half of one famous person's face and the bottom half of a different famous person's face. This created a stimulus set in which the parts of each face were familiar but the face as a whole was novel. We recorded ERPs for both the original famous faces and the composite faces as participants performed a 1-back identity-matching task. If a holistic facial representation is necessary to engage processes reflected by each face-related component, then the composite faces should elicit responses akin to unfamiliar faces. If only partial information is sufficient then we may observe similar responses to both famous and composite faces. Famous and composite faces elicited similar responses at N170 and N400, both resembling that of familiar face processing. In contrast, famous and composite faces were differentiated at the N250, showing smaller N250 repetition effects for composite faces compared to famous faces, a response pattern typically observed for unfamiliar faces. These results suggest that perceptual processing as reflected by the N250 requires holistic facial representation, whereas processing reflected by the N170 (perceptual) and the N400 (semantic) is possible with partial face information.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".