N170 or N1? Spatiotemporal Differences between Object and Face Processing Using ERPs
Bibliographic record
Abstract
The ERP component N170 is face-sensitive, yet its specificity for faces is controversial. We recorded ERPs while subjects viewed upright and inverted faces and seven object categories. Peak, topography and segmentation analyses were performed. N170 was earlier and larger to faces than to all objects. The classic increase in amplitude and latency was found for inverted faces on N170 but also on P1. Segmentation analyses revealed an extra map found only for faces, reflecting an extra cluster of activity compared to objects. While the N1 for objects seems to reflect the return to baseline from the P1, the N170 for faces reflects a supplement activity. The electrophysiological 'specificity' of faces could lie in the involvement of extra generators for face processing compared to objects and the N170 for faces seems qualitatively different from the N1 for objects. Object and face processing also differed as early as 120 ms.
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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".