The Influence of Face Outline, Number of Features, and Feature Saliency on Face Processing: A Mass Univariate Re-Analysis of ERP Data
Bibliographic record
Abstract
Recent ERP research using a gaze-contingent paradigm suggests the face-sensitive N170 component is modulated by the presence of a face outline, the number of parafoveal facial features, and the type of feature in parafovea (Parkington & Itier, 2019). The present study re-analyzed these data using robust mass univariate statistics available through the LIMO toolbox (Pernet et al., 2011), allowing the examination of the ERP signal across all electrodes and time points. We replicated the finding that the presence of a face outline significantly reduced ERP latency, suggesting it is an important part of the canonical face template. However, we found that this effect began around 130ms, and was maximal between the P1-N170 and N170-P2 intervals rather than on the N170 peak itself. We observed a main effect of the number of features present in parafovea, with amplitude and latency increasing as the number of features decreases. This effect was maximal around 180ms, between the N170 and P2. The ERP response was also modulated by feature type; contrary to previous findings this effect was maximal around 200ms and the P2 peak. Although we provide partial replication of previous results, the effects are more temporally distributed than previously observed. The effect of the face outline occurred prior to the N170, the effect of number of features was maximal between N170 and P2, and the effect of parafoveal feature type was most pronounced after the N170, around and after the P2 component. For all observed effects, relatively little modulation of amplitude or latency was observed on the N170 peak itself. This re-analysis demonstrates that classical ERP analysis can obscure important featural aspects of face processing beyond the N170 peak, and that tools like mass univariate statistics are needed to shed light on the whole time-course of face processing.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".