Differential effects of eccentricity on N170 for faces and houses
Bibliographic record
Abstract
The present study aimed to characterize more precisely the link between face processing and the N170, a posterior negative event-related component that is particularly pronounced for human faces. Using a forced choice procedure, observers discriminated faces from houses while high-density EEG (256 electrodes) recordings were collected. All stimuli were flashed for 80 ms at varying eccentricities (0, 3.5, 7 and 10.5 degrees, relative to fixation). Preliminary results revealed a clear N170 for both centrally presented faces and houses, but the N170 was much larger for faces than for houses. The N170 evoked by faces decreased in amplitude and increased in latency with stimulus eccentricity, an effect that mirrored the increase in RT observed at the behavioral level. However, the N170 evoked by houses was nearly invariant with stimulus eccentricity. Hence, the difference between the N170 evoked by faces and houses diminished with stimulus eccentricity, becoming marginal at 10? in some subjects. Our results demonstrate that the generators of the N170 to faces are more affected by stimulus eccentricity than the generators to other objects like houses. This result might reflect a foveal bias affecting the generators of the face N170. Such a bias could be due to cortical magnification or the involvement of different spatial frequency bands in face and house processing. These alternative hypotheses will be investigated in future experiments. In addition, subject-by-subject source analyses will be performed to determine the possible cortical origin of the N170 effect.
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 | 0.000 | 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 | 0.000 |
| 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".