Eccentricity effects on the N170 face ERP component can be eliminated by size scaling
Bibliographic record
Abstract
The N170 is a posterior negative event-related component that is particularly pronounced for human faces. Earlier studies indicated that the N170 is stronger when faces are presented foveally than when presented peripherally (Jeffreys, 1992; Eimer, 2000). We previously reported that the difference between the N170 evoked by faces and houses diminishes with stimulus eccentricity, and becomes only marginally significant at 10° of eccentricity (Rousselet et al., VSS 2003). Following Jeffreys (1992) this result might indicate that the N170 is a response to fixated faces, involving a foveal bias in the generators of the N170. To more rigorously examine the possibility of a foveal bias in face processing, it is necessary to rule out the possibility that eccentricity based effects may be a simple consequence of the reduced cortical representation of peripherally presented stimuli. Thus, we have re-examined the effect of eccentricity on the N170, controlling for the effect of cortical magnification. We tested 15 observers. Faces and houses (matched for spatial frequency content) were presented for 80ms, either centrally, or at 5° or 10° to the left or right of fixation. Peripherally presented stimuli were presented at one of two sizes (either matched to the central presentation size, or scaled to compensate for differences in V1 cortical representation). There were a total of 18 conditions with 120 trials per condition. As found previously, the N170 was larger in amplitude for faces than for houses, and this difference decreased with eccentricity. However, when faces and houses were enlarged to compensate for cortical magnification differences, the difference in N170 strength between faces and houses re-emerged. Thus, we find no evidence that there is a foveal bias for face processing per se. Rather, eccentricity-based differences in face processing appear to be largely attributable to simple differences in cortical magnification.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".