Visual competition during early face processing is driven towards stimuli at the fovea
Bibliographic record
Abstract
The N170 component to a face stimulus presented in the periphery is strongly reduced when another face is present at the fovea (Jacques & Rossion, NeuroReport, 2004). A similar but smaller reduction is observed for fixated faces in the context of peripheral faces (Jacques & Rossion, JOV, in press). Together with the fact that the N170 decreases with eccentricity (Rousselet et al., JOV 2005), these results suggest the existence of a visual competition modulated by a foveal bias, the N170 amplitude being mostly driven by the stimulus at the fovea. Here we clarify this question in an experiment in which a face or a scrambled-face was presented at the fovea together with 2 peripheral faces or scrambled-faces centered at 5 degrees to the left and right of the fixation point. As hypothesized, the N170 amplitude was mostly driven by the stimulus at the fovea. This was particularly true for a foveal face stimulus, in which case there was no increase associated with peripheral faces at left and temporal sites, and a small increase at occipital right hemisphere sites. The presence of a scrambled-face at the fovea, presented simultaneously with peripheral faces, tended to drive the signal toward the lower N170 response to a scrambled-face alone. Yet, there was still an increase due to faces presented in the periphery. This effect had a bilateral occipital-temporal topography. These results suggest that foveal stimuli have a strong competitive advantage over stimuli in the periphery, an effect that is even stronger for faces.
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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 | 0.001 |
| 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 | 0.001 |
| 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".