Processing the Trees and the Forest during Initial Stages of Face Perception: Electrophysiological Evidence
Bibliographic record
Abstract
Although configural processing is considered a hallmark of normal face perception in humans, there is ample evidence that processing face components also contributes to face recognition and identification. Indeed, most contemporary models posit a dual-code view in which face identification relies on the analysis of individual face components as well as the spatial relations between them. We explored the interplay between processing face configurations and inner face components by recording the N170, an event-related potential component that manifests early detection of faces. In contrast to a robust N170 effect elicited by line-drawn schematic faces compared to line-drawn schematic objects, no N170 effect was found if a pair of small objects substituted for the eyes in schematic faces. However, if a pair of two miniaturized faces substituted for the eyes, the N170 effect was restored. Additional experiments ruled out an explanation on the basis of miniaturized faces attracting attention independent of their location in a face-like configuration and show that global and local face characteristics compete for processing resources when in conflict. The results are discussed as they relate to normal and abnormal face processing.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".