Delineate the temporal sequence and mechanisms for perceiving individual faces
Bibliographic record
Abstract
In two event-related potential (ERP) studies, we examined neural correlates of individual face perception. In Study 1, 36 individual female and 9 male faces were randomly presented, and participants were instructed to press a button for male faces. Based on similarity ratings from a previous behavioral study, the female faces could be located in a multidimensional “face-space”. The facial characteristics representing the “face-space” and therefore important for judging face similarities include eye color, face width, eye size and top-of-face height. The face-sensitive N170 component was affected by all these factors. In addition, there was a hemisphere difference: the right N170 amplitude was related to eye color and face width, while the left N170 amplitude was related to eye size and top-of-face height when bottom-of-face height was small. In Study 2, we created a set of faces that varied in identity strength by morphing each of the 36 female faces with an average face formed from the entire set; the relative weighting of an original face ranged from 100% to 0% in 10% decrements. Participants were instructed to press a button whenever they detected a target identity. Accuracy data indicated an ambiguous region between 30% and 60% identity strength. Neither the P1 nor the N170 to non-target faces were influenced by identity strength. However, the amplitude of the P2 component (230-270 ms) became smaller as identity strength decreased, with no categorical boundary effect. Collectively, these results provide electrocortical evidence of structural decoding of individual faces before 200 ms that involves rather fine-tuned analyses of multiple facial characteristics, which might be carried out separately by two hemispheres. Following structural decoding, the electrocortical evidence of individual face identification occurs around 250 ms with minimal response to “average” 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".