N170 and N250 sensitivity to diagnostic facial information during whole-face recognition
Bibliographic record
Abstract
In our presentation at VSS2023, we explored the correlation between N170 face sensitivity and diagnostic information processing during a 10-identities recognition task (Audette et al., 2023). We utilized sparse facial stimuli generated with Bubbles, and previously published classification images (Royer et al., 2018) were employed to quantify the available diagnostic information on a stimulus basis (0-100%). Remarkably, we observed a linear increase in N170 amplitude as diagnostic information increased (r=-0.98), indicating thorough processing of diagnostic information during face identification. However, the revealed amount of diagnostic information strongly correlated with the amount of facial surface (r=0.99), suggesting that this effect may be influenced not only by diagnostic information but also by the overall amount of facial information. Consequently, the second phase systematically examined whether N170 and N250 reflect the quantity of diagnostic information processed by the brain while keeping the amount of facial information constant. Sparse facial stimuli were generated using a method similar to the first phase. To standardize the facial surface across stimuli, we applied inverse bubbles to an average face of the 10 identities, replacing face regions hidden by bubbles with non-diagnostic facial information. The stimuli were categorized into 12 bins ranging from 0.001% to 100%, with an additional 0% condition (average face). EEG data were collected from 10 participants during 1,300 trials of a 10-identities recognition task. We analyzed N170 and N250 peak amplitudes at PO8, along with behavioral responses. Results indicated that only N250 peak amplitude varied: as diagnostic information increased, N250 amplitude linearly increased (r=-0.90). Moreover, N250 amplitudes across bins strongly correlated with recognition accuracies (r=-0.86). Thus, N250 seems to reflect in-depth processing of diagnostic information during face identification, while N170 responds to the ease of categorizing the stimulus as a face.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".