Parametric study of N170 sensitivity to diagnostic facial information during face identification
Bibliographic record
Abstract
Studies have suggested that the face-sensitive N170 indexes a face or an eye detection process. However, such studies have often explored N170 sensitivity in a dichotomic way, to the presence or absence of different facial features, either alone or within a facial context (e.g Parkington & Itier, 2018). Other studies have proposed that the N170 could reflect in-depth integration of diagnostic information (Schyns et al., 2007). The objective of this study was to parametrically investigate whether the N170 reflects the quantity of diagnostic information integrated by the brain. To this end, we randomly created sparse facial stimuli with Bubbles, and used previously published classification images (Royer et al., 2018) to calculate the amount of available diagnostic information on a stimulus basis. Stimuli were then divided into ten bins covering a range from 0.01% to 80% information. Furthermore, a 0% (scrambled face) and 100% (whole face) bin were also adjoined at each extremity of the information spectrum. To equalize energy across stimuli, we applied discrete wavelet transform to unfiltered faces, and filtered the scrambled output with inverse bubbles. In other words, face regions hidden by bubbles were replaced by scrambled face information. EEG was collected from five participants as they each underwent 1,440 trials of a 10-identities recognition task. Using the 0% information condition as baseline, we then looked at the N170 peak amplitude and latency at PO8, in addition to behavioral responses. Results showed both parameters were sensitive to the amount of diagnostic information. As diagnostic information increased, amplitude linearly increased, and latency decreased. Interestingly, individual N170 amplitudes across information bins almost perfectly predicted corresponding recognition accuracies. Perhaps surprisingly, no other electrophysiological process (at PO8) seemed responsive to diagnostic information. Thus, it appears the N170 reflects in-depth processing of diagnostic information during face identification.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".