Early repetition suppression for face identity is caused by the eyes
Bibliographic record
Abstract
Neural repetition suppression (RS) is the decrease in neural activity that follows repeated stimulations, suggesting the same neurons are recruited (Grill-Spector et al., 2006). RS could thus be a useful method to specifically target face sensitive neurons and unveil the nature and the timing of visual information processing in faces. Using a novel approach, we combined RS with Bubbles, a psychophysical technique which consists of randomly sampling image information on a single trial basis with Gaussian apertures (Gosselin & Schyns, 2001). We recorded scalp electroencephalography (EEG; 64 channels) from six participants while they each completed 3000 trials. A trial consisted of presentation of a “bubblized” adaptor face (350ms), followed by an ISI (400-600ms), and presentation of an unfiltered target face (300ms). For the bubblized adaptor, we found an association between N170 and N250 amplitude at PO8 and presence of the left eye, whereby presence of the eye increased amplitudes, replicating previous results (Smith et al., 2004). Afterward, we looked at how target face EEG was modulated by adaptor bubbles, and we found that presence of the left eye in the adaptor led to increased target N170 and N250 suppression. No other facial feature was linked to suppression. These findings suggest that a right hemisphere neural population sensitive to the left eye was solicited both in the N170 and N250 time windows. It has been suggested the N170 indexes a structural encoding (i.e., detection) that starts with the eye region (Schyns et al., 2007). Furthermore, the N250 was linked to stored face representations (Tanaka et al., 2006), and eye information is arguably the most reliable face recognition cue (Butler et al., 2010). Our results thus suggest that the eyes might launch a cascade of face processing, first triggering face detection, and then acting as a cue to retrieve relevant identity representations.
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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 | 0.002 |
| 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 | 0.000 |
| 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".