N250 amplitude is driven by the eyes in mid-to-high spatial frequencies
Bibliographic record
Abstract
One of the most studied face-sensitive event-related potential is the N170. Multiple studies have already explored the specific visual information driving this component’s response. For instance, the N170 has been linked to processing of the eye region and to the integration of diagnostic information (Schyns et al., 2007). However, little is known about what information elicits the N250, another component associated with face identification, more specifically, transient activation of stored face representations (Tanaka et al., 2006). To have a better understanding of this, we recorded scalp electroencephalography (EEG; 64 channels) from four participants while they each completed 12,000 trials (48,000 trials total) of a ten-identity face recognition task. Facial information was randomly sampled with Bubbles (Gosselin & Schyns, 2002), which applies Gaussian windows independently to five non-overlapping spatial frequency (SF) bands (one octave width). At each time point and for each SF band, data from channels PO7 and PO8 were submitted to classification image analysis to measure the association between facial information and EEG voltage. As expected from previous studies, results revealed an association between N170 amplitude and presence of the contralateral eye in every SF band (Rousselet et al., 2014). In addition, N250 amplitude was also linked with presence of the contralateral eye at high (32-64 cpf) and intermediate (4-8 cpf) SFs, but not at lower (2-4 cpf) SFs. Interestingly, the eye region was also found to be the most diagnostic feature for face identification in high to intermediate SFs (Butler et al., 2010). Moreover, the eye region is also rich in horizontal structure (Daking & Watt, 2009), an orientation band that has been both associated with face recognition and the N250 (Hashemi et al., 2018). Together, these results suggest that diagnostic information, especially from the eyes, plays a crucial role in retrieval and activation of stored face 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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".