When a stranger becomes a friend: Measuring the neural correlates of real-world face familiarisation
Bibliographic record
Abstract
Humans can readily and effortlessly learn new faces encountered in the social environment. As a face transitions from unfamiliar to familiar, the ability to generalize across different images of the same person increases substantially. Fast periodic visual stimulation and EEG (FPVS-EEG) was used to isolate identity-specific responses that generalize across different images of the same person from low-level visual processing and face-general processes that aren’t identity-specific. We observed these signals emerge and increase in magnitude as a group of strangers became lab mates (N=9). The neural response to an unfamiliar identity that remained unfamiliar did not change. Comparison of the response to the newly familiarised face to a highly overlearned face (the own-face) showed that this identity-specific signal was modulated by level of familiarity. The study presents the first examination of identity-specific processing changes as they occur in situ from normal, everyday face experience.
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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 | 0.001 |
| 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) | 0.001 | 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".