Temporal dynamics of the face familiarity effect: bootstrap analysis of single-subject event-related potential data
Bibliographic record
Abstract
Prior event-related potential studies using group statistics within a priori selected time windows have yielded conflicting results about familiarity effects in face processing. Our goal was to evaluate the temporal dynamics of the familiarity effect at all time points at the single-subject level. Ten subjects were shown faces of anonymous people or celebrities. Individual results were analysed using a point-by-point bootstrap analysis. While familiarity effects were less consistent at later epochs, all subjects showed them between 130 and 195 ms in occipitotemporal electrodes. However, the relation between the time course of familiarity effects and the peak latency of the N170 was variable. We concluded that familiarity effects between 130 and 195 ms are robust and can be shown in single subjects. The variability of their relation to the timing of the N170 potential may lead to underestimation of familiarity effects in studies that use group-based statistics.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".