Ensemble coding of facial identity is not refined by experience: Evidence from other‐race and inverted faces
Bibliographic record
Abstract
The ability to recognize identity despite within-person variability in appearance is likely a face-specific skill and shaped by experience. Ensemble coding - the automatic extraction of the average of a stimulus array - has been proposed as a mechanism underlying face learning (allowing one to recognize novel instances of a newly learned face). We investigated whether ensemble encoding, like face learning and recognition, is refined by experience by testing participants with upright own-race faces and two categories of faces with which they lacked experience: other-race faces (Experiment 1) and inverted faces (Experiment 2). Participants viewed four images of an unfamiliar identity and then were asked whether a test image of that same identity had been in the study array. Each test image was a matching exemplar (from the array), matching average (the average of the images in the array), non-matching exemplar (a novel image of the same identity), or non-matching average (an average of four different images of the same identity). Adults showed comparable ensemble coding for all three categories (i.e., reported that matching averages had been present more than non-matching averages), providing evidence that this early stage of face learning is not shaped by face-specific 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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".