Implicit face prototype learning from geometric information
Bibliographic record
Abstract
There is evidence that humans implicitly learn an average or prototype of previously studied faces, as the unseen face prototype is falsely recognized as having been learned (Solso & McCarthy, 1981, Br. J. Psychology). Here we investigated the extent and nature of face prototype formation in a multidimensional face space with a face learning and old/new discrimination experiment. Observers first studied eight synthetic faces defined by geometric information in a 37-dimensional face space (Wilson, Loffler, & Wilkinson, 2002, Vision Research). These faces were equidistant from the unseen prototype and comprised face, anti-face pairs along four axes with the unseen prototype as origin. After studying the faces for 30 s each, memory was tested using a series of test faces, each flashed for 240 ms. Test faces included previously studied faces, the unseen prototype, and eight novel distractor faces defined by four new axes in face space with a face and anti-face on each. Results showed that the unseen prototype was falsely identified as learned at a rate of 86%, whereas studied faces were identified correctly 66% of the time and the distractors 32%. This lasts at least one week. Additional studies demonstrated flexibility of prototype learning: the learned prototype could be either the face-space population mean or a highly distinctive non-mean face. Contrary to previous results (Cabeza et al., 1999, Memory & Cognition), this prototype effect for geometric face information also generalized across viewpoints, as the unseen prototype of faces rotated 20° was also falsely recognized after studying frontal views of the same faces (and vice versa). Further experiments suggest that head shape and internal features separately contribute to prototype formation. Thus, implicit face prototype extraction in a multidimensional space may be a very general aspect of geometric face learning.
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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 | 0.009 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".