An adaptation study of internal and external features in face representations
Bibliographic record
Abstract
Background: Previous studies have shown observers rely more on internal than external features when recognising familiar but not unfamiliar faces. Objective: We used an adaptation paradigm to examine whether this difference in internal and external feature contributions to processing is also reflected in differences in the representations of these two classes of faces in the human visual system. Methods: Twelve subjects adapted to a) whole faces, b) internal features alone, or c) external features alone for 5sec, and were then asked whether a briefly shown ambiguous whole-face most resembled the first or second person. Ambiguous faces were created by morphing between pairs of faces. One set of blocks used four pairs of celebrities, while the other used four pairs of anonymous faces. Results: We replicated the finding of face-identity aftereffects with whole face adaptors, with equivalent magnitude for both familiar and unfamiliar faces. For unfamiliar faces, adaptation to internal features alone and to external features alone also generated face aftereffects in whole-face test images, which were similar in magnitude but less than that from whole-face adaptors. However, for familiar faces, identity aftereffects were produced only by whole-face adaptors and not by internal or external features in isolation. Conclusion: Internal and external features are equivalent in perceptual representations of unfamiliar faces. Familiar faces require the whole-face context for access to their representations, which may reflect another characteristic of holistic mechanisms in face processing. Meeting abstract presented at VSS 2012
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".