Neuro-anatomic correlates of the feature-saliency hierarchy in face processing: An fMRI-adaptation study
Bibliographic record
Abstract
Background: Previous fMRI studies suggest that faces are represented holistically in face processing region of the human brain. However, behavioural studies have also shown that some facial features are more important or ‘salient’ than others for face recognition. Objective: We used fMR-adaptation to ask whether different face parts contribute different amounts to the neural signal in face responsive regions of the brain. Methods: 18 subjects first performed a same/different discrimination experiment to characterize their ability to detect changes to different face parts. Next they underwent an fMRI-adaptation study, in which limited portions of the faces were repeated or changed between alternating stimuli. Results: The behavioural study showed high efficiency in identity discrimination when the whole face, top half, or eyes changed, and low efficiency when the bottom half, nose, or mouth changed. On fMRI, there was a release of adaptation in the right fusiform face area (FFA) and right occipital face area (OFA) with changes to the whole face, top face-half, or the eyes. Changes to the bottom half, nose or mouth did not result in a significant release of adaptation. Finally, we asked whether the neural responses were more correlated with individual subjects’ performance in the behavioural experiment or with physical image changes, as determined by an ideal observer technique. Adaptation in the right FFA was correlated with both perceptual and physical changes to faces, but in the right OFA was correlated only with physical properties of the image, and in the left FFA and left OFA was correlated with neither. Conclusions: The hierarchy of facial features is reflected in activity in the right FFA, further supporting the key role of this structure in our perceptual experience of faces. 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 | 0.000 |
| 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".