Internal and External Features of the Face Are Represented Holistically in Face-Selective Regions of Visual Cortex
Bibliographic record
Abstract
The perception and recognition of familiar faces depends critically on an analysis of the internal features of the face (eyes, nose, mouth). We therefore contrasted how information about the internal and external (hair, chin, face outline) features of familiar and unfamiliar faces is represented in face-selective regions. There was a significant response to both the internal and external features of the face when presented in isolation. However, the response to the internal features was greater than the response to the external features. There was significant adaptation to repeated images of either the internal or external features of the face in the fusiform face area (FFA). However, the magnitude of this adaptation was greater for the internal features of familiar faces. Next, we asked whether the internal features of the face are represented independently from the external features. There was a release from adaptation in the FFA to composite images in which the internal features were varied but the external features were unchanged, or when the internal features were unchanged but the external features varied, demonstrating a holistic response. Finally, we asked whether the holistic response to faces could be influenced by the context in which the face was presented. We found that adaptation was still evident to composite images in which the face was unchanged but body features were varied. Together, these findings show that although internal features are important in the neural representation of familiar faces, the face's internal and external features are represented holistically in face-selective regions of the human brain.
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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 | 0.001 |
| 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".