Heterogeneous Structure in Face-selective Human Occipito-temporal Cortex
Bibliographic record
Abstract
It is well established that the human visual system contains a distributed network of regions that are involved in processing faces, but our understanding of how faces are represented within these face-sensitive brain areas is incomplete. We used fMRI to investigate whether face-sensitive brain areas are solely tuned for whole faces, or whether they contain heterogeneous populations of neurons tuned to individual components of the face as well as whole faces, as suggested by physiological investigations in nonhuman primates. The middle fusiform gyrus (fusiform face area, or FFA) and the inferior occipital gyrus (occipital face area, or OFA) produced robust BOLD activation to synthetic whole face stimuli, but also to the internal facial features and head outlines. BOLD responses to whole face stimuli in FFA were significantly reduced after adaptation to whole faces, but not after adaptation to features or head outlines, whereas activation to head outlines was reduced after adaptation to both whole faces and head outlines. OFA showed no significant adaptation effects for matching adaptation and test conditions, but did exhibit cross-adaptation between whole faces and head outlines. The internal face features did not produce any significant adaptation within either FFA or OFA. Our results are consistent with a model in which independent populations of whole face-, feature-, and head outline-tuned neurons exist within face-sensitive regions of human occipito-temporal cortex, which in turn would support tasks such as viewpoint processing, emotion classification, and identity discrimination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".