Classification of fMRI activation patterns in face-sensitive cortex to the parts and location of faces
Bibliographic record
Abstract
The fusiform face area (FFA) and occipital face area (OFA) are known to respond more to faces than other objects, but the spatial structure of processing within these areas is not yet known. Previous physiological investigations in primates and neuroimaging studies in humans suggest that face-sensitive regions contain independent neural populations that are tuned to the internal features of the face, the shape of the head, or the full face, i.e., the conjunction of the features and the head outline. To test this hypothesis, we obtained fMRI data from eight participants while they viewed images of synthetic full faces, internal features, or head outlines. The FFA and OFA, defined as the regions with greater activation to photographs of faces compared to houses, were localized within individual subjects in a separate set of functional scans. We constructed linear pattern classifiers, based on all voxels in the regions of interest, using support vector machines, to test whether the FFA and OFA process the three different types of stimuli in a spatially distributed manner. Classification was significantly above chance for all types of stimuli according to a leave-one-out verification procedure. In an additional experiment, we found a dissociation in processing between the areas. Classification of the physical position of a face, in one of four visual quadrants, was better in OFA, than FFA, but classification of the type of stimulus, across position, was better in FFA than OFA. This suggests that the FFA and OFA are involved in different aspects of face processing, with OFA positioned earlier in the processing stream. The results are consistent with a columnar organization of faces and face parts, which in turn would support tasks such as viewpoint processing, gender 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 | 0.001 | 0.003 |
| 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".