Defining the face processing network: Optimization of the functional localizer in fMRI
Bibliographic record
Abstract
Functional localizers that contrast brain signal when viewing faces versus objects are commonly used in functional magnetic resonance imaging studies of face processing. However, current protocols do not reliably show all regions of the core system for face processing in all subjects when conservative statistical thresholds are used, which is problematic in the study of single subjects. Furthermore, arbitrary variations in the applied thresholds are associated with inconsistent estimates of the size of face-selective regions-of-interest (ROIs). We hypothesized that the use of more natural dynamic facial images in localizers might increase the likelihood of identifying face-selective ROIs in individual subjects, and we also investigated the use of a method to derive the statistically optimal ROI cluster size independent of thresholds. We found that dynamic facial stimuli were more effective than static stimuli, identifying 98% (versus 72% for static) of ROIs in the core face processing system and 69% (versus 39% for static) of ROIs in the extended face processing system. We then determined for each core face processing ROI, the cluster size associated with maximum statistical face-selectivity, which on average was approximately 50 mm(3) for the fusiform face area, the occipital face area, and the posterior superior temporal sulcus. We suggest that the combination of (a) more robust face-related activity induced by a dynamic face localizer and (b) a cluster-size determination based on maximum face-selectivity increases both the sensitivity and the specificity of the characterization of face-related ROIs in individual subjects.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".