Individuating Faces and Common Objects Produces Equal Responses in Putative Face-Processing Areas in the Ventral Occipitotemporal Cortex
Bibliographic record
Abstract
Controversy surrounds the proposal that specific human cortical regions in the ventral occipitotemporal cortex, commonly called the fusiform face area (FFA) and occipital face area (OFA), are specialized for face processing. Here, we present findings from an fMRI study of identity discrimination of faces and objects that demonstrates the FFA and OFA are equally responsive to processing stimuli at the level of individuals (i.e., individuation), be they human faces or non-face objects. The FFA and OFA were defined via a passive viewing task as regions that produced greater activation to faces relative to non-face stimuli within the middle fusiform gyrus and inferior occipital gyrus. In the individuation task, participants judged whether sequentially presented images of faces, diverse objects, or wristwatches depicted the identical or a different exemplar. All three stimulus types produced equivalent BOLD activation within the FFA and OFA; that is, there was no face-specific or face-preferential processing. Critically, individuation processing did not eliminate an object superiority effect relative to faces within a region more closely linked to object processing in the lateral occipital complex (LOC), suggesting that individuation processes are reasonably specific to the FFA and OFA. Taken together, these findings challenge the prevailing view that the FFA and OFA are face-specific processing regions, demonstrating instead that they function to individuate - i.e., identify specific individuals - within a category. These findings have significant implications for understanding the function of brain regions widely believed to play an important role in social cognition.
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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 | 0.001 |
| 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".