Neural Trade-Offs between Recognizing and Categorizing Own- and Other-Race Faces
Bibliographic record
Abstract
Behavioral research has suggested a trade-off relationship between individual recognition and race categorization of own- and other-race faces, which is an important behavioral marker of face processing expertise. However, little is known about the neural mechanisms underlying this trade-off. Using functional magnetic resonance imaging (fMRI) methodology, we concurrently asked participants to recognize and categorize own- and other-race faces to examine the neural correlates of this trade-off relationship. We found that for other-race faces, the fusiform face area (FFA) and occipital face area (OFA) responded more to recognition than categorization, whereas for own-race faces, the responses were equal for the 2 tasks. The right superior temporal sulcus (STS) responses were the opposite to those of the FFA and OFA. Further, recognition enhanced the functional connectivity from the right FFA to the right STS, whereas categorization enhanced the functional connectivity from the right OFA to the right STS. The modulatory effects of these 2 couplings were negatively correlated. Our findings suggested that within the core face processing network, although recognizing and categorizing own- and other-race faces activated the same neural substrates, there existed neural trade-offs whereby their activations and functional connectivities were modulated by face race type and task demand due to one's differential processing expertise with own- and other-race faces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".