Modulation of the Fusiform Face Area following Minimal Exposure to Motivationally Relevant Faces: Evidence of In-group Enhancement (Not Out-group Disregard)
Bibliographic record
Abstract
Studies have shown that fusiform face area (FFA) activity increases with visual expertise. We present an fMRI study showing that faces from a social category made relevant by an experimental manipulation (members of an experimentally created in-group) preferentially recruited the FFA even when they were matched in exposure to face stimuli from a less significant social category (members of an experimentally created out-group). Faces were randomly assigned to groups and fully counterbalanced so that no perceptual cues allowed participants to visually distinguish category membership. The results revealed a pattern of in-group enhancement (not out-group disregard), such that the FFA was selectively engaged following the presentation of in-group compared with out-group or unaffiliated control faces even when the intergroup distinction was arbitrary, and exposure to in-group and out-group faces was equivalent and brief. In addition, individual differences in FFA activity for in-group versus out-group faces were correlated with recognition memory differences for in-group and out-group faces. The effects of group membership on the FFA were not affected by task instruction to respond to in-group or out-group members and were functionally dissociated from early visual processing in the primary visual cortex. This study provides evidence that the FFA is sensitive to top-down influences and may be involved in subordinate level (vs. superordinate level) encoding of stimuli in the absence of long-term exposure or explicit task instructions.
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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 | 0.000 |
| 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) | 0.002 | 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".