Negative stereotype activation alters interaction between neural correlates of arousal, inhibition and cognitive control
Bibliographic record
Abstract
Priming negative stereotypes of African Americans can bias perceptions toward novel Black targets, but less is known about how these perceptions ultimately arise. Examining how neural regions involved in arousal, inhibition and control covary when negative stereotypes are activated can provide insight into whether individuals attempt to downregulate biases. Using fMRI, White egalitarian-motivated participants were shown Black and White faces at fast (32 ms) or slow (525 ms) presentation speeds. To create a racially negative stereotypic context, participants listened to violent and misogynistic rap (VMR) in the background. No music (NM) and death metal (DM) were used as control conditions in separate blocks. Fast exposure of Black faces elicited amygdala activation in the NM and VMR conditions (but not DM), that also negatively covaried with activation in prefrontal regions. Only in VMR, however, did amygdala activation for Black faces persist during slow exposure and positively covary with activation in dorsolateral prefrontal cortex while negatively covarying with activation in orbitofrontal cortex. Findings suggest that contexts that prime negative racial stereotypes seem to hinder the downregulation of amygdala activation that typically occurs when egalitarian perceivers are exposed to Black faces.
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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 | 0.000 |
| 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".