Supportive but biased: Perceptual neural intergroup bias is sensitive to minor reservations about supporting outgroup immigration
Bibliographic record
Abstract
While decreasing negative attitudes against outgroups are often reported by individuals themselves, biased behaviour prevails. This gap between words and actions may stem from unobtrusive mental processes that could be uncovered by using neuroimaging in addition to self-reports. In this study we investigated whether adding neuroimaging to a traditional intergroup bias measure could detect intersubject differences in intergroup bias processes in a societal context where opposing discrimination is normative. In a sample of 43 Finnish students, implicit behavioural measures failed to indicate intergroup bias against Middle Eastern and Muslim immigrants, and explicit measures reported rather positive attitudes and sentiments towards that targeted group. Yet, while implementing a repeatedly validated method for detecting intergroup bias, an implicit association paradigm presenting stereotypical ingroup and outgroup face stimuli while undergoing magnetoencephalography, we detected a clear neural difference between two experimental conditions. The neural effect is thought to reflect intergroup bias in the valence of the associations that faces evoke. The activity cluster of the neural bias peaked in BA37 and included significant activity in the fusiform gyrus, which has been repeatedly found to be active during face perception bias. Importantly, this neural pattern was driven by participants who were explicitly favourable of immigration - but to a lesser extent than others. These findings suggest that such variations in explicit support of immigration are associated with the differential neural sensitivity to the congruency of associations between intergroup faces and valence. This research showcases the potential of neuroimaging to unravel covert perceptual bias against outgroup members and its sensitivity to small variations in explicit attitudes.
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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".