The good, the bad, and the ugly: An fMRI investigation of the functional anatomic correlates of stigma
Bibliographic record
Abstract
Social interactions require fast and efficient person perception, which is best achieved through the process of categorization. However, this process can produce pernicious outcomes, particularly in the case of stigma. This study used fMRI to investigate the neural correlates involved in forming both explicit ("Do you like or dislike this person?") and implicit ("Is this a male or female?") judgments of people possessing well-established stigmatized conditions (obesity, facial piercings, transsexuality, and unattractiveness), as well as normal controls. Participants also made post-scan disgust ratings on all the faces that they viewed during imaging. These ratings were subsequently examined (modeled linearly) in a parametric analysis. Regions of interest that emerged include areas previously demonstrated to respond to aversive and disgust-inducing material (amygdala and insula), as well as regions strongly associated with inhibition and control (anterior cingulate and lateral prefrontal cortex). Further, greater differences in activation were observed in the implicit condition for both the amygdala and prefrontal cortical regions in response to the most negatively perceived faces. Specifically, as subcortical responses (e.g., amygdala) increased, cortical responses (e.g., lateral PFC and anterior cingulate) also increased, indicating the possibility of inhibitory processing. These findings help elucidate the neural underpinnings of stigma.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".