Lateral Orbitofrontal Cortex Links Social Impressions to Political Choices
Bibliographic record
Abstract
Recent studies of political behavior suggest that voting decisions can be influenced substantially by "first-impression" social attributions based on physical appearance. Separate lines of research have implicated the orbitofrontal cortex (OFC) in the judgment of social traits on the one hand and economic decision-making on the other, making this region a plausible candidate for linking social attributions to voting decisions. Here, we asked whether OFC lesions in humans disrupted the ability to judge traits of political candidates or affected how these judgments influenced voting decisions. Seven patients with lateral OFC damage, 18 patients with frontal damage sparing the lateral OFC, and 53 matched healthy participants took part in a simulated election paradigm, in which they voted for real-life (but unknown) candidates based only on photographs of their faces. Consistent with previous work, attributions of "competence" and "attractiveness" based on candidate appearance predicted voting behavior in the healthy control group. Frontal damage did not affect substantially the ability to make competence or attractiveness judgments, but patients with damage to the lateral OFC differed from other groups in how they applied this information when voting. Only attractiveness ratings had any predictive power for voting choices after lateral OFC damage, whereas other frontal patients and healthy controls relied on information about both competence and attractiveness in making their choice. An intact lateral OFC may not be necessary for judgment of social traits based on physical appearance, but it seems to be crucial in applying this information in political decision-making.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".