Individual Differences in Political Ideology and Disgust Sensitivity Affect Real-Time Spoken Language Comprehension
Bibliographic record
Abstract
Individuals' moral views have been shown to affect their event-related potentials (ERP) response to spoken statements, and people's political ideology has been shown to guide their sentence completion behavior. Using pupillometry, we asked whether political ideology and disgust sensitivity affect online spoken language comprehension. 60 native speakers of English listened to spoken utterances while their pupil size was tracked. Some of those utterances contained grammatical errors, semantic anomalies, or socio-cultural violations, statements incongruent with existing gender stereotypes and perceived speaker identity, such as "I sometimes buy my bras at Hudson's Bay," spoken by a male speaker. An individual's disgust sensitivity is associated with the Behavioral Immune System, and may be correlated with socio-political attitudes, for example regarding out-group stigmatization. We found that more disgust-sensitive individuals showed greater pupil dilation with semantic anomalies and socio-cultural violations. However, political views differently affected the processing of the two types of violations: whereas more conservative listeners showed a greater pupil response to socio-cultural violations, more progressive listeners engaged more with semantic anomalies, but this effect appeared much later in the pupil record.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".