Ideological reactivity: Political conservatism and brain responsivity to emotional and neutral stimuli.
Bibliographic record
Abstract
Conservatives are often thought to have a negativity bias-responding more intensely to negative than positive information. Yet, recent research has found that greater endorsement of conservative beliefs follows from both positive and negative emotion inductions. This suggests that the role of affect in political thought may not be restricted to negative valence, and more attention should be given to how conservatives and liberals respond to a wider range of stimulation. In this vein, we examined neural responses to a full range of affective stimuli, allowing us to examine how self-reported ideology moderated these responses. Specifically, we explored the relationship between political orientation and 2 event-related potentials (1 late and 1 early) previously shown to covary with the subjective motivational salience of stimuli-in response to photographs with standardized ratings of arousal and valence. At late time points, conservatives exhibited sustained heightened reactivity, compared with liberals, specifically in response to relatively unarousing and neutral stimuli. At early time points, conservatives exhibited somewhat enhanced neural activity in response to all stimulus types compared with liberals. These results may suggest that conservatives experience a wide variety of stimuli in their environment with increased motivational salience, including positive, neutral, and low-arousal stimuli. No effects of valence were found in this investigation. Such findings have implications for the development and refinement of psychological conceptions of political orientation. (PsycINFO Database 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".