Rethinking the link between cognitive sophistication and politically motivated reasoning
Bibliographic record
Abstract
Partisan disagreement is a salient feature of contemporary American politics. A surprising but robust aspect of this disagreement is that it is often the greatest among individuals who are the most cognitively sophisticated. A popular hypothesis for this phenomenon is that cognitive sophistication magnifies “politically motivated reasoning”—reasoning driven by the motivation to reach conclusions congenial to one’s political group identity. However, in the designs of studies supporting this hypothesis, the effect of political group identity is typically conflated with the effect of specific prior beliefs about the issue under study; and reasoning can be affected by such beliefs in the absence of any political group motivation. The diagnosticity of existing evidence is thus ambiguous. To shed new light on this issue, we conducted three experiments in which we statistically controlled for people’s specific prior beliefs—isolating the direct effect of political group identity—when estimating the association between their cognitive sophistication, political group identity, and reasoning in the paradigmatic design used in the literature. Despite observing a robust direct effect of political group identity (per se) on reasoning, we found no evidence that cognitive sophistication magnifies this effect. In contrast, we found fairly consistent evidence that cognitive sophistication magnifies a direct effect of specific prior beliefs on reasoning. We conclude that there is currently a lack of compelling empirical evidence that cognitive sophistication magnifies politically motivated reasoning as commonly understood.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".