Depolarizing American voters: Democrats and Republicans are equally susceptible to false attitude feedback
Bibliographic record
Abstract
American politics is becoming increasingly polarized, which biases decision-making and reduces open-minded debate. In two experiments, we demonstrate that despite this polarization, a simple manipulation can make people express and endorse less polarized views about competing political candidates. In Study 1, we approached 136 participants at the first 2016 presidential debate and on the streets of New York City. Participants completed a survey evaluating Hillary Clinton and Donald Trump on various personality traits; 72% gave responses favoring a single candidate. We then covertly manipulated their surveys so that the majority of their responses became moderate instead. Participants only noticed and corrected a few of these manipulations. When asked to explain their responses, 94% accepted the manipulated responses as their own and rationalized this neutral position accordingly, even though they reported more polarized views moments earlier. In Study 2, we replicated the experiment online with a more politically diverse sample of 498 participants. Both Clinton and Trump supporters showed nearly identical rates of acceptance and rationalization of their manipulated-to-neutral positions. These studies demonstrate how false feedback can powerfully shape the expression of political views. More generally, our findings reveal the potential for open-minded discussion even in a fundamentally divided political climate.
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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 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".