Perspective Taking and Self-Persuasion: Why “Putting Yourself in Their Shoes” Reduces Openness to Attitude Change
Bibliographic record
Abstract
Counterattitudinal-argument generation is a powerful tool for opening people up to alternative views. On the basis of decades of research, it should be especially effective when people adopt the perspective of individuals who hold alternative views. In the current research, however, we found the opposite: In three preregistered experiments (total N = 2,734), we found that taking the perspective of someone who endorses a counterattitudinal view lowers receptiveness to that view and reduces attitude change following a counterattitudinal-argument-generation task. This ironic effect can be understood through value congruence: Individuals who take the opposition's perspective generate arguments that are incongruent with their own values, which diminishes receptiveness and attitude change. Thus, trying to "put yourself in their shoes" can ultimately undermine self-persuasion. Consistent with a value-congruence account, this backfire effect is attenuated when people take the perspective of someone who holds the counterattitudinal view yet has similar overall values.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".