A Painful Reminder: The Role of Level and Salience of Attitude Importance in Cognitive Dissonance
Bibliographic record
Abstract
In his seminal book, L. Festinger (1957) emphasized the role of attitude importance in cognitive dissonance. This study (N = 308) explored whether people's use of dissonance reduction strategies differs as a function of level of attitude importance and whether the personal importance of an attitude is salient. Results showed that level and salience of attitude importance interacted to affect high-choice (HC) participants' tendency to use attitude change and trivialization to reduce dissonance. When HC participants were not reminded of the personal importance of their attitude (i.e., it was not salient), they changed their attitudes equally irrespective of attitude importance, but engaged in greater trivialization with increasing levels of importance. In contrast, when attitude importance was salient, HC participants changed their attitudes less with increasing attitude importance and showed no evidence of trivializing under any level of importance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".