Is dissonance reduction a special case of fluid compensation? Evidence that dissonant cognitions cause compensatory affirmation and abstraction.
Bibliographic record
Abstract
Cognitive dissonance theory shares much in common with other perspectives that address anomalies, uncertainty, and general expectancy violations. This has led some theorists to argue that these theories represent overlapping psychological processes. If responding to dissonance and uncertainty occurs through a common psychological process, one should expect that the behavioral outcomes of feeling uncertain would also apply to feelings of dissonance, and vice versa. One specific prediction from the meaning maintenance model would be that cognitive dissonance, like other expectancy violations, should lead to the affirmation of unrelated beliefs, or the abstraction of unrelated schemas when the dissonant event cannot be easily accommodated. This article presents 4 studies (N = 1124) demonstrating that the classic induced-compliance dissonance paradigm can lead not only to a change of attitudes (dissonance reduction), but also to (a) an increased reported belief in God (Study 2), (b) a desire to punish norm-violators (Study 1 and 3), (c) a motivation to detect patterns amid noise (Study 3), and (d) polarizing support of public policies among those already biased toward a particular side (Study 4). These results are congruent with theories that propose content-general fluid compensation following the experience of anomaly, a finding not predicted by dissonance theory. The results suggest that dissonance reduction behaviors may share psychological processes described by other theories addressing violations of expectations.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".