“If Only I Had Done Better”: Perfectionism and the Functionality of Counterfactual Thinking
Bibliographic record
Abstract
Although a recent update on the functional theory of counterfactual thinking suggests that counterfactuals are important for behavior regulation, there is some evidence that counterfactuals may not be functional for everyone. Two studies found differences between maladaptive and high personal standards perfectionism in the functionality of counterfactuals and variables relevant to behavior regulation. Maladaptive but not personal standards perfectionism predicted making more upward counterfactuals after recalling a negative event and was linked to a variety of negative markers of achievement. Maladaptive perfectionism was associated with making controllable, subtractive, and less specific counterfactuals. High personal standards perfectionism moderated the effects of maladaptive perfectionism on counterfactual controllability. Generating counterfactuals increased motivation for personal standards perfectionists relative to a noncounterfactual control group but had no effect on motivation for maladaptive perfectionists. The findings suggest a continuum of counterfactual functionality for perfectionists and highlight the importance of considering counterfactual specificity and structure.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".