Outrage Fatigue? Cognitive Costs and Decisions to Blame
Bibliographic record
Abstract
Across nine studies (N=1,672), we assessed the link between cognitive costs and the choice to express outrage by blaming. We developed the Blame Selection Task, a binary free-choice paradigm that examines the propensity to blame transgressors (versus an alternative choice)—either before or after reading vignettes and viewing images of moral transgressions. We hypothesized that participants’ choice to blame wrongdoers would negatively relate to how cognitively inefficacious, effortful, and aversive blaming feels (compared to the alternative choice). With vignettes, participants approached blaming and reported that blaming felt more efficacious. With images, participants avoided blaming and reported that blaming felt more inefficacious, effortful, and aversive. Blame choice was greater for vignette-based transgressions than image-based transgressions. Blame choice was positively related to moral personality constructs, blame-related social-norms, and perceived efficacy of blaming, and inversely related to perceived effort and aversiveness of blaming. The BST is a valid behavioral index of blame propensity, and choosing to blame is linked to its cognitive costs.
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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 | 0.013 | 0.065 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".