The misattribution of emotions and the error-related negativity: a Registered Report
Bibliographic record
Abstract
A growing body of work in social and affective neuroscience suggests that emotion plays an instrumental role in error monitoring processes, rather than only a moderating one. High-powered replications of studies that support this idea, however, are lacking. Here, we attempted a preregistered replication of our own study that had provided evidence for the functional role of emotions in error monitoring: that a neural signal of error monitoring—the error-related negativity—is reduced when participants undergo a misattribution of arousal procedure (Inzlicht & Al-Khindi, 2012). Like a previous replication attempt (Rodilla, Beauducel, & Leue, 2016), our misattribution procedure failed to reduce the amplitude of the ERN. However, it also failed its manipulation check to reduce state anxiety, limiting the conclusions we can draw. Nonetheless, these findings are consistent with the view that our original study may have been a false positive. We discuss these findings in the context of the replication crisis in psychology and of work on the emotional properties of the ERN.
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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 | 0.005 | 0.051 |
| 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 | 0.001 | 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".