The relation between belief in a just world and early processing of deserved and undeserved outcomes: An ERP study
Bibliographic record
Abstract
We used event-related potentials (ERPs) to examine how quickly people in general, and certain people in particular, process deservingness-relevant information. Female university students completed individual difference measures, including individual differences in the belief in a just world (BJW), a belief that people get what they deserve. They then read stories in which an outcome was deserved, undeserved, or neither deserved nor undeserved (i.e., “neutral”) while their ERPs were recorded with scalp electrodes. We found no overall differentiation between early ERP responses (<300 ms post-stimulus onset) to deserved, undeserved, and neutral outcomes. However, BJW correlated with the difference between early ERP responses to these forms of information (rs from |.44| to |.61|; ps from .018 to < .001). The early nature of our effects (e.g., 96 ms after stimulus onset) suggests individual differences in socially-relevant information processing that begins before conscious evaluation of the stimuli. Potential underlying processes include automatic attention to schema-relevant information and to unexpected (and therefore salient) information and automatic processing of belief-consistent information. Our research underscores the importance of the concept of deservingness in human information processing as well as the utility of ERP technology and robust statistical analyses in investigations of complex social stimuli.
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".