Natural myside bias is independent of cognitive ability
Bibliographic record
Abstract
Natural myside bias is the tendency to evaluate propositions from within one's own perspective when given no instructions or cues (such as within-participants conditions) to avoid doing so. We defined the participant's perspective as their previously existing status on four variables: their sex, whether they smoked, their alcohol consumption, and the strength of their religious beliefs. Participants then evaluated a contentious but ultimately factual proposition relevant to each of these demographic factors. Myside bias is defined between-participants as the mean difference in the evaluation of the proposition between groups with differing prior status on the variable. Whether an individual difference variable (such as cognitive ability) is related to the magnitude of the myside bias is indicated by whether the individual difference variable interacts with the between-participants status variable. In two experiments involving a total of over 1400 university students (n = 1484) and eight different comparisons, we found very little evidence that participants of higher cognitive ability displayed less natural myside bias. The degree of myside bias was also relatively independent of individual differences in thinking dispositions. We speculate that ideas from memetic theory and dual-process theory might help to explain why natural myside bias is quite dissociated from individual difference variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.035 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".