The domain specificity and generality of mental contamination: Accuracy and projection in judgments of mental content
Bibliographic record
Abstract
In this study we examined individual differences across two tasks requiring people to judge the mental contents of the minds of other people--an opinion prediction task and a knowledge prediction task. The tendency to overproject one's own mental contents in both of these tasks has been interpreted as an instance of mental contamination by Wilson and Brekke (1994). Results demonstrate no domain generality in the process of projecting one's own internal states onto predictions about the internal states of others. Furthermore, projection was efficacious in the opinion prediction task but not in the knowledge prediction task. The differing consequences of mental contamination across these tasks was moderated by the presence of other diagnostic cues that were negatively correlated with the diagnosticity of one's own mental contents in the knowledge prediction task but not in the opinion prediction task. Mental contamination was largely unrelated to cognitive ability or to styles of epistemic regulation. However, predictive accuracy (and its primary determinant--use of other diagnostic cues) was correlated across the two tasks, and was also related to cognitive ability and styles of epistemic regulation. The results are interpreted within the context of two-process models of cognitive functioning (e.g. Evans & Over, 1996; Sloman, 1996).
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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 | 0.004 | 0.068 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".