Source monitoring biases and auditory hallucinations
Bibliographic record
Abstract
INTRODUCTION: Previous source monitoring studies on schizophrenia reported an association between external source misattribution and hallucinations, but this is often not replicated. This inconsistency may be attributable to a failure in accounting for guessing parameters when computing source monitoring biases. METHODS: Fifty-one patients and 20 healthy controls were required to recall the source of items originating from external (computer and experimenter) or internal (the subject) sources. When statistically determined criteria were met, the appropriate counts of false positives were entered as covariates in the statistical analyses (analysis of covariance; ANCOVA) to exclude guessing from source monitoring bias measures. RESULTS: When comparing patients to controls, impairments on item recognition and source discrimination were observed. When comparing patient groups split on hallucinations, a bias towards attributing self-generated items to an external source was observed. A group difference on the externalisation bias was absent when the sample was split on delusions. CONCLUSIONS: A bias towards attributing self-generated items to an external source was associated with hallucinations. This ANCOVA methodology is recommended for source monitoring studies investigating group differences, and suggests that previously reported null results may be attributable to a failure in separating guessing and source monitoring measures.
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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 | 0.003 | 0.029 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".