Source monitoring biases and auditory hallucinations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it