Hierarchical Bayesian models of social inference for probing persecutory delusional ideation.
Bibliographic record
Abstract
While persecutory delusions (PDs) have been linked to fallacies of reasoning and social inference, computational characterizations of delusional tendencies are rare. Here, we examined 151 individuals from the general population on opposite ends of the PD spectrum (Paranoia Checklist [PCL]). Participants made trial-wise predictions in a probabilistic lottery, guided by advice from a more informed human and a nonsocial cue. Additionally, 2 frames differentially emphasized causes of invalid advice: (a) the adviser's possible intentions (dispositional frame) or (b) the rules of the game (situational frame). We applied computational modeling to examine possible reasons for group differences in behavior. Comparing different models, we found that a hierarchical Bayesian model (hierarchical Gaussian filter) explained participants' responses better than other learning models. Model parameters determining participants' belief updates about the adviser's fidelity and the contribution of prior beliefs about fidelity to trial-wise decisions, respectively, showed significant Group × Frame interactions: High PCL scorers held more rigid beliefs about the adviser's fidelity across both experimental frames and relied less on advice in situational frames than low scorers. These results suggest that PD tendencies are associated with rigid beliefs and prevent adaptive use of social information in "safe" contexts. This supports previous proposals of a link between PD and aberrant social inference. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".