English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Bayesian modelling of Jumping-to-Conclusions bias in delusional patients

MPS-Authors
There are no MPG-Authors in the publication available
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Moutoussis, M., Bentall, R., El-Deredy, W., & Dayan, P. (2011). Bayesian modelling of Jumping-to-Conclusions bias in delusional patients. Cognitive Neuropsychiatry, 16(5), 422-447. doi:10.1080/13546805.2010.548678.


Cite as: https://hdl.handle.net/21.11116/0000-0002-C7CF-5
Abstract
Introduction. When deciding about the cause underlying serially presented events, patients with delusions utilise fewer events than controls, showing a “Jumping-to-Conclusions” bias. This has been widely hypothesised to be because patients expect to incur higher costs if they sample more information. This hypothesis is, however, unconfirmed.

Methods. The hypothesis was tested by analysing patient and control data using two models. The models provided explicit, quantitative variables characterising decision making. One model was based on calculating the potential costs of making a decision; the other compared a measure of certainty to a fixed threshold.

Results. Differences between paranoid participants and controls were found, but not in the way that was previously hypothesised. A greater “noise” in decision making (relative to the effective motivation to get the task right), rather than greater perceived costs, best accounted for group differences. Paranoid participants also deviated from ideal Bayesian reasoning more than healthy controls.

Conclusions. The Jumping-to-Conclusions Bias is unlikely to be due to an overestimation of the cost of gathering more information. The analytic approach we used, involving a Bayesian model to estimate the parameters characterising different participant populations, is well suited to testing hypotheses regarding “hidden” variables underpinning observed behaviours.