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

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.