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

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.

Seeing more clearly through psychosis: Depth inversion illusions are normal in bipolar disorder but reduced in schizophrenia

“…People with schizophrenia are putatively less adept at detecting discrepancies (prediction errors) between sensory input and expectations for the purposes of appropriately updating their expectations as to how the world is supposed to look. These individuals will express under-confidence in the visual priors or relative over-confidence in the incoming sensory information (Adams et al, 2013; Moutoussis et al, 2011), leading to a lessened role of high-level visual priors on the interpretation of unusual visual input. There is at least suggestive neurobiological evidence for this view: dynamic causal modeling, functional imaging, and event-related potentials show that feedback from intraparietal sulcus (IPS) to lateral occipital complex (LOC) is stronger in healthy controls than in schizophrenia patients and that this predicts reduced DIIs in patients (Dima et al, 2009, 2010).…”

Challenges and promises for translating computational tools into clinical practice

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A model-based analysis of impulsivity using a slot-machine gambling paradigm

“…This notion is rapidly gaining attention, particularly in the application to psychiatry (cf. “computational psychiatry”; Moutoussis et al, 2011; Montague et al, 2012; Stephan and Mathys, 2014), and represents the approach pursued in this paper. To gain acceptance in the field, however, any model-based approach of this sort will have to show construct validity with respect to an established standard, i.e., a commonly used questionnaire (for a similar rationale, see Huys et al, 2012).…”