ABSTRACT
Background: Jumping to conclusions during probabilistic reasoning is a cognitive bias reliably observed in psychosis, and linked to delusion formation. Although the reasons why people jump to conclusions are unknown, one suggestion is that they view sampling information as costly. However, previous computational modelling Moutoussis et al. 2011) has provided evidence against it, suggesting that patients with chronic schizophrenia jump to conclusion because of noisy decision making. We therefore developed a novel version of the classical beads-task where the cost of information gathering is systematically manipulated. Methods: For 31 individuals with early psychosis, and 31 healthy volunteers we examined the numbers of ‘draws to decision’ when information sampling had no, a fixed, or an escalating cost. Computational modelling was identical to previous work, and involved estimating a cost of information sampling and a cognitive noise parameters. Results: Overall patients sampled less information than controls. However, group differences in numbers of draws became less prominent at higher cost trials, as less information was sampled by both groups. The attenuation of group difference was not due to floor effects, as in the most costly condition participants sampled more information than an ideal Bayesian agent. Computational modelling showed that patients attributed higher costs to information sampling than controls, but groups did not differ in the noise parameter. Conclusion: Using a psychological manipulation and computational modelling, we provide evidence that early psychosis patients jump to conclusions because of attributing higher costs to sampling information, and not because of being noisy decision makers.