Who's more Bayesian? Belief updating and no conservatism bias in Schizophrenia

We update our beliefs based on evidence but often slower than Bayesian theory demands. Beliefs in stability may lead to these conservatism bias. Notably, patients with delusions do not show the conservatism bias and can be more Bayesian in probabilistic reasoning tasks. Still, their reasoning has been explained with reduced general cognitive abilities, i.e.a lower working memory capacity, overweighting of recent information, or lower thresholds for switching from one belief to another. We modeled the graded estimate version of the beads task, i.e. a task where one sees two jars containing opposite ratios of colored beads. One then estimates the probability that a shown bead comes from jar A. We model the deviations from an ideal Bayesian observer on three independent datasets, totalling n=176 healthy controls and n=128 patients with schizophrenia. The parameters describe a) the number of beads considered (memory), b) systematic deviation and c) unsystematic deviations (volatility) from probability estimates. We find that on average patients consider fewer beads, and show more volatile responding. However, patients have on average probability estimates that are closer to the true probabilities and hence show less of a conservatism bias. Our mathematical model captures well the cognitive mechanisms proposed to contribute to performance differences, known as jumping to conclusion bias, in the beads task. It also shows that taking fewer data into account may reduce a cognitive bias.