Abstract
How can individuals with schizophrenia best be equipped to distinguish delusions from accurate judgements about their environment? This study presents an approach based on the principles of Bayesian probability and presents the results of a series of tests in which a simulated observer classifies randomly generated data characteristic of a simulated environment. The complexity of the data ranges from scalars to vectors of variable lengths, and the simulated observer makes its decisions based on either perfect or imperfect models of its environment. We find that when a low-dimensional observation is considered characteristic of both real observations and delusions, the prior probabilities of any observation being real or fake are of greater importance to the final decision than the attributes of the observation. However, when an observation is high-dimensional (complex), classification accuracy tends to improve toward 100% with increasing complexity of observations, as long as the patient's model of the world isn't drastically inaccurate. On the contrary, when the observer's model is sufficiently inaccurate, the accuracy rate decreases with increasing observational complexity. Overall, the results suggest applicability of the Bayesian model to the use of interventional therapy for those who suffer from psychosis.