Abstract

We review the hypothesis that the brain uses a generative model to explain the causes of sensory inputs, using prediction schemes that operate based upon assimilation of time-series sensory data. We put this hypothesis in the context of psychopathology, in particular, schizophrenia's positive symptoms. Building upon work of Helmholtz and upon theories in computational cognitive processing, we hypothesize that delusions in schizophrenia can be explained in terms of false inference. An impairment in inferring appropriate information from the sensory input reflects upon the ability to assess the environment and predict outcomes. Although the inference mechanism likely involves both conscious and unconscious processes, we hypothesize that the trigger of delusions may lie within the unconscious neural pathways. A collection of computational predictive codes have been proposed for modeling perception. We discuss two examples, which may be eligible as substrates for intuitive coding. We argue that failure of the psychotic patient to choose the correct computational scheme, or the optimal range of parameters, may readily lead to an altered reconstruction of the object and false inference, feeding into the delusion mechanism. We finally propose using these models in conjunction with cognitive and imaging data, in order to obtain more testable predictions