11 April 2018: Visuomotor Prediction Abnormalities in the Schizophrenia Spectrum

Katy Thakkar
Department of Psychology
Michigan State University

The notion of disordered prediction features prominently in mechanistic theories of psychosis, which highlight the influence of past experiences on how we perceive and interpret our current situation. When stimuli are consistent with predictions, we do not pay them much mind, sparing resources for stimuli that violate predictions. Likewise, predictions support cognition and behavior by providing context when momentary input is inconclusive. It is argued that psychosis arises due to an abnormality in forming or using stored regularities, leading to misinterpretation of sensory information, over-interpretation of meaningless associations and, in general, to a fragmented interaction with the external world and a disjointed idea of self. These prediction abnormalities should manifest both in a failure to predict stimuli and events from prior experience and a failure to predict sensory consequences of action. The visuomotor system provides a test bed for investigating these abnormalities, as predictive processes in the brain influence how we see and where we look. I will present data from a series of studies showing a failure to appropriately predict and compensate for the perceptual consequences of an eye movement in schizophrenia patients. As these sensory predictions of action are crucial to achieving a subjective sense of agency over action, the results speak to a possible mechanism of self-disturbances in schizophrenia. I will also show evidence for a reduced influence of prior information on current perceptual processing using basic visual adaptation paradigms in the schizophrenia spectrum. Neural prediction in the visuomotor system lends itself to systematic investigation and may be extrapolated to understand general principles that guide prediction in the brain as a whole, thus enabling a link between core phenomenological experiences in schizophrenia to activity of single neurons.