Impairments in Probabilistic Prediction and Bayesian Learning Can Explain Reduced Neural Semantic Priming in Schizophrenia.

It has been proposed that abnormalities in probabilistic prediction and dynamic belief updating explain the multiple features of schizophrenia. Here, we used electroencephalography (EEG) to ask whether these abnormalities can account for the well-established reduction in semantic priming observed in schizophrenia under nonautomatic conditions. We isolated predictive contributions to the neural semantic priming effect by manipulating the prime's predictive validity and minimizing retroactive semantic matching mechanisms. We additionally examined the link between prediction and learning using a Bayesian model that probed dynamic belief updating as participants adapted to the increase in predictive validity. We found that patients were less likely than healthy controls to use the prime to predictively facilitate semantic processing on the target, resulting in a reduced N400 effect. Moreover, the trial-by-trial output of our Bayesian computational model explained between-group differences in trial-by-trial N400 amplitudes as participants transitioned from conditions of lower to higher predictive validity. These findings suggest that, compared with healthy controls, people with schizophrenia are less able to mobilize predictive mechanisms to facilitate processing at the earliest stages of accessing the meanings of incoming words. This deficit may be linked to a failure to adapt to changes in the broader environment. This reciprocal relationship between impairments in probabilistic prediction and Bayesian learning/adaptation may drive a vicious cycle that maintains cognitive disturbances in schizophrenia.