The Predictive Coding Account of Schizophrenia: Dysfunctional Interaction across Linguistic Levels?

• Applicants: Dr. Yifei He; Dr. Lars Meyer
• Subject Area: Biological Psychiatry
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 529614204

Schizophrenia is characterized by marked language dysfunctions occurring at multiple levels of the linguistic hierarchy, ranging from lower-level auditory perception to higher-level semantic processing. To date, the neuropathology of language deficits in schizophrenia remains unresolved. Here, we aim at providing a unitary electrophysiological account of these linguistic deficits within the framework of predictive coding, which explains language dysfunctions in schizophrenia as an imbalance between prediction and incoming sensory inputs. Specifically, stepping from extant clinical neuroscience studies that investigate language dysfunctions within encapsulated linguistic levels, we hypothesize that patients are impaired in the interaction between prediction from higher abstract linguistic levels and prediction error from lower auditory sensory levels, and that the study of these impairments may provide a phenomenological explanation of the functional deficit underlying auditory hallucinations. To this end, we will employ electroencephalography (EEG) to compare online speech perception and language processing between three groups of participants: patients with schizophrenia with and without auditory hallucinations, and matched healthy controls, with three work packages (WPs). In WP1, we examine auditory perception in the form of syllable omission with a classic oddball paradigm and a sentence-based paradigm, to test if interaction between statistic (oddball) / semantic (sentence) prediction and auditory perception is impaired in schizophrenia. In WP2, a naturalistic paradigm will be employed in which word-by-word indices of semantic prediction and phoneme-level prediction error are obtained through state-of-the-art computational modelling. In WP3, we examine excitation and inhibition (im)balance with resting-state EEG. We hypothesize that EEG markers of dysfunctional linguistic prediction (event related potentials, narrow-band oscillations) are dissociable between patients and controls, and between patients with and without auditory hallucinations. Additionally, the EEG markers from WP1–3 will be further analyzed in WP4 with modern machine learning methods, to test if the electrophysiological account could support EEG-based classification and clustering. To summarize, being the first to address impaired linguistic prediction and prediction error across linguistic levels in schizophrenia with EEG, the project will not only provide a sharpened neurobiological understanding of language deficits in schizophrenia, but also will provide translational knowledge that benefits clinical practice.

• DFG Programme: Research Grants
• Co-Investigators: Professor Dr. Tilo Kircher; Professor Mathias Scharinger, Ph.D.