Historical pursuits of the language pathway hypothesis of schizophrenia

Disembodiment and Language in Schizophrenia: An Integrated Psychopathological and Evolutionary Perspective

Different hypotheses have flourished to explain the evolutionary paradox of schizophrenia. In this contribution, we sought to illustrate how, in the schizophrenia spectrum, the concept of embodiment may underpin the phylogenetic and developmental pathways linking sensorimotor processes, the origin of human language, and the construction of a basic sense of the self. In particular, according to an embodied model of language, we suggest that the reuse of basic sensorimotor loops for language, while enabling the development of fully symbolic thought, has pushed the human brain close to the threshold of a severe disruption of self-embodiment processes, which are at the core of schizophrenia psychopathology. We adopted an inter-disciplinary approach (psychopathology, neuroscience, developmental biology) within an evolutionary framework, to gain an integrated, multi-perspectival model on the origin of schizophrenia vulnerability. A maladaptive over-expression of evolutionary-developmental trajectories toward language at the expense of embodiment processes would have led to the evolutionary "trade-off" of a hyper-symbolic activity to the detriment of a disembodied self. Therefore, schizophrenia psychopathology might be the cost of long-term co-evolutive interactions between brain and language.

How to reap the benefits of language for psychiatry

Our aim is to find accurate and valid markers for diagnosis, prognosis, and the monitoring of treatment to improve outcome for patients with schizophrenia-spectrum disorders. This search has led us into the disciplines of computational linguistics and artificial intelligence, as automatic analysis of spoken language may provide useful markers for psychiatry. Together with our language team at UMC Groningen and with great colleagues around the globe, we intend to push this field forward and provide tools that can support service users in self-monitoring and help clinicians with diagnosis, treatment monitoring and risk prediction.