Automated Assessment of Social Cognition in People with a Schizophrenia Spectrum Disorder

Abstract

Social cognitive deficits are core features of schizophrenia spectrum disorders (SSD) or schizophrenia, amongst other conditions. These deficits limit overall functioning, arguably the most important outcome when treating the mentally ill. However, as noted by former National Institute of Mental Health director Thomas Insel, “one cannot treat what they cannot measure” , and these deficits are difficult to measure in consistent and scalable ways. In this work, I leverage neural language representations (word embeddings and a deep neural network) to derive four novel measures of social cognition from transcribed responses to two video stimuli - one designed to evoke emotions, and the other representing intentions. The resulting measures are evaluated for their ability to distinguish patients with SSD from neurotypical controls, their relationships to validated measures of social cognition and SSD symptomatology, and their ability to detect the effects of an experimental therapeutic agent intended to enhance social cognitive abilities. The resulting automated measures of social cognition can mediate new approaches to the diagnosis, monitoring, treatment, and rehabilitation of people with an SSD, and other conditions involving social cognitive deficits.