ToM (Theory of Mind)-ML: Machine Learning predicts Mentalization
This work presents an effort to model the prediction about mentalization from neural activity using predictive modeling, and shows how this approach can help improve the prognosis of psychological disorders such as autism and schizophrenia.
Abstract
Introduction Mentalizing is defined as “being engaged in a form of (mostly preconscious) imaginative mental activity that enables us to perceive and interpret human behavior in terms of intentional mental states ,” (Allen, Fonagy, and Bateman 2008) such as needs, desires, thoughts, feelings, intentions. In previous studies, impairment in mentalization has been linked to various psychological disorders such as autism (Frith 2001; Castelli et al. 2002; White et al. 2011; Abell, Happé, and Frith 2000), psychopathy (Decety et al. 2013), and schizophrenia (Russell et al. 2006). In light of these studies, we present an effort to model the prediction about mentalization from neural activity using predictive modeling.