High Predictive Accuracy of Negative Schizotypy With Acoustic Measures

Negative schizotypal traits potentially can be digitally phenotyped using objective vocal analysis. Prior attempts have shown mixed success in this regard, potentially because acoustic analysis has relied on small, constrained feature sets. We employed machine learning to (a) optimize and cross-validate predictive models of self-reported negative schizotypy using a large acoustic feature set, (b) evaluate model performance as a function of sex and speaking task, (c) understand potential mechanisms underlying negative schizotypal traits by evaluating the key acoustic features within these models, and (d) examine model performance in its convergence with clinical symptoms and cognitive functioning. Accuracy was good (> 80%) and was improved by considering speaking task and sex. However, the features identified as most predictive of negative schizotypal traits were generally not considered critical to their conceptual definitions. Implications for validating and implementing digital phenotyping to understand and quantify negative schizotypy are discussed.

Automated summary

This study utilized machine learning to optimize and validate predictive models of negative schizotypy, finding that accuracy was good and improved by considering sex and speaking task. The identified predictive features were not considered critical to the conceptual definitions of negative schizotypal traits. The implications for validating and implementing digital phenotyping to understand and quantify negative schizotypy were discussed.