Automated clustering and switching algorithms applied to semantic verbal fluency data in schizophrenia spectrum disorders

Abstract

ABSTRACT In the cognitive assessment of Schizophrenia Spectrum Disorders (SSD), the standard scoring method for Verbal Fluency (VF) tasks is the number of correct words produced. Finer-grained measures, such as the size of semantic clusters and the number of transitions between them, have been proposed to characterise the cognitive functions involved, but results based on human ratings are heterogeneous. The objective of this study was to develop a computational procedure based on Vector Space Models (VSMs) to assess the predictive ability of these fine-grained measures for class membership in SSD. A semantic VF task was administered to thirty-five people with SSD and a matched group of healthy participants, and their VF productions were characterised manually and using a set of ad-hoc algorithms. Computational estimates consistently showed higher predictive accuracy than models built on VF measures computed by a human rater and models built on the sole total number of words.