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Prior research has focused on the latent structure of endophenotypic markers of schizophrenia liability, or schizotypy. The work supports the existence of 2 relatively distinct latent classes and derives largely from the taxometric analysis of psychometric values. The present study used finite mixture modeling as a technique for discerning latent structure and the laboratory-measured endophenotypes of sustained attention deficits and eye-tracking dysfunction as endophenotype indexes. In a large adult community sample (N = 311), finite mixture analysis of the sustained attention index d' and 2 eye-tracking indexes (gain and catch-up saccade rate) revealed evidence for 2 latent components. A putative schizotypy class accounted for 27% of the sample. A supplementary maximum covariance taxometric analysis yielded highly consistent results. Subjects in the schizotypy component displayed higher rates of schizotypal personality features and an increased rate of treated schizophrenia in their 1st-degree biological relatives compared with subjects in the other component. Implications of these results are examined in light of major theories of schizophrenia liability, and methodological advantages of finite mixture modeling for psychopathology research, with particular emphasis on genomic issues, are discussed. Keywords: finite mixture modeling, schizotypy, schizophrenia, endophenotype, latent structure

Heterogeneity in the performance of persons affected with schizophrenia or schizotypic psychopathology on various laboratory tasks has long been recognized, both for its consistency across tasks and studies and for the massive methodological and substantive challenges it poses for experimental psychopathology, genetic, and other investigations. Traditional multivariate techniques, such as factor analysis, discriminant function analysis, and cluster analysis, have all been deemed inadequate for resolving heterogeneity, because of one or another statistical limitation. Here, an objective statistical approach based on a formal statistical model that uses the ubiquitous and well-developed expectation-maximization (EM) algorithm (A. P. Dempster, N. M. Laird, & D. B. Rubin, 1977) is presented, which enables one effectively to partition a group of experimental subjects, in this case identified initially using the well-known Perceptual Aberration Scale (L. J. Chapman, J. P. Chapman, & M. L. Raulin, 1978), in a manner that reduces heterogeneity and allows for the separation of what are termed genuine and false-positive schizotypes. The validity of the parsing strategy was supported by reference to other laboratory indexes of relevance to schizophrenia and schizotypy that were not included in the initial EM-based analyses. The potential utility of this approach is discussed with reference to future schizophrenia and schizotypy research.

This study reports evidence that schizophrenia patients are significantly impaired in both spatial and object (shape) working memory. A 3-s delay between exposure and recall of targets was used and Bayesian item-response theory was applied to compensate for the tasks' differential difficulty while simultaneously taking account of missing data from participant attrition. Weaker evidence was found that in schizophrenia both domains are equally impaired on average, that spatial and object working memory appear to be more highly correlated with each other in the schizophrenia population than in the normal population, and that schizophrenia patients show greater variability in spatial than object working memory performance.