Dimensionality vs taxonicity of schizotypy: some new data and challenges ahead

Abstract

Heterogeneity in the expression of schizotypy may arise from underlying dimensional processes or a taxonic population structure. In a 2-phase study, we tested the taxonicity of self-reported schizotypy within a general psychiatric sample (n = 109) and examined taxon validity by testing its association with clinical schizotaxia in follow-up subsamples. Taxometric analyses indicated a taxonic structure (schizotypy prevalence = 38.8%) provided the best description of the underlying population distribution. After a year, schizotypal (n = 14) and nonschizotypal (n = 14) subsamples returned for diagnosis of clinical schizotaxia by assessment of executive functioning, attention, memory, and negative symptoms. Seven patients met diagnostic criteria, all members of the schizotypy class. Schizotypy was associated with impaired attention and memory, more negative symptoms, poorer global functioning, and more extensive psychiatric histories. We reconcile inconsistencies in the literature by discussing threats to the validity of this and similar research on Meehl's taxonomic model of schizotypy, including conceptual limitations of the lexical hypothesis and conventions of factor analysis. Scrutiny of Meehl's model should involve disambiguation and better measurement of the schizotaxia-schizotypy phenotype.

Keywords: latent variable modeling; negative symptoms; neuropsychological impairment; schizophrenia; schizotaxia; schizotypal personality; taxometrics.

Fig. 1.

Phase 1 taxometric and latent profile results (n = 107). (A) The maximum covariance (MAXCOV) analysis plot obtained on the final iteration with 4 indicators (disorganized thought, social fear, hallucinations, and disrupted thought). Dots indicate mean covariance and the line, the loess smoothed mean covariance. (B) Frequencies of Bayesian posterior probabilities of schizotypy class membership obtained from the MAXCOV analysis. (C) Latent profile analysis fit statistics for 1–4 classes obtained on 4 indicators. LL, log-likelihood; AIC, Akaike information criterion; BIC, Bayesian information criterion; BSS, sample size-adjusted BIC.

Fig. 2.

Mean-above-minus-below-a-cut (MAMBAC) difference plot (n = 107) showing the observed MAMBAC curve (solid line) and how this compares to the 95% CIs for the mean MAMBAC curves estimated from 100 dimensional (dashed lines) and 100 taxonic (shaded area) simulated distributions of the same sample size.