Fitting multivariage normal finite mixtures subject to structural equation modeling

This paper is about fitting multivariate normal mixture distributions subject to structural equation modeling. The general model comprises common factor and structural regression models. The introduction of covariance and mean structure models reduces the number of parameters to be estimated in fitting the mixture and enables one to investigate a variety of substantive hypotheses concerning the differences between the components in the mixture. Within the general model, individual parameters can be subjected to equality, nonlinear and simple bounds constraints. Confidence intervals are based on the inverse of the Hessian and on the likelihood profile. Several illustrations are given and results of a simulation study concerning the confidence intervals are reported.     

A framework for ML estimation of parameters of (mixtures of) common reaction time distributions given optional truncation or censoring

We present a framework for distributional reaction time (RT) analysis, based on maximum likelihood (ML) estimation. Given certain information relating to chosen distribution functions, one can estimate the parameters of these distributions and of finite mixtures of these distributions. In addition, left and/or right censoring or truncation may be imposed. Censoring and truncation are useful methods by which to accommodate outlying observations, which are a pervasive problem in RT research. We consider five RT distributions: the Weibull, the ex-Gaussian, the gamma, the log-normal, and the Wald. We employ quasi-Newton optimization to obtain ML estimates. Multicase distributional analyses can be carried out, which enable one to conduct detailed (across or within subjects) comparisons of RT data by means of loglikelihood difference tests. Parameters may be freely estimated, estimated subject to boundary constraints, constrained to be equal (within or over cases), or fixed. To demonstrate the feasibility of ML estimation and to illustrate some of the possibilities offered by the present approach, we present three small simulation studies. In addition, we present three illustrative analyses of real data.     

EZ does it! Extensions of the EZ-diffusion model

In this rejoinder, we address two of Ratcliff’s main concerns with respect to the EZ-diffusion model (Ratcliff, 2008). First, we introduce “robust-EZ,” a mixture model approach to achieve robustness against the presence of response contaminants that might otherwise distort parameter estimates. Second, we discuss an extension of the EZ model that allows the estimation of starting point as an additional parameter. Together with recently developed, user-friendly software programs for fitting the full diffusion model (Vandekerckhove & Tuerlinckx, 2007; Voss & Voss, 2007), the development of the EZ model and its extensions is part of a larger effort to make diffusion model analyses accessible to a broader audience, an effort that is long overdue.     

The power to detect sex differences in IQ test scores using Multi-Group Covariance and Means Structure Analyses

Research into sex differences in general intelligence, g, has resulted in two opposite views. In the first view, a g-difference is nonexistent, while in the second view, g is associated with a male advantage. Past research using Multi-Group Covariance and Mean Structure Analysis (MG-CMSA) found no sex difference in g. This failure raised the question whether the g-difference is truly absent or whether MG-CMSA lacked statistical power to detect it. The present study used the likelihood ratio test to investigate the power to detect a g-difference in the WAIS-III factor structure with MG-CMSA. Various situations were examined including those reported in the literature. Results showed that power varies greatly among different scenarios. The scenarios based on previous results were associated with power coefficients of about 0.5–0.6. Implications of these findings are discussed in the light of research into sex differences in IQ.     

Heteroscedastic one‐factor models and marginal maximum likelihood estimation

In the present paper, a general class of heteroscedastic one‐factor models is considered. In these models, the residual variances of the observed scores are explicitly modelled as parametric functions of the one‐dimensional factor score. A marginal maximum likelihood procedure for parameter estimation is proposed under both the assumption of multivariate normality of the observed scores conditional on the single common factor score and the assumption of normality of the common factor score. A likelihood ratio test is derived, which can be used to test the usual homoscedastic one‐factor model against one of the proposed heteroscedastic models. Simulation studies are carried out to investigate the robustness and the power of this likelihood ratio test. Results show that the asymptotic properties of the test statistic hold under both small test length conditions and small sample size conditions. Results also show under what conditions the power to detect different heteroscedasticity parameter values is either small, medium, or large. Finally, for illustrative purposes, the marginal maximum likelihood estimation procedure and the likelihood ratio test are applied to real data.     

Altered emotional decision-making in prisoners with borderline personality disorder

Previous studies have identified neuropsychological deficits in individuals with antisocial personality disorder and/or psychopathy. Few studies have examined neuropsychological functioning in individuals with borderline personality disorder (BPD), and no studies have yet investigated cognitive and emotional function in male prisoners with BPD. In this study, we compared the risky decision-making of 17 participants with a history of serious violent or sexual offenses and a diagnosis of DSM-IV BPD with that of 17 participants with similar offending histories but personality disorders other than BPD. Those with BPD exhibited altered processing of information about potential losses (punishment) when the probability of gains (reward) was high; they also increased their choice of risky options even in circumstances where this was clearly avoidable. These data suggest that individuals with a diagnosis of BPD and a history of serious offenses have problems integrating different reinforcement signals when choosing between risky actions, perhaps reflecting corticolimbic dysfunction as an underlying mechanism in BPD.     

Does treatment really make psychopaths worse? A review of the evidence

We aimed to evaluate critically the evidence behind the perceived inverse association between the degree of psychopathy as reflected by a high score on the Hare Psychopathy Checklist-Revised (PCL-R) and treatment response. A literature search with the key identifiers of PCL-R (or its derivatives) and treatment response produced 24 studies that were then systematically evaluated. This showed that only three studies were of an appropriate research design to answer the question and of these, none met our standard for an acceptable study. We conclude therefore that the commonly held belief of an inverse relationship between high-scores on the PCL-R and treatment response has not been established.     

Why g is not an adaptation: a comment on Kanazawa (2004)

In S. Kanazawa's evolutionary theory of general intelligence (g), g is presented as a species-typical information-processing mechanism. This conceptualization of g departs radically from the accepted conceptualization of g as a source of individual differences that is manifest in the positive manifold. Kanazawa's theory is thus problematic in the sense that it concerns a purely hypothetical, and empirically unsupported, conceptualization of g. The authors argue that an evolutionary account of g should address it as a source of individual differences--that is, in a manner that is consistent with the empirical support for g.     

Imaging the developing brain: what have we learned about cognitive development?

The human brain undergoes significant changes in both its structural architecture and functional organization across the life span. Advances in neuroimaging techniques over the past decade have allowed us to track these changes safely in the human in vivo. We review the imaging literature on the neurobiology of cognitive development, focusing specifically on cognitive task-dependent changes observed in brain physiology and anatomy across childhood and adolescence. The findings suggest that cortical function becomes fine-tuned with development. Brain regions associated with more basic functions such as sensory and motor processes mature first, followed by association areas involved in top-down control of behavior.