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Aggression in inpatients with psychotic disorders is harmful to patients and health care professionals. The current study introduces a novel approach for assessing short-term sequences of different types of aggression. Occurrence and type of aggressive behavior was assessed retrospectively by reviewing hospital charts in a sample of 120 inpatients with psychotic disorders, admitted to the psychiatric wards of an academic hospital using the Modified Overt Aggression Scale (MOAS). Behavioral sequences of verbal aggression, physical aggression against objects, physical aggression against oneself and physical aggression against others were analyzed by using Markov models, a statistical technique providing the probabilities of transferring from one state to another. The Markov models showed that when patients behave aggressively, they are likely to either show the same type of aggression or to be non-aggressive consecutively. Patients are, however, unlikely to subsequently show another type of aggression. Non-aggressive behavior is very unlikely to result in physical aggression or aggression against objects. The current study introduced a novel approach on how to investigate aggressive behavior in patients with psychotic disorders. Replication of our results in a bigger sample is needed to reliably develop a day-to-day risk assessment tool for aggressive behavior. [ABSTRACT FROM AUTHOR]

Network approaches to psychometric constructs, in which constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure that pertains to a construct, to a more comparative stance, in which the goal is to compare network structures across populations. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT), which uses resampling-based permutation testing to compare network structures from two independent, cross-sectional data sets on invariance of (a) network structure, (b) edge (connection) strength, and (c) global strength. Performance of NCT is evaluated in simulations that show NCT to perform well in various circumstances for all three tests: The Type I error rate is close to the nominal significance level, and power proves sufficiently high if sample size and difference between networks are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed. The network approach, in which psychological constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure, to a more comparative stance, in which the goal is to compare network structures across groups. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT). NCT is a statistical test that compares two network structures on three types of characteristics. Performance of NCT is evaluated by means of a simulation study. Simulated data shows that NCT performs well in various circumstances for all three tests: when the groups are simulated to be similar, the error rate (i.e., NCT indicating that they are different, while the simulated networks are similar) is adequately low, and when the groups are simulated to be different, the ability to detect a difference is sufficiently high when the difference between simulated networks and the sample size are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed. [ABSTRACT FROM AUTHOR]

Gaussian mixture models (GMMs) are a popular and versatile tool for exploring heterogeneity in multivariate continuous data. Arguably the most popular way to estimate GMMs is via the expectation–maximization (EM) algorithm combined with model selection using the Bayesian information criterion (BIC). If the GMM is correctly specified, this estimation procedure has been demonstrated to have high recovery performance. However, in many situations, the data are not continuous but ordinal, for example when assessing symptom severity in medical data or modeling the responses in a survey. For such situations, it is unknown how well the EM algorithm and the BIC perform in GMM recovery. In the present paper, we investigate this question by simulating data from various GMMs, thresholding them in ordinal categories and evaluating recovery performance. We show that the number of components can be estimated reliably if the number of ordinal categories and the number of variables is high enough. However, the estimates of the parameters of the component models are biased independent of sample size. Finally, we discuss alternative modeling approaches which might be adopted for the situations in which estimating a GMM is not acceptable. [ABSTRACT FROM AUTHOR]

Simultaneous interpreting (SI) is a complex skill, where language comprehension and production take place at the same time in two different languages. In this study we identified some of the basic cognitive skills involved in SI, focusing on the roles of memory and lexical retrieval. We administered a reading span task in two languages and a verbal digit span task in the native language to assess memory capacity, and a picture naming and a word translation task to tap the retrieval time of lexical items in two languages, and we related performance on these four tasks to interpreting skill in untrained bilinguals. The results showed that word translation and picture naming latencies correlate with interpreting performance. Also digit span and reading span were associated with SI performance, only less strongly so. A graphical models analysis indicated that specifically word translation efficiency and working memory form independent subskills of SI performance in untrained bilinguals.

Over the past decade, there has been a surge of empirical research investigating mental disorders as complex systems. In this article, we investigate how to best make use of this growing body of empirical research and move the field toward its fundamental aims of explaining, predicting, and controlling psychopathology. We first review the contemporary philosophy of science literature on scientific theories and argue that fully achieving the aims of explanation, prediction, and control requires that we construct formal theories of mental disorders: theories expressed in the language of mathematics or a computational programming language. We then investigate three routes by which one can use empirical findings (i.e., data models) to construct formal theories: (a) using data models themselves as formal theories, (b) using data models to infer formal theories, and (c) comparing empirical data models to theory-implied data models in order to evaluate and refine an existing formal theory. We argue that the third approach is the most promising path forward. We conclude by introducing the abductive formal theory construction (AFTC) framework, informed by both our review of philosophy of science and our methodological investigation. We argue that this approach provides a clear and promising way forward for using empirical research to inform the generation, development, and testing of formal theories both in the domain of psychopathology and in the broader field of psychological science. [ABSTRACT FROM AUTHOR]

This study examines how broad attitude networks are affected by tailored interventions aimed at variables selected based on their connectiveness with other variables. We first computed a broad attitude network based on a large-scale cross-sectional COVID-19 survey (N = 6,093). Over a period of approximately 10 weeks, participants were invited five times to complete this survey, with the third and fifth wave including interventions aimed at manipulating specific variables in the broad COVID-19 attitude network. Results suggest that targeted interventions that yield relatively strong effects on variables central to a broad attitude network have downstream effects on connected variables, which can be partially explained by the variables the interventions were aimed at. We conclude that broad attitude network structures can reveal important relations between variables that can help to design new interventions. [ABSTRACT FROM AUTHOR]

The majority of commentators agree on one thing: Our network approach might be the prime candidate for offering a new perspective on the origins of mental disorders. In our response, we elaborate on refinements (e.g., cognitive and genetic levels) and extensions (e.g., to Axis II disorders) of the network model, as well as discuss ways to test its validity. doi: 10.1017/S0140525X10000920

The pivotal problem of comorbidity research lies in the psychometric foundation it rests on, that is, latent variable theory, in which a mental disorder is viewed as a latent variable that causes a constellation of symptoms. From this perspective, comorbidity is a (bi)directional relationship between multiple latent variables. We argue that such a latent variable perspective encounters serious problems in the study of comorbidity, and offer a radically different conceptualization in terms of a network approach, where comorbidity is hypothesized to arise from direct relations between symptoms of multiple disorders. We propose a method to visualize comorbidity networks and, based on an empirical network for major depression and generalized anxiety, we argue that this approach generates realistic hypotheses about pathways to comorbidity, overlapping symptoms, and diagnostic boundaries, that are not naturally accommodated by latent variable models: Some pathways to comorbidity through the symptom space are more likely than others; those pathways generally have the same direction (i.e., from symptoms of one disorder to symptoms of the other); overlapping symptoms play an important role in comorbidity; and boundaries between diagnostic categories are necessarily fuzzy. Keywords: comorbidity; complex networks; generalized anxiety; latent variable models; major depression doi: 10.1017/S0140525X09991567

Several methods [model selection procedures (MSPs)] to determine the number of sources in electroencephalogram (EEG) and magnetoencphalogram (MEG) data have previously been investigated in an instantaneous analysis. In this paper, these MSPs are extended to a spatio-temporal analysis if possible. It is seen that the residual variance (RV) tends to overestimate the number of sources. The Akaike information criterion (AIC) and the Wald test on amplitudes (WA) and the Wald test on locations (WL) have the highest probabilities of selecting the correct number of sources. The WA has the advantage that it offers the opportunity to test which source is active at which time sample. Index Terms--Akaike criterion, Bayesian criterion, EEG, MEG, model order selection, source localization, testing source activity.

Interactions between cortical areas are crucial for cognitive functioning. Methods currently in use to assess such interactions are not well suited for this task because they lack timing precision, localization precision, or both. We present a method for simultaneous estimation of source location and orientation parameters and cross-spectral parameters to overcome these problems. Different estimators are evaluated for their performance. From a simulation study, we conclude that the estimators derived from the maximum-likelihood procedure have good statistical properties with moderate sample sizes, whereas those obtained from the generalized least squares procedure are biased and give poor-quality standard errors. The method is illustrated with visually evoked field data, with inconclusive results Index Terms--Coherence, covariance structures, cross-spectrum structures, EEG, frequency domain, functional connectivity, MEG, source modeling.

Estimating causal relations between two or more variables is an important topic in psychology. Establishing a causal relation between two variables can help us in answering that question of why something happens. However, using solely observational data are insufficient to get the complete causal picture. The combination of observational and experimental data may give adequate information to properly estimate causal relations. In this study, we consider the conditions where estimating causal relations might work and we show how well different algorithms, namely the Peter and Clark algorithm, the Downward Ranking of Feed-Forward Loops algorithm, the Transitive Reduction for Weighted Signed Digraphs algorithm, the Invariant Causal Prediction (ICP) algorithm and the Hidden Invariant Causal Prediction (HICP) algorithm, determine causal relations in a simulation study. Results showed that the ICP and the HICP algorithms perform best in most simulation conditions. We also apply every algorithm to an empirical example to show the similarities and differences between the algorithms. We believe that the combination of the ICP and the HICP algorithm may be suitable to be used in future research. [ABSTRACT FROM AUTHOR]

The Ising model is a model for pairwise interactions between binary variables that has become popular in the psychological sciences. It has been first introduced as a theoretical model for the alignment between positive (1) and negative (−1) atom spins. In many psychological applications, however, the Ising model is defined on the domain {0, 1} instead of the classical domain { − 1 , 1 }. While it is possible to transform the parameters of the Ising model in one domain to obtain a statistically equivalent model in the other domain, the parameters in the two versions of the Ising model lend themselves to different interpretations and imply different dynamics, when studying the Ising model as a dynamical system. In this tutorial paper, we provide an accessible discussion of the interpretation of threshold and interaction parameters in the two domains and show how the dynamics of the Ising model depends on the choice of domain. Finally, we provide a transformation that allows one to transform the parameters in an Ising model in one domain into a statistically equivalent Ising model in the other domain. [ABSTRACT FROM AUTHOR]

Pairwise network models such as the Gaussian Graphical Model (GGM) are a powerful and intuitive way to analyze dependencies in multivariate data. A key assumption of the GGM is that each pairwise interaction is independent of the values of all other variables. However, in psychological research, this is often implausible. In this article, we extend the GGM by allowing each pairwise interaction between two variables to be moderated by (a subset of) all other variables in the model, and thereby introduce a Moderated Network Model (MNM). We show how to construct MNMs and propose an ℓ 1 -regularized nodewise regression approach to estimate them. We provide performance results in a simulation study and show that MNMs outperform the split-sample based methods Network Comparison Test (NCT) and Fused Graphical Lasso (FGL) in detecting moderation effects. Finally, we provide a fully reproducible tutorial on how to estimate MNMs with the R-package mgm and discuss possible issues with model misspecification. [ABSTRACT FROM AUTHOR]

Networks are gaining popularity as an alternative to latent variable models for representing psychological constructs. Whereas latent variable approaches introduce unobserved common causes to explain the relations among observed variables, network approaches posit direct causal relations between observed variables. While these approaches lead to radically different understandings of the psychological constructs of interest, recent articles have established mathematical equivalences that hold between network models and latent variable models. We argue that the fact that for any model from one class there is an equivalent model from the other class does not mean that both models are equally plausible accounts of the data-generating mechanism. In many cases the constraints that are meaningful in one framework translate to constraints in the equivalent model that lack a clear interpretation in the other framework. Finally, we discuss three diverging predictions for the relation between zero-order correlations and partial correlations implied by sparse network models and unidimensional factor models. We propose a test procedure that compares the likelihoods of these models in light of these diverging implications. We use an empirical example to illustrate our argument. [ABSTRACT FROM AUTHOR]

Time series of individual subjects have become a common data type in psychological research. These data allow one to estimate models of within-subject dynamics, and thereby avoid the notorious problem of making within-subjects inferences from between-subjects data, and naturally address heterogeneity between subjects. A popular model for these data is the Vector Autoregressive (VAR) model, in which each variable is predicted by a linear function of all variables at previous time points. A key assumption of this model is that its parameters are constant (or stationary) across time. However, in many areas of psychological research time-varying parameters are plausible or even the subject of study. In this tutorial paper, we introduce methods to estimate time-varying VAR models based on splines and kernel-smoothing with/without regularization. We use simulations to evaluate the relative performance of all methods in scenarios typical in applied research, and discuss their strengths and weaknesses. Finally, we provide a step-by-step tutorial showing how to apply the discussed methods to an openly available time series of mood-related measurements. [ABSTRACT FROM AUTHOR]

Summary: A new method is proposed to estimate graphical models simultaneously from data obtained at different coarseness scales. Starting from a predefined scale the method offers the possibility to zoom in or out over scales on particular edges. The estimated graphs over the different scales have similar structures although their level of sparsity depends on the scale at which estimation takes place. The method makes it possible to evaluate the evolution of the graphs from the coarsest to the finest scale or vice versa. We select an optimal coarseness scale to be used for further analysis. Simulation studies and an application on functional magnetic resonance brain imaging data show the method's performance in practice. [ABSTRACT FROM AUTHOR]

Mental disorders like major depressive disorder can be modeled as complex dynamical systems. In this study we investigate the dynamic behavior of individuals to see whether or not we can expect a transition to another mood state. We introduce a mean field model to a binomial process, where we reduce a dynamic multidimensional system (stochastic cellular automaton) to a one-dimensional system to analyse the dynamics. Using maximum likelihood estimation, we can estimate the parameter of interest which, in combination with a bifurcation diagram, reflects the expectancy that someone has to transition to another mood state. After numerically illustrating the proposed method with simulated data, we apply this method to two empirical examples, where we show its use in a clinical sample consisting of patients diagnosed with major depressive disorder, and a general population sample. Results showed that the majority of the clinical sample was categorized as having an expectancy for a transition, while the majority of the general population sample did not have this expectancy. We conclude that the mean field model has great potential in assessing the expectancy for a transition between mood states. With some extensions it could, in the future, aid clinical therapists in the treatment of depressed patients. [ABSTRACT FROM AUTHOR]