Publications

The explanation of psychological phenomena is a central aim of psychological science. However, the nature of explanation and the processes by which we evaluate whether a theory explains a phenomenon are often unclear. Consequently, it is often unknown whether a given psychological theory indeed explains a phenomenon. We address this shortcoming by proposing a productive account of explanation: a theory explains a phenomenon to some degree if and only if a formal model of the theory produces the statistical pattern representing the phenomenon. Using this account, we outline a workable methodology of explanation: (a) explicating a verbal theory into a formal model, (b) representing phenomena as statistical patterns in data, and (c) assessing whether the formal model produces these statistical patterns. In addition, we provide three major criteria for evaluating the goodness of an explanation (precision, robustness, and empirical relevance), and examine some cases of explanatory breakdowns. Finally, we situate our framework within existing theories of explanation from philosophy of science and discuss how our approach contributes to constructing and developing better psychological theories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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Over the past decade there has been a surge of empirical research investigating mental disorders as complex systems. In this paper, 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.

The present study investigated the psychopathological processes of post-traumatic stress disorder (PTSD) following the network approach to psychopathology. The directed acyclic graph model was employed to analyse a large longitudinal data-set of Chinese children and adolescents exposed to a destructive earthquake. It was found that intrusion symptoms were first activated by trauma exposure, and subsequently activated other PTSD symptoms. The data are consistent with the idea that symptoms may form a self-sustaining dynamic network by interacting with each other to promote or maintain the chronicity of PTSD. The findings advance the current understanding about the psychopathological processes of PTSD, and inform further research and clinical practices on post-traumatic psychopathology.

To advance our understanding, prediction, and treatment of psychopathology, researchers have recently suggested a paradigm shift towards dynamical systems theory for conceptualizing mental health and illness. Importantly, in dynamical systems, the pathways into states (e.g., mental illness) are rarely the same as pathways out. Yet, current theories of psychopathology implicitly assume path symmetry: they almost exclusively outline paths towards mental illness, but rarely (if ever) describe resilience or recovery paths towards health. In this Viewpoint, we illustrate how this striking assumption of path symmetry is misaligned with complex dynamical systems, restricts the scope of research, and limits our understanding and treatment of mental disorders. We call for observational, conceptual, formal theoretical, and experimental investigations of successful transitions out of psychopathology to advance the science of mental health and illness as complex dynamical systems.

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.

The comparison of idiographic network structures in order to determine the presence of heterogeneity is a challenging endeavor in many applied settings. Previously, researchers eyeballed idiographic networks, computed correlations between idiographic networks, as well as used techniques that make use of the multilevel structure of the data (e.g., GIMME and mlVAR) to investigate individual differences. However, these methods do not allow for testing the equality of idiographic network structures directly. In this paper, we propose the Individual Network Invariance Test (INIT). INIT extends common model comparison practices in Structural Equation Modeling (SEM) to idiographic network structures to test for (in)equality between idiographic network structures. In a simulation study, we evaluated the performance of INIT on both saturated and pruned idiographic network structures by inspecting the rejection rate of the χ2 difference test and of model selection criteria such as the AIC and BIC. Results show INIT performs adequately when 100 or more data points are available per individual. When applying INIT on saturated networks, AIC performed best as a model selection criteria, while BIC showed better results when applying INIT on pruned networks. In an empirical example, we highlight the possibilities of this new technique, illustrating how INIT provides researchers with a means of testing for (in)equality between idiographic network structures, and within idiographic network structures over time. To conclude, recommendations for empirical researchers are provided.

Network theory, as a theoretical and methodological framework, is energizing many research fields, among which clinical psychology and psychiatry. Fundamental to the network theory of psychopathology is the role of specific symptoms and their interactions. Current statistical tools, however, fail to fully capture this constitutional property. We propose community detection tools as a means to evaluate the complex network structure of psychopathology, free from its original boundaries of distinct disorders. Unique to this approach is that symptoms can belong to multiple communities. Using a large community sample and spanning a broad range of symptoms (Symptom Checklist-90-Revised), we identified 18 communities of interconnected symptoms. The differential role of symptoms within and between communities offers a framework to study the clinical concepts of comorbidity, heterogeneity and hallmark symptoms. Symptoms with many and strong connections within a community, defined as stabilizing symptoms, could be thought of as the core of a community, whereas symptoms that belong to multiple communities, defined as communicating symptoms, facilitate the communication between problem areas. We propose that defining symptoms on their stabilizing and/or communicating role within and across communities accelerates our understanding of these clinical phenomena, central to research and treatment of psychopathology.

A fundamental question in psychotherapy is whether interventions should target client problems (i.e., problem-focused approaches) or client strengths (i.e., strength-focused approaches). In this study, we first propose to address this question from a network perspective on schema modes (i.e., healthy or dysfunctional patterns of co-occurring emotions, cognitions, and behaviors). From this perspective, schema modes mutually influence each other (e.g., healthy modes reduce dysfunctional modes). Recent evidence suggests that changes in modes that are strongly associated to other modes (i.e., central modes) could be associated with greater treatment effects. Therefore, we suggest that research should investigate the relative centrality of healthy and dysfunctional modes. To make an exploratory start, we investigated the cross-sectional network structure of schema modes in a clinical (comprising individuals diagnosed with paranoid, narcissistic, histrionic, and Cluster C personality disorders) and non-clinical sample. Results showed that, in both samples, the Healthy Adult was significantly less central than several dysfunctional modes (e.g., Undisciplined Child and Abandoned and Abused Child). Although our study cannot draw causal conclusions, this finding could suggest that weakening dysfunctional modes (compared to strengthening the Healthy Adult) might be more effective in decreasing other dysfunctional modes. Our study further indicates that several schema modes are negatively associated, which could suggest that decreasing one might increase another. Finally, the Healthy Adult was among the modes that most strongly discriminated between clinical and non-clinical individuals. Longitudinal and experimental research into the network structure of schema modes is required to further clarify the relative influence of schema modes.