A descriptivist approach to trait conceptualization and inference

Psychosis superspectrum I: Nosology, etiology, and lifespan development

This review describes the Hierarchical Taxonomy of Psychopathology (HiTOP) model of psychosis-related psychopathology, the psychosis superspectrum. The HiTOP psychosis superspectrum was developed to address shortcomings of traditional diagnoses for psychotic disorders and related conditions including low reliability, arbitrary boundaries between psychopathology and normality, high symptom co-occurrence, and heterogeneity within diagnostic categories. The psychosis superspectrum is a transdiagnostic dimensional model comprising two spectra-psychoticism and detachment-which are in turn broken down into fourteen narrow components, and two auxiliary domains-cognition and functional impairment. The structure of the spectra and their components are shown to parallel the genetic structure of psychosis and related traits. Psychoticism and detachment have distinct patterns of association with urbanicity, migrant and ethnic minority status, childhood adversity, and cannabis use. The superspectrum also provides a useful model for describing the emergence and course of psychosis, as components of the superspectrum are relatively stable over time. Changes in psychoticism predict the onset of psychosis-related psychopathology, whereas changes in detachment and cognition define later course. Implications of the superspectrum for genetic, socio-environmental, and longitudinal research are discussed. A companion review focuses on neurobiology, treatment response, and clinical utility of the superspectrum, and future research directions.

What is a psychopathology dimension?

Coherence in the science and practice of mental health assessment depends upon a tight connection between psychopathology concepts that are used and the way those concepts are operationalized and defined. In contrast, the use of the same word to mean more than one thing contributes to incoherence, inefficiency, and confusion. In this paper, we review three possible meanings of the word "dimension" as it relates to the assessment of psychopathology and describe how the indiscriminate use of this word has caused confusion in the general context of the transition to a more evidence-based approach to mental health diagnosis. We attempt to disambiguate the term "dimension" by demarcating three concepts that can be distinguished based on different empirical standards: continuous variables, unidimensional dimensions, and distinct dimensions.

Reliability as Lindley Information

This paper introduces a definition of reliability based on Lindley information, which is the mutual information between an observed measure and latent attribute. This definition reduces to the traditional definition of reliability in the case of normal variables, but can be applied to any joint distribution of observed and latent variables. Importantly, unlike traditional definitions of reliability, this formulation of reliability applies to vector- or matrix-valued estimates and summaries of responses, and therefore generalizes reliability to sets of scores and estimates in addition to individual scores and estimates. This formulation also leads to new bounds for reliability, as well as newly reported relationships between reliability and the traditional Fisher information function familiar in item response theory literature. This form of reliability can be estimated using formulae, or methods used in Bayesian inference such as Markov Chain Monte Carlo (MCMC) depending on the case. Examples based on well-studied datasets are provided, as well as applications to drift-diffusion modeling and randomly-varying intraindividual covariance structures.

Values in Psychometrics

When it originated in the late 19th century, psychometrics was a field with both a scientific and a social mission: Psychometrics provided new methods for research into individual differences and at the same time considered these methods a means of creating a new social order. In contrast, contemporary psychometrics-because of its highly technical nature and its limited involvement in substantive psychological research-has created the impression of being a value-free discipline. In this article, we develop a contrasting characterization of contemporary psychometrics as a value-laden discipline. We expose four such values: that individual differences are quantitative (rather than qualitative), that measurement should be objective in a specific sense, that test items should be fair, and that the utility of a model is more important than its truth. Our goal is not to criticize psychometrics for supporting these values but rather to bring them into the open and to show that they are not inevitable and are in need of systematic evaluation.

Common Factors and Interpretation of the p Factor of Psychopathology

One of the most discussed recent topics in psychopathology research is the p factor of mental illness. This single dimension is understood to measure "a person's liability to mental disorder, comorbidity among disorders, persistence of disorders over time, and severity of symptoms."¹ A recent paper by Constantinou et al.² published in the Journal investigated the external validity of the p factor. We commend the authors for the contribution to the literature and want to highlight two points: (1) the interpretation of p as a causal entity, and (2) selection of bifactor models over alternative models for reasons of superior fit.

Toward an Integrative Psychometric Model of Emotions

Emotions are part and parcel of the human condition, but their nature is debated. Three broad classes of theories about the nature of emotions can be distinguished: affect-program theories, constructionist theories, and appraisal theories. Integrating these broad classes of theories into a unifying theory is challenging. An integrative psychometric model of emotions can inform such a theory because psychometric models are intertwined with theoretical perspectives about constructs. To identify an integrative psychometric model, we delineate properties of emotions stated by emotion theories and investigate whether psychometric models account for these properties. Specifically, an integrative psychometric model of emotions should allow (a) identifying distinct emotions (central in affect-program theories), (b) between- and within-person variations of emotions (central in constructionist theories), and (c) causal relationships between emotion components (central in appraisal theories). Evidence suggests that the popular reflective and formative latent variable models-in which emotions are conceptualized as unobservable causes or consequences of emotion components-cannot account for all properties. Conversely, a psychometric network model-in which emotions are conceptualized as systems of causally interacting emotion components-accounts for all properties. The psychometric network model thus constitutes an integrative psychometric model of emotions, facilitating progress toward a unifying theory.

A Hierarchical Taxonomy of Psychopathology Can Transform Mental Health Research

For more than a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a dimensional approach to the science of mental illness. Here we outline one such dimensional system-the Hierarchical Taxonomy of Psychopathology (HiTOP)-that is based on empirical patterns of co-occurrence among psychological symptoms. We highlight key ways in which this framework can advance mental-health research, and we provide some heuristics for using HiTOP to test theories of psychopathology. We then review emerging evidence that supports the value of a hierarchical, dimensional model of mental illness across diverse research areas in psychological science. These new data suggest that the HiTOP system has the potential to accelerate and improve research on mental-health problems as well as efforts to more effectively assess, prevent, and treat mental illness.

Interpersonal Dynamics in Personality and Personality Disorders

Clinical and basic personality psychologists interact less than they should, given their similar interests. In clinical personality psychology, available evidence supports a transition from the current categorical system to a hierarchical trait scheme for diagnosing the stable features of personality disorder. However, trait models do not capture the dynamic aspects of personality disorders as they have been described in the clinical literature, and thus miss a clinically critical feature of personality pathology. In contrast, basic personality psychologists have coalesced around a consensual structure of individual differences and become increasingly interested in the dynamic processes that underlie and contextualize traits. But trait psychology models are not sufficiently specific to characterize dynamic personality processes. In this paper, I filter clinical descriptions of personality disorders through the lens of interpersonal theory to specify a recursive within–situation interpersonal pattern of motives, affects, behaviours, and perceptions that could contribute to the stable between–situation patterns of maladaptive behaviour of historical interest to both basic and clinical personality psychologists. I suggest that this interpersonal model adds specificity to recent proposals regarding processes in the basic personality literature and has significant potential to advance research on personality dynamics. © 2018 European Association of Personality Psychology

Network Models for Cognitive Development and Intelligence

Cronbach's (1957) famous division of scientific psychology into two disciplines is still apparent for the fields of cognition (general mechanisms) and intelligence (dimensionality of individual differences). The welcome integration of the two fields requires the construction of mechanistic models of cognition and cognitive development that explain key phenomena in individual differences research. In this paper, we argue that network modeling is a promising approach to integrate the processes of cognitive development and (developing) intelligence into one unified theory. Network models are defined mathematically, describe mechanisms on the level of the individual, and are able to explain positive correlations among intelligence subtest scores-the empirical basis for the well-known g-factor-as well as more complex factorial structures. Links between network modeling, factor modeling, and item response theory allow for a common metric, encompassing both discrete and continuous characteristics, for cognitive development and intelligence.

On the Psychometric Study of Human Life History Strategies

This article attends to recent discussions of validity in psychometric research on human life history strategy (LHS), provides a constructive critique of the extant literature, and describes strategies for improving construct validity. To place the psychometric study of human LHS on more solid ground, our review indicates that researchers should (a) use approaches to psychometric modeling that are consistent with their philosophies of measurement, (b) confirm the dimensionality of life history indicators, and (c) establish measurement invariance for at least a subset of indicators. Because we see confirming the dimensionality of life history indicators as the next step toward placing the psychometrics of human LHS on more solid ground, we use nationally representative data and structural equation modeling to test the structure of middle adult life history indicators. We found statistically independent mating competition and Super-K dimensions and the effects of parental harshness and childhood unpredictability on Super-K were consistent with past research. However, childhood socioeconomic status had a moderate positive effect on mating competition and no effect on Super-K, while unpredictability did not predict mating competition. We conclude that human LHS is more complex than previously suggested-there does not seem to be a single dimension of human LHS among Western adults and the effects of environmental components seem to vary between mating competition and Super-K.