The narcissism spectrum model synthesizes extensive personality, social–psychological, and clinical evidence, building on existing knowledge about narcissistic grandiosity and vulnerability to reveal ...a view of narcissism that respects its clinical origins, embraces the diversity and complexity of its expression, and reflects extensive scientific evidence about the continuity between normal and abnormal personality expression. Critically, the proposed model addresses three key, inter-related problems that have plagued narcissism scholarship for more than a century. These problems can be summarized as follows: (a) What are the key features of narcissism? (b) How are they organized and related to each other? and (c) Why are they organized that way, that is, what accounts for their relationships? By conceptualizing narcissistic traits as manifested in transactional processes between individuals and their social environments, the model enables integration of existing theories of narcissism and thus provides a compelling perspective for future examination of narcissism and its developmental pathways.
Abstract
Recent years have seen an emergence of network modeling applied to moods, attitudes, and problems in the realm of psychology. In this framework, psychological variables are understood to ...directly affect each other rather than being caused by an unobserved latent entity. In this tutorial, we introduce the reader to estimating the most popular network model for psychological data: the partial correlation network. We describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data. We show how to perform these analyses in R and demonstrate the method in an empirical example on posttraumatic stress disorder data. In addition, we discuss the effect of the hyperparameter that needs to be manually set by the researcher, how to handle non-normal data, how to determine the required sample size for a network analysis, and provide a checklist with potential solutions for problems that can arise when estimating regularized partial correlation networks.
Translational Abstract
Recent years have seen an emergence in the use of networks models in psychological research to explore relationships of variables such as emotions, symptoms, or personality items. Networks have become particularly popular in analyzing mental illnesses, as they facilitate the investigation of how individual symptoms affect one-another. This article introduces a particular type of network model: the partial correlation network, and describes how this model can be estimated using regularization techniques from statistical learning. With these techniques, a researcher can gain insight in predictive and potential causal relationships between the measured variables. The article provides a tutorial for applied researchers on how to estimate these models, how to determine the sample size needed for performing such an analysis, and how to investigate the stability of results. We also discuss a list of potential pitfalls when using this methodology.
Moving Forward Fried, Eiko I.; Cramer, Angélique O. J.
Perspectives on psychological science,
11/2017, Letnik:
12, Številka:
6
Journal Article
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Since the introduction of mental disorders as networks of causally interacting symptoms, this novel framework has received considerable attention. The past years have resulted in over 40 scientific ...publications and numerous conference symposia and workshops. Now is an excellent moment to take stock of the network approach: What are its most fundamental challenges, and what are potential ways forward in addressing them? After a brief conceptual introduction, we first discuss challenges to network theory: (1) What is the validity of the network approach beyond some commonly investigated disorders such as major depression? (2) How do we best define psychopathological networks and their constituent elements? And (3) how can we gain a better understanding of the causal nature and real-life underpinnings of associations among symptoms? Next, after a short technical introduction to network modeling, we discuss challenges to network methodology: (4) heterogeneity of samples studied with network analytic models, and (5) a lurking replicability crisis in this strongly data-driven and exploratory field. Addressing these challenges may propel the network approach from its adolescence into adulthood and promises advances in understanding psychopathology both at the nomothetic and idiographic level.
In both child and adult psychiatry, empirical evidence has now accrued to suggest that a single dimension is able to measure a person’s liability to mental disorder, comorbidity among disorders, ...persistence of disorders over time, and severity of symptoms. This single dimension of general psychopathology has been termed “p,” because it conceptually parallels a dimension already familiar to behavioral scientists and clinicians: the “g” factor of general intelligence. As the g dimension reflects low to high mental ability, the p dimension represents low to high psychopathology severity, with thought disorder at the extreme. The dimension of p unites all disorders. It influences present/absent status on hundreds of psychiatric symptoms, which modern nosological systems typically aggregate into dozens of distinct diagnoses, which in turn aggregate into three overarching domains, namely, the externalizing, internalizing, and psychotic experience domains, which finally aggregate into one dimension of psychopathology from low to high: p. Studies show that the higher a person scores on p, the worse that person fares on measures of family history of psychiatric illness, brain function, childhood developmental history, and adult life impairment. A dimension of p may help account for ubiquitous nonspecificity in psychiatry: multiple disorders share the same risk factors and biomarkers and often respond to the same therapies. Here, the authors summarize the history of the unidimensional idea, review modern research into p, demystify statistical models, articulate some implications of p for prevention and clinical practice, and outline a transdiagnostic research agenda.AJP AT 175: Remembering Our Past As We Envision Our FutureOctober 1910: A Study of Association in InsanityGrace Helen Kent and A.J. Rosanoff: "No sharp distinction can be drawn between mental health and mental disease; a large collection of material shows a gradual and not an abrupt transition from the normal state to pathological states.”(Am J Psychiatry 1910; 67(2):317–390)
The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an ...environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
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DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in ...psychology. They view decision making as a process of noisy accumulation of evidence from a stimulus. The standard model assumes that evidence accumulates at a constant rate during the second or two it takes to make a decision. This process can be linked to the behaviors of populations of neurons and to theories of optimality. Diffusion models have been used successfully in a range of cognitive tasks and as psychometric tools in clinical research to examine individual differences. In this review, we relate the models to both earlier and more recent research in psychology.
Reciprocal feedback processes between experience and development are central to contemporary developmental theory. Autoregressive cross-lagged panel (ARCL) models represent a common analytic approach ...intended to test such dynamics. The authors demonstrate that—despite the ARCL model's intuitive appeal—it typically (a) fails to align with the theoretical processes that it is intended to test and (b) yields estimates that are difficult to interpret meaningfully. Specifically, using a Monte Carlo simulation and two empirical examples concerning the reciprocal relation between spanking and child aggression, it is shown that the cross-lagged estimates derived from the ARCL model reflect a weighted—and typically uninterpretable—amalgam of between- and within-person associations. The authors highlight one readily implemented respecification that better addresses these multiple levels of inference.
Bifactor measurement models are increasingly being applied to personality and psychopathology measures (Reise, 2012). In this work, authors generally have emphasized model fit, and their typical ...conclusion is that a bifactor model provides a superior fit relative to alternative subordinate models. Often unexplored, however, are important statistical indices that can substantially improve the psychometric analysis of a measure. We provide a review of the particularly valuable statistical indices one can derive from bifactor models. They include omega reliability coefficients, factor determinacy, construct reliability, explained common variance, and percentage of uncontaminated correlations. We describe how these indices can be calculated and used to inform: (a) the quality of unit-weighted total and subscale score composites, as well as factor score estimates, and (b) the specification and quality of a measurement model in structural equation modeling.
Social scientists are increasingly interested in multilevel hypotheses, data, and statistical models as well as moderation or interactions among predictors. The result is a focus on hypotheses and ...tests of multilevel moderation within and across levels of analysis. Unfortunately, existing approaches to multilevel moderation have a variety of shortcomings, including conflated effects across levels of analysis and bias due to using observed cluster averages instead of latent variables (i.e., "random intercepts") to represent higher-level constructs. To overcome these problems and elucidate the nature of multilevel moderation effects, we introduce a multilevel structural equation modeling (MSEM) logic that clarifies the nature of the problems with existing practices and remedies them with latent variable interactions. This remedy uses random coefficients and/or latent moderated structural equations (LMS) for unbiased tests of multilevel moderation. We describe our approach and provide an example using the publicly available High School and Beyond data with Mplus syntax in Appendix. Our MSEM method eliminates problems of conflated multilevel effects and reduces bias in parameter estimates while offering a coherent framework for conceptualizing and testing multilevel moderation effects.
Social cognitive theory is founded on an agentic perspective. This article reviews the core features of human agency and the individual, proxy, and collective forms in which it is exercised. Agency ...operates through a triadic codetermination process of causation. Knowledge from this line of theorizing is widely applied to effect individual and social change, including worldwide applications that address some of the most urgent global problems.
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