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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.
Southward, Cheavens, and Coccaro (2022, Psychological Medicine) conducted an ambitious investigation aimed at determining the nature of the general p factor of psychopathology by considering the correlation between the p factor and five candidate constructs. Generally, in this area of research, the bifactor model is preferred to the second order common factor model. In this commentary, we identify several interpretational issues concerning the bifactor model, which are based on a realistic psychometric view of latent variables. These issues may hamper the study of the nature of p factor model using the bifactor model.
The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, we turn our attention to the next decade of the network approach and propose critical avenues for future research in each of these domains. We argue that this program of research will be best served by working toward two overarching aims: (a) the identification of robust empirical phenomena and (b) the development of formal theories that can explain those phenomena. We recommend specific steps forward within this broad framework and argue that these steps are necessary if the network approach is to develop into a progressive program of research capable of producing a cumulative body of knowledge about how specific mental disorders operate as causal systems.
Cognition played a pivotal role in the acceleration of technological innovation during the Industrial Revolution. Growing affluence may have provided favourable environmental conditions for a boost in cognition, enabling individuals to tackle more complex (industrial) problems. Dynamical systems thinking may provide useful tools to describe sudden transitions like the Industrial Revolution, by modelling the recursive feedback between psychology and environment.
Psychosis spectrum disorder is a heterogeneous, multifactorial clinical phenotype, known to have a high heritability, only a minor portion of which can be explained by molecular measures of genetic variation. This study proposes that the identification of genetic variation underlying psychotic disorder may have suffered due to issues in the psychometric conceptualization of the phenotype. Here we aim to open a new line of research into the genetics of mental disorders by explicitly incorporating genes into symptom networks. Specifically, we investigate whether links between a polygenic risk score (PRS) for schizophrenia and measures of psychosis proneness can be identified in a network model.
We analyzed data from n = 2180 subjects (controls, patients diagnosed with a non-affective psychotic disorder, and the first-degree relatives of the patients). A network structure was computed to examine associations between the 42 symptoms of the Community Assessment of Psychic Experiences (CAPE) and the PRS for schizophrenia.
The resulting network shows that the PRS is directly connected to the spectrum of positive and depressive symptoms, with the items conspiracy and no future being more often located on predictive pathways from PRS to other symptoms.
To our knowledge, the current exploratory study provides a first application of the network framework to the field of behavior genetics research. This allows for a novel outlook on the investigation of the relations between genome-wide association study-based PRSs and symptoms of mental disorders, by focusing on the dependencies among variables.
We address the commentaries on our target article in terms of four major themes. First, we note that virtually all commentators agree that mental disorders are not brain disorders in the common interpretation of these terms, and establish the consensus that explanatory reductionism is not a viable thesis. Second, we address criticisms to the effect that our article was misdirected or aimed at a straw man; we argue that this is unlikely, given the widespread communication of reductionist slogans in psychopathology research and society. Third, we tackle the question of whether intentionality, extended systems, and multiple realizability are as problematic as claimed in the target article, and we present a number of nuances and extensions with respect to our article. Fourth, we discuss the question of how the network approach should incorporate biological factors, given that wholesale reductionism is an unlikely option.
Studies on the association between depression and dementia risk mostly use sum scores on depression questionnaires to model symptomatology severity. Since individual items may contribute differently to this association, this approach has limited validity.
We used network analysis to investigate the functioning of individual Geriatric Depression Scale (GDS-15) items, of which, based on studies that used factor analysis, 3 are generally considered to measure apathy (GDS-3A) and 12 depression (GDS-12D). Functional disability and future dementia were also included in our analysis. Data were extracted from 3229 participants of the Prevention of Dementia by Intensive Vascular care trial (preDIVA), analyzed as a single cohort, yielding 20,542 person-years of observation. We estimated a sparse network by only including connections between variables that could not be accounted for by variance in other variables. For this, we used a repeated L1 regularized regression procedure.
This procedure resulted in a selection of 59/136 possible connections. GDS-3A items were strongly connected to each other and with varying strength to several GDS-12D items. Functional disability was connected to all three GDS-3A items and the GDS-12D items “helplessness” and “worthlessness”. Future dementia was only connected to the GDS-12D item “memory problems”, which was in turn connected to the GDS-12D items “unhappiness” and “helplessness” and all three GDS-3A items.
Network analysis reveals interesting relationships between GDS items, functional disability and dementia risk. We discuss what implications our results may have for (future) research on the associations between depression and/or apathy with dementia.
In the past decades, reductionism has dominated both research directions and funding policies in clinical psychology and psychiatry. The intense search for the biological basis of mental disorders, however, has not resulted in conclusive reductionist explanations of psychopathology. Recently, network models have been proposed as an alternative framework for the analysis of mental disorders, in which mental disorders arise from the causal interplay between symptoms. In this target article, we show that this conceptualization can help explain why reductionist approaches in psychiatry and clinical psychology are on the wrong track. First, symptom networks preclude the identification of a common cause of symptomatology with a neurobiological condition; in symptom networks, there is no such common cause. Second, symptom network relations depend on the content of mental states and, as such, feature intentionality. Third, the strength of network relations is highly likely to depend partially on cultural and historical contexts as well as external mechanisms in the environment. Taken together, these properties suggest that, if mental disorders are indeed networks of causally related symptoms, reductionist accounts cannot achieve the level of success associated with reductionist disease models in modern medicine. As an alternative strategy, we propose to interpret network structures in terms of D. C. Dennett's (1987) notion of real patterns, and suggest that, instead of being reducible to a biological basis, mental disorders feature biological and psychological factors that are deeply intertwined in feedback loops. This suggests that neither psychological nor biological levels can claim causal or explanatory priority, and that a holistic research strategy is necessary for progress in the study of mental disorders.
Lindquist et al. present a strong case for a constructionist account of emotion. First, we elaborate on the ramifications that a constructionist account of emotions might have for psychiatric disorders with emotional disturbances as core elements. Second, we reflect on similarities between Lindquist et al.'s model and recent attempts at formulating psychiatric disorders as networks of causally related symptoms.
Jones & Love (J&L) suggest that Bayesian approaches to the explanation of human behavior should be constrained by mechanistic theories. We argue that their proposal misconstrues the relation between process models, such as the Bayesian model, and mechanisms. While mechanistic theories can answer specific issues that arise from the study of processes, one cannot expect them to provide constraints in general.
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.
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.
We argue that neural networks for semantic cognition, as proposed by Rogers & McClelland (R&M), do not acquire semantics and therefore cannot be the basis for a theory of semantic cognition. The reason is that the neural networks simply perform statistical categorization procedures, and these do not require any semantics for their successful operation. We conclude that this has severe consequences for the semantic cognition views of R&M.
Scientific theories can be viewed as attempts to explain phenomena by showing how they would arise, if certain assumptions concerning the structure of the world were true. Such theories invariably involve a reference to theoretical entities and attributes. Theoretical attributes include such things as electrical charge and distance in physics, inclusive fitness and selective pressure in biology, brain activity and anatomic structure in neuroscience, and intelligence and developmental stages in psychology. These attributes are not subject to direct observation but require an inferential process by which the researcher infers positions of objects on the attribute on the basis of a set of observations.
To make such inferences, one needs to have an idea of how different observations map on to different positions on the attribute (which, after all, is not itself observable). This requires a measurement model. A measurement model explicates how the structure of theoretical attributes relates to the structure of observations. For instance, a measurement model for temperature may stipulate how the level of mercury in a thermometer is systematically related to temperature, or a measurement model for intelligence may specify how IQ scores are related to general intelligence.
The reliance on a process of measurement and the associated measurement model usually involves a degree of uncertainty; the researcher assumes, but cannot know for sure, that a measurement procedure is appropriate in a given situation.
An effective restructuring of the social sciences around the evolutionary model requires that evolutionary theory has explanatory power with respect to the spread of cultural traits: The causal mechanisms involved should be structurally analogous to those of biological evolution. I argue that this is implausible because phenotypical consequences of cultural traits are not causally relevant to their chances of “survival.”
It may be that the task of the new psychometrics is impossible; that fundamental measures will never be constructed. If this is the case, then the truth must be faced that perhaps psychology can never be a science …
Paul Kline, 1998
In the 1930s, the British Association for the Advancement of Science installed a number of its members with a most peculiar task: to decide whether or not there was such a thing as measurement in psychology. The commission, consisting of psychologists and physicists (among the latter was Norman Campbell, famous for his philosophical work on measurement), was unable to reach unanimous agreement. However, a majority of its members concluded that measurement in psychology was impossible; Campbell (cited in Narens and Luce, 1986, p. 186), for example, asked ‘why do not psychologists accept the natural and obvious conclusion that subjective measurements (…) cannot be the basis of measurement’. Similarly, Guild (cited in Reese, 1943, p. 6) stated that ‘to insist on calling these other processes [i.e., attempts at psychological measurement] measurement adds nothing to their actual significance, but merely debases the coinage of verbal intercourse. Measurement is not a term with some mysterious inherent meaning, part of which may be overlooked by the physicists and may be in course of discovery by psychologists.’ For this reason, Guild concluded that using the term ‘measurement’ to cover quantitative practices in psychology ‘does not broaden its meaning but destroys it’.
Psychological measurement plays an important role in modern society. Teachers have schoolchildren tested for dyslexia or hyperactivity, parents have their children's interests and capacities assessed by commercial research bureaus, countries test entire populations of pupils to decide who goes to which school or university, and corporate firms hire other corporate firms to test the right person for the job. The diversity of psychological characteristics measured in such situations is impressive. There exist tests for measuring an enormous range of capacities, abilities, attitudes, and personality factors; these tests are said to measure concepts as diverse as intelligence, extraversion, quality of life, client satisfaction, neuroticism, schizophrenia, and amnesia. The ever increasing popularity of books of the test-your-emotional-intelligence variety has added to the acceptance of psychological testing as an integral element of society.
When we shift our attention from the larger arena of society to the specialized disciplines within scientific psychology, the list of measurable psychological attributes does not become shorter but longer. Within the larger domain of intelligence measurement, we then encounter various subdomains of research where subjects are being probed for their levels of spatial, verbal, numerical, emotional, and perceptual intelligence; from the literature on personality research, we learn that personality is carved up into the five factors of extraversion, neuroticism, conscientiousness, openness to experience, and agreeableness, each of these factors themselves being made up of more specific subfactors; and in clinical psychology we discover various subtypes of schizophrenia, dyslexia, and depression, each of which can be assessed with a numerous variety of psychological tests.
Nothing, not even real data, can contradict classical test theory …
Philip Levy, 1969
In September 1888, Francis Ysidro Edgeworth read a paper before Section F of the British Association at Bath, in which he unfolded some ideas that would profoundly influence psychology. In this paper, he suggested that the theory of errors, at that point mainly used in physics and astronomy, could also be applied to mental test scores. The paper's primary example concerned the evaluation of student essays. Specifically, Edgeworth (1888, p. 602) argued that ‘… it is intelligible to speak of the mean judgment of competent critics as the true judgment; and deviations from that mean as errors’. Edgeworth's suggestion, to decompose observed test scores into a ‘true score’ and an ‘error’ component, was destined to become the most famous equation in psychological measurement: Observed = True + Error.
In the years that followed, the theory was refined, axiomatized, and extended in various ways, but the axiomatic system that is now generally presented as classical test theory was introduced by Novick (1966), and formed the basis of the most articulate exposition of the theory to date: the seminal work by Lord and Novick (1968). Their treatment of the classical test model, unrivalled in clarity, precision, and scope, is arguably the most influential treatise on psychological measurement in the history of psychology.
About five decades ago, the visionary Dutch psychologist A. D. De Groot started building an extraordinary academic group at the University of Amsterdam. It consisted of psychometricians, statisticians, philosophers of science, and psychologists with a general methodological orientation. The idea was to approach methodological problems in psychology from the various angles these different specialists brought to the subject matter. By triangulating their viewpoints, methodological problems were to be clarified, pinpointed, and solved. This idea is in several respects the basis for this book. At an intellectual level, the research reported here is carried out exactly along the lines De Groot envisaged, because it applies insights from psychology, philosophy of science, and psychometrics to the problem of psychological measurement. At a more practical level, I think that, if De Groot had not founded this group, the book now before you would not have existed. For the people in the psychological methods department both sparked my interests in psychometrics and philosophy of science, and provided me with the opportunity to start out on the research that is the basis for this book. Hence, I thank De Groot for his vision, and the people in the psychological methods group for creating such a great intellectual atmosphere.