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 ofreal 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. (shrink)

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. (shrink)

Is it possible to measure psychological attributes like intelligence, personality and attitudes and if so, how does that work? What does the term 'measurement' mean in a psychological context? This fascinating and timely book discusses these questions and investigates the possible answers that can be given response. Denny Borsboom provides an in-depth treatment of the philosophical foundations of widely used measurement models in psychology. The theoretical status of classical test theory, latent variable theory and positioned in terms of the underlying (...) philosophy of science. Special attention is devoted to the central concept of test validity and future directions to improve the theory and practice of psychological measurement are outlined. (shrink)

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 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.

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. (shrink)

The literature on thought experiments has been mainly concernedwith thought experiments that are directed at a theory, be it in aconstructive or a destructive manner. This has led somephilosophers to argue that all thought experiments can beformulated as arguments. The aim of this paper is to drawattention to a type of thought experiment that is not directed ata theory, but fulfills a specific function within a theory. Suchthought experiments are referred to as functional thoughtexperiments, and they are routinely used in (...) applied statistics. An example is given from frequentist statistics, where a thoughtexperiment is required to establish the probability space. It isconcluded that (a) not all thought experiments can be formulated asarguments, and (b) the role of thought experiments is more generaland more important to scientific reasoning than has previouslybeen recognized. (shrink)

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.” (Published Online November 9 2006).

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. (shrink)

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, these psychometric instruments were considered a means to create a new social order. In contrast, contemporary psychometrics - due to 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 utility of a model is more important than its truth. Our goal is not to criticize psychometrics for employing these values, but rather to bring them into the open and to show that these values are not inevitable and are in need of systematic evaluation. (shrink)

According to Oude Maatman (2020), our recent suggestion (Borsboom et al., 2019) that symptom networks are irreducible because they rely on folk psychological descriptions, threatens to undermine the main achievements of the network approach. In this article, we take up Oude Maatman’s challenge and develop an argument showing in what sense folk psychological concepts describe features of reality, and what it means to say that folk psychology is a causal language.

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.

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.

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.

Asay (2018) criticizes our contention that psychologists do best to adhere to a substantive theory of correspondence truth. He argues that deflationary theory can serve the same purposes as correspondence theory. In the present article we argue that (a) scientific realism, broadly construed, requires a version of correspondence theory and (b) contrary to Asay’s suggestion, correspondence theory does have important additional resources over deflationary accounts in its ability to support generalizations over classes of true sentences.