This paper offers a philosophical analysis of the connection between mental disorder and suicide risk. In contemporary psychiatry, it is commonly suggested that this connection is a causal connection that has been established through empirical discovery. Herein, I examine the extent to which this claim can be sustained. I argue that the connection between mental disorder and increased suicide risk is not wholly causal but is partly conceptual. This in part relates to the way suicidality is built into the definitions (...) of some psychiatric diagnoses. It also relates to the broader normative assumption that suicidal behavior is by definition mentally disordered behavior. The above has significant epistemological implications, which I explore. I propose that the claim that suicide is connected with mental disorder cannot be justified solely by appealing to empirical evidence but also warrants a justification on conceptual and normative grounds. (shrink)

Despite several criticisms surrounding the DSM classification in psychiatry, a significant bulk of research on mental conditions still operates according to two core assumptions: a) homogeneity, that is the idea that mental conditions are sufficiently homogeneous to justify generalization; b) additive comorbidity, that is the idea that the coexistence of multiple conditions in the same individual can be interpreted as additive. In this paper we take autism research as a case study to show that, despite a plethora of criticism, psychiatric (...) research often continues to operate in accordance with this model. Then we argue that such a model runs into problems once facts about comorbidity are taken into account. Finally, we offer some suggestions on how to tackle the challenge raised by comorbidity and its impact on heterogeneity. To do so, we explore transdiagnostic stratification accounts and network models to show that combining these approaches can move us in the right direction. (shrink)

In recent decades, attempts to explain autism have been frustrated by the heterogeneous nature of its behavioral symptoms and the underlying genetic, neural, and cognitive mechanisms that produce them. This has led some to propose eliminating the category altogether. The eliminativist inference relies on a conception of psychiatric categories as kinds defined by their underlying mechanistic structure. I review the evidence for eliminativism and propose an alternative model of the family of autisms. On this account, autism is a network category (...) defined by a set of idealized exemplars linked by multiple levels of theoretically significant properties. I argue that this network model better captures the empirical phenomena, the historical growth of the category, and the ways the category has been shaped by social norms and interests. Finally, I defend a realist interpretation of network categories against the challenge from eliminativists. (shrink)

In clinical medicine, a diagnosis can offer an explanation of a patient's symptoms by specifying the pathology that is causing them. Diagnoses in psychiatry are also sometimes presented in clinical texts as if they pick out pathological processes that cause sets of symptoms. However, current evidence suggests the possibility that many diagnostic categories in psychiatry are highly causally heterogeneous. For example, major depressive disorder may not be associated with a single type of underlying pathological process, but with a range of (...) different causal pathways, each involving complex interactions of various biological, psychological, and social factors. This paper explores the implications of causal heterogeneity for whether psychiatric diagnoses can be said to serve causal explanatory roles in clinical practice. I argue that while they may fall short of picking out a specific cause of the patient's symptoms, they can nonetheless supply different sorts of clinically relevant causal information. In particular, I suggest that some psychiatric diagnoses provide negative information that rules out certain causes, some provide approximate or disjunctive information about the range of possible causal processes, and some provide causal information about the relations between the symptoms themselves. (shrink)

Major depression (MD) is a highly heterogeneous diagnostic category. Diverse symptoms such as sad mood, anhedonia, and fatigue are routinely added to an unweighted sum-score, and cutoffs are used to distinguish between depressed participants and healthy controls. Researchers then investigate outcome variables like MD risk factors, biomarkers, and treatment response in such samples. These practices presuppose that (1) depression is a discrete condition, and that (2) symptoms are interchangeable indicators of this latent disorder. Here I review these two assumptions, elucidate (...) their historical roots, show how deeply engrained they are in psychological and psychiatric research, and document that they contrast with evidence. Depression is not a consistent syndrome with clearly demarcated boundaries, and depression symptoms are not interchangeable indicators of an underlying disorder. Current research practices lump individuals with very different problems into one category, which has contributed to the remarkably slow progress in key research domains such as the development of efficacious antidepressants or the identification of biomarkers for depression. The recently proposed network framework offers an alternative to the problematic assumptions. MD is not understood as a distinct condition, but as heterogeneous symptom cluster that substantially overlaps with other syndromes such as anxiety disorders. MD is not framed as an underlying disease with a number of equivalent indicators, but as a network of symptoms that have direct causal influence on each other: insomnia can cause fatigue which then triggers concentration and psychomotor problems. This approach offers new opportunities for constructing an empirically based classification system and has broad implications for future research. (shrink)

Recently Kenneth Kendler and Peter Zachar have raised doubts about the correspondence theory of truth and scientific realism in psychopathology. They argue that coherentist or pragmatist approaches to truth are better suited for understanding the reality of psychiatric disorders. In this article, I show that rejecting realism based on the correspondence theory is deeply problematic: It makes psychopathology categorically different from other sciences, and results in an implausible view of scientific discovery and progress. As an alternative, I suggest a robustness-based (...) approach that can accommodate the significance of coherence and pragmatic factors without rejecting scientific realism and the correspondence theory of truth. (shrink)

The frequent occurrence of comorbidity has brought about an extensive theoretical debate in psychiatry. Why are the rates of psychiatric comorbidity so high and what are their implications for the ontological and epistemological status of comorbid psychiatric diseases? Current explanations focus either on classification choices or on causal ties between disorders. Based on empirical and philosophical arguments, we propose a conventionalist interpretation of psychiatric comorbidity instead. We argue that a conventionalist approach fits well with research and clinical practice and resolves (...) two problems for psychiatric diseases: experimenter’s regress and arbitrariness. (shrink)

Network Analysis is considered as a new method that challenges Latent Variable models in inferring psychological attributes. With Network Analysis, psychological attributes are derived from a complex system of components without the need to call on any latent variables. But the ontological status of psychological attributes is not adequately defined with Network Analysis, because a psychological attribute is both a complex system and a property emerging from this complex system. The aim of this article is to reappraise the legitimacy of (...) latent variable models by engaging in an ontological and epistemological discussion on psychological attributes. Psychological attributes relate to the mental equilibrium of individuals embedded in their social interactions, as robust attractors within complex dynamic processes with emergent properties, distinct from physical entities located in precise areas of the brain. Latent variables thus possess legitimacy, because the emergent properties can be conceptualized and analyzed on the sole basis of their manifestations, without exploring the upstream complex system. However, in opposition with the usual Latent Variable models, this article is in favor of the integration of a dynamic system of manifestations. Latent Variables models and Network Analysis thus appear as complementary approaches. New approaches combining Latent Network Models and Network Residuals are certainly a promising new way to infer psychological attributes, placing psychological attributes in an inter-subjective dynamic approach. Pragmatism-realism appears as the epistemological framework required if we are to use latent variables as representations of psychological attributes. (shrink)

The comorbidity of personality disorders and mental disorders is commonly understood through three types of theoretical models: either a) personality disorders precede mental disorders, b) mental disorders precede personality disorders, c) mental disorders and personality disorders share common etiological grounds. Although these hypotheses differ with respect to their idea of causal direction, they all imply a latent variable perspective, in which it is assumed that either personality and mental disorders are latent variables that have certain causal relations [models a) and (...) b)]; or that, as in model c), the common etiology is in fact a latent variable that causes symptomatology of both personality and mental disorders. We aim to provide another perspective on the comorbidity between personality and mental disorders, namely a network perspective. To this end, we investigated Major Depression and Borderline Personality Disorder and hypothesized that symptoms of BPD and MD could interact with one another rather than being caused by a latent variable. To illustrate this theoretical network conceptualization of the comorbidity of BPD and MD we analyzed a cross-sectional clinical dataset of 376 patients who were asked to complete the Structured Clinical Interview for DSM-IV Axis II Disorders and the Beck Depression Inventory II. The results identify direct associations between symptoms of MD and BPD. If we take the links in this empirical network to be ‘substantive', this suggests a radical shift of our current conceptualization of the comorbidity of mental disorders and personality disorders. (shrink)

Lindquist et al. convincingly argue that the brain implements psychological operations that are constitutive of emotion rather than modules subserving discrete emotions. However, thenatureof such psychological operations is open to debate. I argue that considering appraisal theories may provide alternative interpretations of the neuroimaging data with respect to the psychological operations involved.

I reject Lindquist et al.'s implicit claim that all emotion theories other than constructionist ones subscribe to a “brain locationist” approach. The neural mechanisms underlying relevance detection, reward, attention, conceptualization, or language use are consistent with many theories of emotion, in particular componential appraisal theories. I also question the authors' claim that the meta-analysis they report provides support for thespecificassumptions of constructionist theories.

Psychiatric diagnostic covariation suggests that the underlying structure of psychopathology is not one of circumscribed disorders. Quantitative modeling of individual differences in diagnostic patterns has uncovered several broad domains of mental disorder liability, of which the Internalizing and Externalizing spectra have garnered the greatest support. These dimensions have generally been estimated from lifetime or past-year comorbidity patters, which are distal from the covariation of symptoms and maladaptive behavior that ebb and flow in daily life. In this study, structural models are (...) applied to daily diary data ( Median = 94 days) of maladaptive behaviors collected from a sample ( N = 101) of individuals diagnosed with personality disorders (PDs). Using multilevel and unified structural equation modeling, between-person, within-person, and person-specific structures were estimated from 16 behaviors that are encompassed by the Internalizing and Externalizing spectra. At the between-person level (i.e., individual differences in average endorsement across days) we found support for a two-factor Internalizing–Externalizing model, which exhibits significant associations with corresponding diagnostic spectra. At the within-person level (i.e., dynamic covariation among daily behavior pooled across individuals) we found support for a more differentiated, four-factor, Negative Affect-Detachment-Hostility-Disinhibition structure. Finally, we demonstrate that the person-specific structures of associations between these four domains are highly idiosyncratic. (shrink)

In addition to active wake, emotions are generated and experienced in a variety of functionally different states such as those of sleep, during which external stimulation and cognitive control are lacking. The neural basis of emotions can be specified by regarding the multitude of emotion-related brain states, as well as the distinct neuro- and psychodynamic stages (generation and regulation) of emotional experience.

The psychiatric category of addiction has recently been broadened to include new behaviors. This has prompted critical discussion about the value of a concept that covers so many different substances and activities. Many of the debates surrounding the notion of addiction stem from different views concerning what kind of a thing addiction fundamentally is. In this essay, we put forward an account that conceptualizes different addictions as sharing a cluster of relevant properties (the syndrome) that is supported by a matrix (...) of causal mechanisms. According to this “addiction-as-a-kind” hypothesis, several different kinds of substance and behavioral addictions can be thought of as instantiations of the same thing – addiction. We show how a clearly articulated account of addiction can facilitate empirical research and the theoretical integration of different perspectives on addiction. The causal matrix approach provides a promising alternative to existing accounts of the nature of psychiatric disorders, the traditional disease model, and its competitors. It is a positive addition to discussions about diagnostic criteria, and sheds light on how psychiatric classification may be integrated with research done in other scientific fields. We argue that it also provides a plausible approach to understanding comorbidity. (shrink)

Borsboom et al. have written a trenchant critique of biological reductionism in psychopathology. After commenting on recent controversies concerning the network perspective, I discuss ways of integrating biology into the network enterprise.

BackgroundPsychological distress is common among infertility patients. Total scale scores are often used to represent the severity of anxiety, depression, or stress, which ignores important differences between specific symptoms, and relationships between symptoms. This study aimed to identify patterns of psychological distress experienced by infertility patients and to identify the most central symptoms of anxiety, depression, and stress.MethodFrom June to September 2016, 740 infertility patients were included in this cross-sectional study. Infertility patients were asked to complete the Generalized Anxiety Disorder-7, (...) Patients Health Questionnaire-9 (PHQ-9), and Fertility Problem Inventory. Network analysis was used to examine the patterns of psychological distress in infertility patients and to test the most central symptoms of anxiety, depression, and stress.ResultsRestlessness was the most central symptom in infertility patients. “Feelings of guilt” had the highest strength among PHQ-9 symptoms. “Relationship concern stress” and “sexual concern stress” had the strongest connections in the network. Stability estimation indicated that the order of node strength centrality was more stable than the order of closeness and betweenness (the CS-coefficients were 0.75, 0.13, and 0.67, respectively). In addition, network structure and global strength were invariant across gender.LimitationsThe cross-sectional design did not permit identification of causal relationships. Patients in this study were recruited from one reproductive hospital; especially, most patients had low socioeconomic status, which limits generalizability of the findings.ConclusionThis study reinforces the need to better understand the underlying causes of psychological distress in infertile patients. A more detailed investigation of the relationship between these symptoms could provide information for psychosocial interventions aimed beyond “alleviating psychological distress.” We should consider the individual psychological symptom pattern and its potential causes in infertility patients instead of assuming a consistent psychological distress structure. (shrink)

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.

In the current paper, we propose a latent interdependence approach to modeling psychometric data in social networks. The idea of latent interdependence is adopted from social relations models, which formulate a mutual-rating process by both dyad members’ characteristics. Under the framework of the latent interdependence approach, we introduce two psychometric models: The first model includes the main effects of both rating-sender and rating-receiver, and the second model includes a latent distance effect to assess the influence from the dissimilarity between the (...) latent characteristics of both sides. The latent distance effect is quantified by the Euclidean distance between both sides’ trait scores. Both models use Bayesian estimation via Markov chain Monte Carlo. How accurately model parameters were estimated was evaluated in a simulation study. Parameter recovery results showed that all parameters were accurately recovered under most of the conditions investigated. As expected, the accuracy of model estimation was significantly improved as network size grew. Also, through analyzing empirical data, we showed how to use the estimates of model parameters to predict the latent weight of connections among group members and rebuild either a univariate or multivariate network at a latent trait level. Finally, we discuss issues regarding model comparison and offer suggestions for future studies. (shrink)

This study investigates the effect of socio-ecological networks on the willingness to communicate in English among Japanese people. Previous studies have shown that relational mobility, which is defined as the availability of opportunities to choose new relationship partners, positively affects the WTC in English for Japanese people. However, the network structure of the variables of relational mobility and its effects have not been revealed yet. The present study conducted network analysis with 474 Japanese university students and found the two clusters (...) that correspond to the dimensions of relational mobility in the partial correlation network. Three variables regarding opportunities to meet new people and leave current relationships positively affected the WTC in English; one had a negative effect. Centrality indices, such as nodes strength, betweenness, and closeness, revealed the centrality of several variables in the network. Bootstrapping methods showed the trustworthiness of the estimated network structure and centrality indices as well as edges and variables whose effects differed significantly from that of others. Contrary to the regression analysis results, the network analysis findings can help us understand the in-depth effect of relational mobility on the WTC in a second language, which will prove useful for intervention studies. (shrink)

Life history theory is a fruitful source of testable hypotheses about human individual differences. However, this field of study is beset by unresolved debates about basic concepts and methods. One of these controversies concerns the usefulness of instruments that purport to tap a unidimensional life history factor based on a set of self-reported personality, social, and attitudinal variables. Here, we take a novel approach to analyzing the psychometrics of two variants of the Arizona Life History Battery: the Mini-K and the (...) K-SF-42. Psychological network analysis generates models in which psychological variables comprise the nodes of a network, while partial correlation coefficients between these variables comprise the edges of the network. Centrality indices operationalize each node’s importance based on the pattern of the connections in which that node plays a role. Because childhood environments are hypothesized to influence adult LH, we tested the hypothesis that among the Mini-K items, and the K-SF-42 scales, those that tap relationships with parents are central to the networks constructed from these instruments. In an MTurk sample and an undergraduate sample that completed the Mini-K, and an MTurk sample that completed the K-SF-42, this hypothesis was falsified. Indeed, the “relationships with parents” items were among the most peripheral in all three networks. We propose that network analysis, as an alternative to latent variable modeling, offers considerable potential to test hypotheses about the input-output mappings of specific evolved psychological mechanisms. (shrink)