Beliefs can be highly resilient in the sense that they are not easily abandoned in the face of counterevidence. This has the advantage of guiding consistent behavior and judgments but may also have destructive consequences for individuals, nature, and society. For instance, pathological beliefs can sustain psychiatric disorders, the belief that rhinoceros horn is an aphrodisiac may drive a species extinct, beliefs about gender or race may fuel discrimination, and belief in conspiracy theories can undermine democracy. Here, we present a unifying framework of how self-amplifying feedbacks shape the inertia of beliefs on levels ranging from neuronal networks to social systems. Sustained exposure to counterevidence can destabilize rigid beliefs but requires organized rational override as in cognitive behavioral therapy for pathological beliefs or institutional control of discrimination to reduce racial biases. Black-and-white thinking is a major risk factor for the formation of resilient beliefs associated with psychiatric disorders as well as prejudices and conspiracy thinking. Such dichotomous thinking is characteristic of a lack of cognitive resources, which may be exacerbated by stress. This could help explain why conspiracy thinking and psychiatric disorders tend to peak during crises. A corollary is that addressing social factors such as poverty, social cleavage, and lack of education may be the most effective way to prevent the emergence of rigid beliefs, and thus of problems ranging from psychiatric disorders to prejudices, conspiracy theories, and posttruth politics.
No abstract is provided for this article.
This article shows that measurement invariance (defined in terms of an invariant measurement model in different groups) is generally inconsistent with selection invariance (defined in terms of equal sensitivity and specificity across groups). In particular, when a unidimensional measurement instrument is used and group differences are present in the location but not in the variance of the latent distribution, sensitivity and positive predictive value will be higher in the group at the higher end of the latent dimension, whereas specificity and negative predictive value will be higher in the group at the lower end of the latent dimension. When latent variances are unequal, the differences in these quantities depend on the size of group differences in variances relative to the size of group differences in means. The effect originates as a special case of Simpson's paradox, which arises because the observed score distribution is collapsed into an accept-reject dichotomy. Simulations show the effect can be substantial in realistic situations. It is suggested that the effect may be partly responsible for overprediction in minority groups as typically found in empirical studies on differential academic performance. A methodological solution to the problem is suggested, and social policy implications are discussed.
Mottus gives the impression that composites, as well as other models in which traits are a result rather than a cause of their indicators, require “emergent properties” to have causal power. We argue that this is not necessary; composites can be considered causally relevant by themselves when they mediate the relation between their constituents and the outcome variable.
Many individuals with autism report generally low quality of life (QoL). Identifying predictors for pathways underlying this outcome is an urgent priority. We aim to examine multivariate patterns that predict later subjective and objective QoL in autistic individuals. Autistic characteristics, comorbid complaints, aspects of daily functioning, and demographics were assessed online in a 2‐year longitudinal study with 598 autistic adults. Regression trees were fitted to baseline data to identify factors that could predict QoL at follow‐up. We found that sleep problems are an important predictor of later subjective QoL, while the subjective experience of a person's societal contribution is important when it comes to predicting the level of daily activities. Sleep problems are the most important predictor of QoL in autistic adults and may offer an important treatment target for improving QoL. Our results additionally suggest that social satisfaction can buffer this association. Autism Research 2019, 12: 794–801 . © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary Many individuals with autism report generally low quality of life (QoL). In this study, we looked at factors that predict long‐term QoL and found that sleep problems are highly influential. Our results additionally suggest that social satisfaction can buffer this influence. These findings suggest that sleep and social satisfaction could be monitored to increase QoL in autistic adults.
This article is a revision of the previous edition article by J.O. Ramsay, volume 18, pp. 12416–12422, © 2001, Elsevier Ltd.
The authors use this chapter to break from their shared authorial voice and enter into dialog on the topics covered in the book. Dialog from the first edition
Network theories have been put forward for psychopathology (in which mental disorders originate from causal relations between symptoms) and for personality (in which personality factors originate from coupled equilibria of cognitions, affect states, behaviors, and environments). Here, we connect these theoretical strands in an overarching Personality-Resilience-Psychopathology (PRP) model. In this model, factors in personality networks control the shape of the dynamical landscape in which symptom networks evolve; for example, the neuroticism item “I often feel blue” measures a general tendency to experience negative affect, which is hypothesized to influence the threshold parameter of the symptom “Depressed Mood” in the psychopathology network. Conversely, events at the level of the fast-evolving psychopathology network (e.g., a depressive episode), can influence the slow-evolving personality variables (e.g., by increasing feelings of worthlessness). We apply the theory to neuroticism and Major Depressive Disorder (MDD). Through simulations, we show that the model can accommodate important phenomena, such as the strong relation between neuroticism and depression, and individual differences in the change of neuroticism levels and development of depression over time. The results of the simulation are implemented in an online, interactive tool. Implications for research into the relationship between personality and psychopathology are discussed.
This paper formulates a metatheoretical framework for latent variable modeling. It does so by spelling out the difference between observed and latent variables. This difference is argued to be purely epistemic in nature: We treat a variable as observed when the inference from data structure to variable structure can be made with certainty and as latent when this inference is prone to error. This difference in epistemic accessibility is argued to be directly related to the data-generating process, i.e., the process that produces the concrete data patterns on which statistical analyses are executed. For a variable to count as observed through a set of data patterns, the relation between variable structure and data structure should be (a) deterministic, (b) causally isolated, and (c) of equivalent cardinality. When any of these requirements is violated, (part of) the variable structure should be considered latent. It is argued that, on these criteria, observed variables are rare to nonexistent in psychology; hence, psychological variables should be considered latent until proven observed.
The mutualism model, an alternative for the g-factor model of intelligence, implies a formative measurement model in which “g” is an index variable without a causal role. If this model is accurate, the search for a genetic of brain instantiation of “g” is deemed useless. This also implies that the (weighted) sum score of items of an intelligence test is just what it is: a weighted sum score. Preference for one index above the other is a pragmatic issue that rests mainly on predictive value.
Background Previous research has shown that stressful life events (SLEs) influence the pattern of individual depressive symptoms. However, we do not know how these differences arise. Two theories about the nature of psychiatric disorders have different predictions about the source of these differences: (1) SLEs influence depressive symptoms and correlations between them indirectly, via an underlying acute liability to develop a dysphoric episode (DE; common cause hypothesis); and (2) SLEs influence depressive symptoms and correlations between them directly (network hypothesis). The present study investigates the predictions of these two theories. Method We divided a population-based sample of 2096 Caucasian twins (49.9% female) who reported at least two aggregated depressive symptoms in the last year into four groups, based on the SLE they reported causing their symptoms. For these groups, we calculated tetrachoric correlations between the 14 disaggregated depressive symptoms and, subsequently, tested whether the resulting correlation patterns were significantly different and if those differences could be explained by underlying differences in a single acute liability to develop a DE. Results The four SLE groups had markedly different correlation patterns between the depressive symptoms. These differences were significant and could not be explained by underlying differences in the acute liability to develop a DE. Conclusions Our results are not compatible with the common cause perspective but are consistent with the predictions of the network hypothesis. We elaborate on the implications of a conceptual shift to the network perspective for our diagnostic and philosophical approach to the concept of what constitutes a psychiatric disorder.
Early warning indicators based on critical slowing down have been suggested as a model-independent and low-cost tool to anticipate the (re)emergence of infectious diseases. We studied whether such indicators could reliably have anticipated the second COVID-19 wave in European countries. Contrary to theoretical predictions, we found that characteristic early warning indicators generally decreased rather than increased prior to the second wave. A model explains this unexpected finding as a result of transient dynamics and the multiple timescales of relaxation during a non-stationary epidemic. Particularly, if an epidemic that seems initially contained after a first wave does not fully settle to its new quasi-equilibrium prior to changing circumstances or conditions that force a second wave, then indicators will show a decreasing rather than an increasing trend as a result of the persistent transient trajectory of the first wave. Our simulations show that this lack of timescale separation was to be expected during the second European epidemic wave of COVID-19. Overall, our results emphasize that the theory of critical slowing down applies only when the external forcing of the system across a critical point is slow relative to the internal system dynamics.
