Evolutionary psychologists search for human universals, differential psychologists for variation around common human themes. So far, evolutionary psychology and differential psychology seem somewhat disparate and unconnected, although Kanazawa (May–June 2010) is certainly not the first to attempt integrating them (see Penke, 2010, and references therein). Kanazawa uses intelligence to elaborate his view of integration. His evolutionary theory of intelligence is based on two assumptions: (1) General intelligence (g) is both an individual-differences variable and a domain-specific adaptation, and (2) the domain to which general intelligence is adapted is evolutionary novelty. Both claims are erroneous. Kanazawa (2010) defended his first assumption by arguing that there are individual differences associated with any adaptation. To use one of his illustrations: Bipedalism is a universal human adaptation, but there are individual differences in running speed. In the same vein, Kanazawa claimed that general intelligence is a universal human adaptation but that there are also individual differences in general intelligence. He then inferred that g, an individual-differences variable, can be used as a “measure” or “indicator” of a general intelligence adaptation. This line of reasoning is troublesome. As Borsboom and Dolan (2006) have spelled out, g is a psychometric construct, reflecting positive correlations between scores on different cognitive tests (i.e., the positive manifold). To interpret g as something other than a psychometric construct is to go far beyond the data. Specifically, in contrast to adaptations such as language acquisition or color perception, g refers exclusively to human individual differences, not to a human universal. Individual differences certainly exist in the efficiency, size, quality, sensitivity, or performance of adaptations, but these differences are not the basis of their existence (Borsboom & Dolan, 2006; Penke, 2010). The existence of g does not indicate that general intelligence is present within every normal human, but that every human occupies one of its levels, which is a completely different statement. In short, g is not an adaptation or causal mechanism, but a variable. A variable is not necessarily associated with just one modular adaptation (or mechanism/process/cause). Running speed is associated with bipedalism, but also with the cardiovascular system, with the achievement motive to train harder, and so forth—arguably adaptations in their own rights. A given variable can indicate parameters of adaptations (Penke, 2010), but the variable is never tantamount to the adaption. In addition, a variable is not the cause of the existence of an adaptation (running speed is not the cause of bipedalism), nor does the existence of an adaptation explain the nature of individual differences (bipedalism is not the cause of differences in running speed). The empirical observation of g in itself tells us nothing about the causal reasons why people show individual differences in g. Kanazawa (2010) assumed that g is the result of a single domain-specific adaptation. If this were true, then different individuals with the same g score (or rank on the dimension described by g) should have this score for the same reasons; that is, g differences reflect differences in the performance of a single coherent adaptation. The available biological evidence, however, points to causal heterogeneity underlying g and thus against the single-intelligence-adaptation hypothesis: Different individuals seem to use their brains differently to solve intelligence tests equally well, and different rare (probably private or family-specific) mutations likely contribute substantially to the genetics of g in different individuals (Deary, Penke, & Johnson, 2010; Penke, 2010). Thus, although one cannot rule out the single-intelligence-adaptation hypothesis a priori, the biological evidence does not support it. We need to keep in mind that the g-factor is just one among many models for describing the actual empirical observation, namely, positive-manifold correlations among cognitive tests. Equally plausible and explanatory models exist: For instance, Godfrey Thomson (1881–1955) presented a “bonds” model based on random sampling of cognitive processes for solving test items, thus positing heterogeneity rather than homogeneity in those cognitive processes (Bartholomew, Deary, & Lawn, 2009). Van der Maas et al. (2006) proposed a second plausible model that explains g by positing mutualistic developmental interactions among distinct cognitive processes. These models are irreconcilable with Kanazawa’s (2010) claim that g reflects a single domain-specific adaptation. Kanazawa’s (2010) second assumption, that general intelligence is a specific adaptation to the domain of evolutionary novelty, is also questionable. “Evolutionary novelty,” which is defined by exclusion (i.e., as everything previously not encountered in our evolutionary past), is not a coherent characterization of an adaptive problem. Selection can only tailor domainspecific adaptations to common problem structures. So which structural feature do lightning, flash floods, television characters, genetically unrelated groups, and electric light have in common? According to Kanazawa, their communality is that they pose problems that are logically solvable. But what is logically correct about being politically liberal when living in unrelated groups or about being slightly more nocturnal when having electric light? Furthermore, there is no coherent computational mechanism that embraces “methods of induction . . . deductive reasoning . . . , analogy, abstraction, and so forth” (Kanazawa, 2010, p. 283) and could thus be called a domain-specific general intelligence adaptation. Finally, novelty violates requirements for rational decision theory (including logic), as by definition relevant information is unknown or has to be estimated from small samples when encountering novelty (Gigerenzer & Brighton, 2009). More plausible evolutionary responses to novelty include simple heuristics (Gigerenzer & Brighton, 2009), open developmental programs (Mayr, 1974), and domain-specific adaptations supporting social/cultural learning (instead of widespread individual reasoning) (Henrich & McElreath, 2003). To conclude, while evolved adaptations can and often do vary in certain parameters, an individual-differences variable need not correspond to a specific underlying adaptation. Because g is an individual-differences variable, it is uninformative about whether a domain-specific adaptation for evolutionary novelty exists. This undermines Kanazawa’s (2010) integration of evolutionary and differential psychology as well as his empirical evidence for his evolutionary theory of intelligence—all based on g—completely. In tapraid4/z2n-amepsy/z2n-amepsy/z2n00811/z2n3794d11z xppws S 1 10/12/11 4:21 Art: 2010-2207
Network approaches that investigate the interaction between symptoms and behaviours have opened new ways of understanding psychological phenomena in health and disorder in recent years. In parallel, network approaches that characterise the interaction between brain regions have become the dominant approach in neuroimaging research. In this paper, we introduce a methodology for combining network psychometrics and network neuroscience. This approach utilises the information from the psychometric network to obtain neural correlates that are associated with each node in the psychometric network (network-based regression). Moreover, we combine the behavioural variables and their neural correlates in a joint network to characterise their interactions. We illustrate the approach by highlighting the interaction between the triad of autistic traits and their resting-state functional connectivity associations. To this end, we utilise data from 172 male autistic participants (10-21 years) from the autism brain data exchange (ABIDE, ABIDE-II) that completed resting-state fMRI and were assessed using the autism diagnostic interview (ADI-R). Our results indicate that the network-based regression approach can uncover both unique and shared neural correlates of behavioural measures. For instance, our example analysis indicates that the overlap between communication and social difficulties is not reflected in the overlap between their functional brain correlates.
In this paper, we present the academic genealogy of presidents of the Psychometric Society by constructing a genealogical tree, in which Ph.D. students are encoded as descendants of their advisors. Results show that most of the presidents belong to five distinct lineages that can be traced to Wilhelm Wundt, James Angell, William James, Albert Michotte or Carl Friedrich Gauss. Important psychometricians Lee Cronbach and Charles Spearman play only a marginal role. The genealogy systematizes important historical knowledge that can be used to inform studies on the history of psychometrics and exposes the rich and multidisciplinary background of the Psychometric Society.
No abstract is provided for this article.
Anybody who has some familiarity with the research literature in scientific psychology has probably thought, at one time or another, ‘Well, all these means and correlations are very interesting, but what do they have to do with me, as an individual...
Weinberger (2015) claims that if a latent variable is a cause, it must be a within-subject cause. In addition, Weinberger suggests that this fact refutes the conclusion of Borsboom, Mellenbergh, and Van Heerden (2003), who stated that standard psychometric models have a causal interpretation that is cast strictly in a between-subjects sense: individual differences in the latent variable may cause individual differences in test scores, while the latent variable has no causal relevance at the level of the individual. Weinberger’s argument elucidates the status of causal relations in latent variable models, and clearly spells out the strong assumptions that underlie the use of such models. However, contrary to Weinberger’s claims, a pure individual-differences reading of the causal model is possible. This interpretation relies on the fact that, for latent variable models, shifts of the person relative to the latent dimension can either be interpreted as a change of the individual, or as a shift of the population relative to the individual. The latter interpretation does not require us to place assumptions on what interventions would do intra-individually, but nevertheless is consistent with a causal interpretation along the lines suggested by Weinberger.
BackgroundMajor depressive disorder (MDD) is a heterogeneous mental disorder. International guidelines present overall symptom severity as the key dimension for clinical characterisation. However, additional layers of individual differences may reside within severity levels related to differences in how symptoms interact with one-another in a given patient, referred to as symptom dynamics. We investigate these individual differences by estimating the proportion of patients that display differences in their symptom relationship patterns while sharing the same overall symptom severity.MethodsPatients with MDD recruited at four centres in the Netherlands between 2016-2018 rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for symptoms were collected through Ecological Momentary Assessments scheduled to measure each patient five times per day for 28 days. Each patient’s symptom dynamics were estimated using dynamic networks based on the graphical vector autoregressive model. Individual differences in these symptom relationship patterns in groups of patients sharing the same symptom severity levels were estimated using Individual Network Invariance Tests, before the overall proportion of patients that displayed differential symptom dynamics while sharing the same symptom severity was calculated. To compute 95% bootstrapped confidence intervals around this proportion, 10,000 re-estimations following random draws with replacement of the symptom severity groups were performed. A supplementary simulation study was conducted to investigate the accuracy of our methodology by identifying its average false positive detection rate in a simulated scenario where no individual differences should be present.ResultsOut of 74 patients, 73 were analysed (Mage = 34.57 [SD 13.12]; 56.16% females; 63.01% employed) and 8,395 observations were conducted across the 28-day period (average completed assessments per person: 115; SD 16.81). 23 severity levels (IDS-SR values) were observed in the sample, enabling the investigation of individual differences in 23 groups of MDD patients (between 2 to 6 patients per group) sharing the same severity. Differential symptom dynamics were identified across 63.01% (95% bootstrapped CI 40.98, 82.05) of patients displaying the same severity. The simulation study revealed our method’s average false detection of individual differences in our scenario to be 2.22%.ConclusionsThe majority of MDD patients sharing the same symptom severity displayed differential symptom dynamics. Our findings were robust against false positive conclusions. Examining symptom dynamics provides information about the person-specific psychopathological expression of patients beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may serve as a promising dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.
In this article, we present the findings of an oral history project on the past, present, and future of psychometrics, as obtained through structured interviews with twenty past Psychometric Society presidents. Perspectives on how psychometrics should be practiced vary strongly. Some presidents are psychology-oriented, whereas others have a more mathematical or statistical approach. The originally strong relationship between psychometrics and psychology has weakened, and contemporary psychometrics has become a diverse and multifaceted discipline. The presidents are confident psychometrics will continue to be relevant but believe psychometrics needs to become better at selling its strong points to relevant research areas. We recommend for psychometrics to cherish its plurality and make its goals and priorities explicit.
Importance Psychiatric disorders may come and go with symptoms changing over a lifetime. This suggests the need for a paradigm shift in diagnosis and treatment. Here we present a fresh look inspired by dynamical systems theory. This theory is used widely to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. Observations In the dynamical systems view, we propose the healthy state has a basin of attraction representing its resilience, while disorders are alternative attractors in which the system can become trapped. Rather than an immutable trait, resilience in this approach is a dynamical property. Recent work has demonstrated the universality of generic dynamical indicators of resilience that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforests and tipping elements of the climate system. Other dynamical systems tools are used in ecology and climate science to infer causality from time series. Moreover, experiences in ecological restoration confirm the theoretical prediction that under some conditions, short interventions may invoke long-term success when they flip the system into an alternative basin of attraction. All this implies practical applications for psychiatry, as are discussed in part 2 of this article. Conclusions and Relevance Work in the field of dynamical systems points to novel ways of inferring causality and quantifying resilience from time series. Those approaches have now been tried and tested in a range of complex systems. The same tools may help monitoring and managing resilience of the healthy state as well as psychiatric disorders.
Psychological network theories provide an important alternative to traditional common cause theories, such as the g-theory of general intelligence and brain-based explanations of depression. Network theories, which are often formalized using the Ising model from statistical physics, have gained significant empirical support. However, the binary nature of nodes in Ising-type models presents a limitation, as many psychological datasets include responses with uncertain or neutral categories (e.g., "don't know" or "not relevant"). Ternary spin models, such as the Blume-Capel model, overcome this constraint by incorporating a third node state, zero, that can represent such responses, enabling more nuanced scale representations. The resulting models exhibit more complex dynamics and provide new insights into research across a range of psychological constructs. We illustrate our approach with examples from three key subdisciplines of psychology. First, we introduce a ternary spin model for attitudes, extending the Ising attitude model. Next, we propose a unified framework encompassing both bipolar disorder and major depressive disorder. Finally, we present a novel ternary network model for understanding knowledge acquisition.
Nolen-Hoeksema and Watkins (2011, this issue) propose a useful model for thinking about transdiagnostic processes involved in mental disorders. Here, we argue that their model is naturally compatible with a network account of mental disorders, in which disorders are viewed as sets of mutually reinforcing symptoms. We show that network models are typically transdiagnostic in nature, because different disorders often share symptoms. We illustrate this by constructing a network for generalized anxiety and major depression. In addition, we show that even a simple network structure naturally accounts for the phenomena of multifinality and divergent trajectories that Nolen-Hoeksema and Watkins identify as crucial in thinking about transdiagnostic phenomena.
Health-related quality of life (HRQoL) is a dynamic construct. Experience sampling methods (ESM) are becoming increasingly popular to capture within-person fluctuations in HRQoL. An emerging approach to analyze such momentary data is network analysis. Our aim was to explore the use of network analysis for investigating the dynamics within individual's HRQoL.We analyzed ESM data of 30 patients with stable coronary artery disease (CAD). Patients completed eight HRQoL items representing four scales (i.e., positive mood, negative mood, CAD symptoms, and physical state) at nine times a day for seven consecutive days. Network analysis was used to analyze the data at group level to estimate the average HRQoL dynamics and at patient level to estimate HRQoL dynamics of individual patients.Group-level analysis showed that, on average, feeling "tired" and feeling "anxious" are the most central items in patients' HRQoL. Patient-level analysis revealed differences in patients' network structures, indicating within-person differences in HRQoL dynamics.This study is one of the first to apply network analysis to momentary HRQoL data. To the extent that network models are meaningful representations of HRQoL dynamics, they may help deepening our insight into experienced HRQoL and provide targets for personalized treatment.
