Recent trends in psychiatry involve a transition from categorical to dimensional frameworks, in which the boundary between health and pathology is understood as a difference in degree rather than as a difference in kind. A major tenet of dimensional approaches is that no qualitative distinction can be made between health and pathology. As a consequence, these approaches tend to characterize such a threshold as pragmatic or conventional in nature. However, dimensional approaches to psychopathology raise several epistemological and ontological issues. First, (...) we review major sources of evidence usually recruited in support of the dimensional trend (focusing on clinical observation and biological data), and we show that these are connected to different conceptualizations of how dimensional traits extend across health and pathology. Second, we criticize two unquestioned assumptions that stand at the core of the dimensional trend: a) that there is continuity from health to pathology at the symptomatic level; b) that such continuity reflects an underlying continuity in the genetic liability for pathological conditions. Third, we argue against the idea of a conventional threshold by showing that such a view implies a linear relationship between the genotype and the phenotype. Fourth, drawing on epigenetics and developmental biology, we offer a characterization of mental disorders as stable and dynamic constellations of multi-level variables that differ qualitatively from ‘healthy states’. We conclude by showing that our account has several theoretical advantages over both categorical and dimensional approaches. Notably, it provides crucial insights into psychological development over time and individual differences, with major implications in terms of intervention and clinical decision-making. (shrink)

Theories of different and independent types of intelligence constitute a Lakatosian research program, as they all claim that human intelligence has a multidimensional structure, consisting of independent cognitive abilities, and that human intelligence is not characterized by any general ability that is of greater practical importance, or that has greater predictive validity, than other, more specialized cognitive abilities. This paper argues that the independent intelligences research program is degenerating, since it has not led to novel, empirically corroborated predictions. However, despite (...) its flaws, the program provides an illustrative example of some of the philosophical problems that inhere in Lakatos’s so-called “methodology”. Indeed, Lakatos’s conceptions of the negative heuristic, the positive heuristic, and the relationship between scientific appraisal and advice are all vulnerable to objections. The upshot is that theories of independent intelligences indeed teach us more about philosophy of science than about the nature of human intelligence. (shrink)

This paper challenges the common assumption that some phenotypic traits are quantitative while others are qualitative. The distinction between these two kinds of traits is widely influential in biological and biomedical research as well as in scientific education and communication. This is probably due to both historical and epistemological reasons. However, the quantitative/qualitative distinction involves a variety of simplifications on the genetic causes of phenotypic variability and on the development of complex traits. Here, I examine three cases from the life (...) sciences that show inconsistencies in the distinction: Mendelian traits, Mendelian diseases, and polygenic mental disorders. I show that these traits can be framed both quantitatively and qualitatively depending, for instance, on the methods through which they are investigated and on specific epistemic purposes. This suggests that the received view of quantitative and qualitative traits has a limited heuristic power—limited to some local contexts or to the specific methodologies adopted. Throughout the paper, I provide directions for framing phenotypes beyond the quantitative/qualitative distinction. I conclude by pointing at the necessity of developing a principled characterisation of what phenotypic traits, in general, are. (shrink)

Cognitive abilities cannot be measured directly. What we can measure is individual variation in task performance. In this paper, we first make the case for why we should be interested in mapping individual differences in task performance on to particular cognitive abilities: we suggest that it is crucial for examining the causes and consequences of variation both within and between species. As a case study, we examine whether multiple measures of inhibitory control for non-human animals do indeed produce correlated task (...) performance; however, no clear pattern emerges that would support the notion of a common cognitive ability underpinning individual differences in performance. We advocate a psychometric approach involving a three-step programme to make theoretical and empirical progress: first, we need tasks that reveal signature limits in performance. Second, we need to assess the reliability of individual differences in task performance. Third, multi-trait multi-method test batteries will be instrumental in validating cognitive abilities. Together, these steps will help us to establish what varies between individuals that could impact their fitness and ultimately shape the course of the evolution of animal minds. Finally, we propose executive functions, including working memory, inhibitory control and attentional shifting, as a sensible starting point for this endeavour. (shrink)

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 model of human intelligence that is most widely adopted derives from psychometrics and behavioral genetics. This standard approach conceives intelligence as a general cognitive ability that is genetically highly heritable and describable using quantitative traits analysis. The paper analyzes intelligence within the debate on natural kinds and contends that the general intelligence conceptualization does not carve psychological nature at its joints. Moreover, I argue that this model assumes an essentialist perspective. As an alternative, I consider an HPC theory of (...) intelligence and evaluate how it deals with essentialism and with intuitions coming from cognitive science. Finally, I highlight some concerns about the HPC model as well, and conclude by suggesting that it is unnecessary to treat intelligence as a kind in any sense. (shrink)

Psychometric g—a statistical factor capturing intercorrelations between scores on different IQ tests—is of theoretical interest despite being a low-fidelity model of both folk psychological intelligence and its cognitive/neural underpinnings. Psychometric g idealizes away from those aspects of cognitive/neural mechanisms that are not explanatory of the relevant variety of folk psychological intelligence, and it idealizes away from those varieties of folk psychological intelligence that are not generated by the relevant cognitive/neural substrate. In this manner, g constitutes a high-fidelity bridge model of (...) the relationship between its two targets and, thereby, helps demystify the relationship between folk and scientific psychology. (shrink)

This paper offers a new angle on the common idea that the process of science does not support epistemic diversity. Under minimal assumptions on the nature of journal editing, we prove that editorial procedures, even when impartial in themselves, disadvantage less prominent research programs. This purely statistical bias in article selection further skews existing differences in the success rate and hence attractiveness of research programs, and exacerbates the reputation difference between the programs. After a discussion of the modeling assumptions, the (...) paper ends with a number of recommendations that may help promote scientific diversity through editorial decision making. (shrink)

Psychological constructs such as personality dimensions or cognitive traits are typically unobserved and are therefore measured by observing so-called indicators of the latent construct. The Common Factor Model models the relations between the observed indicators and the latent variable. In this article we argue in favor of interpreting the CFM as a causal model rather than merely a statistical model, in which common factors are only descriptions of the indicators. When there is sufficient reason to hypothesize that the underlying causal (...) structure of the data is a common cause structure, a causal interpretation of the CFM has several benefits over a merely statistical interpretation of the model. We argue that a causal interpretation conforms with most research questions in which the goal is to explain the correlations between indicators rather than merely summarizing them; a causal interpretation of the factor model legitimizes the focus on shared, rather than unique variance of the indicators; and a causal interpretation of the factor model legitimizes the assumption of local independence. (shrink)

The presence of general intelligence poses a major evolutionary puzzle, which has led to increased interest in its presence in nonhuman animals. The aim of this review is to critically evaluate this question and to explore the implications for current theories about the evolution of cognition. We first review domain-general and domain-specific accounts of human cognition in order to situate attempts to identify general intelligence in nonhuman animals. Recent studies are consistent with the presence of general intelligence in mammals (rodents (...) and primates). However, the interpretation of a psychometricgfactor as general intelligence needs to be validated, in particular in primates, and we propose a range of such tests. We then evaluate the implications of general intelligence in nonhuman animals for current theories about its evolution and find support for the cultural intelligence approach, which stresses the critical importance of social inputs during the ontogenetic construction of survival-relevant skills. The presence of general intelligence in nonhumans implies that modular abilities can arise in two ways, primarily through automatic development with fixed content and secondarily through learning and automatization with more variable content. The currently best-supported model, for humans and nonhuman vertebrates alike, thus construes the mind as a mix of skills based on primary and secondary modules. The relative importance of these two components is expected to vary widely among species, and we formulate tests to quantify their strength. (shrink)

After describing the disorder of psychopathy, I examine the theories and the evidence concerning the psychopaths’ deficient moral capacities. I first examine whether or not psychopaths can pass tests of moral knowledge. Most of the evidence suggests that they can. If there is a lack of moral understanding, then it has to be due to an incapacity that affects not their declarative knowledge of moral norms, but their deeper understanding of them. I then examine two suggestions: it is their deficient (...) practical reason or their stunted emotions that are at fault. The evidence supports both explanations. I conclude with an overview of the debate concerning whether they are morally or legally responsible for their actions. (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)

(2013). Statistical models as cognitive models of individual differences in reasoning. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 89-102. doi: 10.1080/19462166.2012.674061.

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.

Cramer et al.'s article is an example of the fruitful application of complex dynamic systems theory. We extend their approach with examples from our own work on development and developmental psychopathology and address three issues: (1) the level of aggregation of the network, (2) the required research methodology, and (3) the clinical and educational application of dynamic network thinking.

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.

We welcome network theory as a tool for modelling the multi-directional interactions that characterise disease. However, we feel that Cramer et al. have neglected one important aspect: how diseases change over developmental time. We discuss principles such as fan in, fan out, bottlenecks, and common pathways, and argue that modelling these developmental aspects can be vital, particularly in deriving properly targeted treatments.

Prior research has identified age 9–11 as a critical period for the development of the control-of-variables strategy (CVS). We examine the stability of interindividual differences in children's CVS skills with regard to their precursor skills during this critical developmental period. To this end, we relate two precursor skills of CVS at age 9 to four skills constituting fully developed CVS more than 2 years later, controlling for children's more general cognitive development. Note thatN= 170 second- to fourth-graders worked on multiple (...) choice-assessments of their understanding of indeterminacy of evidence and of confounding. We find relations between these two precursor skills and children's CVS skills 2 years later at age 11 inplanning, identifying, andinterpretingcontrolled experiments, and in recognizing the inconclusiveness of confounded comparisons (understanding). In accordance with the perspective that both indeterminacy and confounding constitute critical, related yet distinct elements of CVS, both precursor skills contribute to the prediction of later CVS. Together, the two precursor skills can explain 39% of students' later CVS mastery. Overall, the understanding of indeterminacy is a stronger predictor of fully developed CVS than that of confounding. The understanding of confounding, however, is a better predictor of the more difficult CVS sub-skills of understanding the inconclusiveness of confounded comparisons, and of planning a correctly controlled experiment. Importantly, both precursor skills maintain interactive predictive strength when controlling for children's general cognitive abilities and reading comprehension, showing that the developmental dynamics of CVS and its precursor skills cannot be fully ascribed to general cognitive development. We discuss implications of these findings for theories about the development of CVS and broader scientific reasoning. (shrink)

Cramer et al's proposal to view mental disorders as the outcome of network dynamics among symptoms obviates the need to invoke latent traits to explain co-occurrence of symptoms and syndromes. This commentary considers the consequences of such a network view for genetic association studies.

The dominant model for interpreting brain imaging experiments assumes that the brain is organized to support mental processes that control behavior. However functional neuroimaging experiments, particularly of cognitive tasks, have not shown a high level of reproducibility and localization. This lack of clear functional segregation has been blamed on limitations in imaging technology and non linearity and regional overlap in how the brain implements these processes. However the validity of the underlying cognitive models used to describe the brain have rarely (...) been questioned or directly tested against imaging results. We propose an alternative model of brain function which correlates observed human behavior directly with measured brain activity without assumptions about intervening cognitive processes, that we term the Non-Cognitive Behavioral Model (NBM). Our model derives from Behavioral Psychology but is extended to include brain activations in addition to behavior as an observable. A further extension is the role of neuroplasticity as opposed to innate cognitive processes in the development of the brains support of cognitive behavior. Applications of NBM to neuroimaging studies are described and contrasted with the standard methodology which we refer to as the Standard Cognitive Model (SCM). In one application, the NBM correctly predicted the global, total energy consumption as the brain parameter that best correlates with a person being in the state of consciousness, a correlation missed by studies using the SCM due to its assumption that global activity does not support cognitive processes. Neuroplasticity is shown to provide a natural explanation of findings that object recognition activating the fusiform facial area comes from expertise through raining as opposed to the SCM explanation of an innate cognitive process specialized for face recognition. The conclusions are generalized to explain the poor reproducibility of cognitive processes reported in functional neuroimaging and to suggest how such studies can be used to better explain how the brain supports behavior. (shrink)

Competitive state anxiety is an integral feature of sports performance but despite its pervasiveness, there is still much debate concerning the measurement of the construct. Adopting a network approach that conceptualizes symptoms of a construct as paired associations, we proposed re-examining competitive state anxiety as a system of interacting components in a dataset of 485 competitive athletes from the United Kingdom. Following a process of data reduction, we estimated a network structure for 15 items from the modified Three Factor Anxiety (...) Inventory using the graphical LASSO algorithm. We then examined network connectivity using node predictability. Exploratory graph analysis was used to detect communities in the network and bridge expected influence calculated to estimate the influence of items from one community to items in other communities. The resultant network produced a range of node predictability values. Community detection analysis derived three communities that corresponded with previous research and several nodes were identified that bridged these communities. We conclude that network analysis is a useful tool to explore the competitive state anxiety response and we discuss how the results of our analysis might inform the assessment of the construct and how this assessment might inform interventions. (shrink)