Theories and Models of Human Intelligence: Recent Developments
21st European Society for Cognitive Psychology Conference (ESCoP)
25-28 September 2019 | Tenerife, Spain
Andrew R.A. Conway
Claremont Graduate University, USA
For almost a century, the field of human intelligence has developed independently of mainstream experimental/cognitive psychology, resulting in what Cronbach famously identified as a separation of the two disciplines of scientific psychology: correlational and experimental. Relatedly, research on intelligence had been dominated by taxonomies rather than cognitive models. The beginning of the 21st century has seen a drastic change; the field of intelligence suddenly proliferated in theories and models that integrate results from the study of the cognition and the neural bases of cognitive processes in order to explain core phenomena in the individual differences literature. Such recent advances include the mutualism model (van der Maas et al., 2006), process overlap theory (Kovacs & Conway, 2016), the watershed model (Kievit et al., 2016), and network neuroscience theory (Barbey, 2017). According to these models, the general factor of intelligence is not a psychological attribute; instead, it is an emergent property of dynamic cognitive and neural networks. At the same time, research on one of the oldest topics in intelligence, mental speed, has gained momentum from methodological improvements (Schubert et al., 2015). The proposed symposium will bring together an international group of researchers who are all actively working on theories and models of human intelligence. These models integrate findings from psychometrics, neuroscience, and cognitive psychology. The speakers will present models and theories they have developed as well as the state of the art of research motivated by these models and theories.
The Network Approach to General Intelligence
Han L.J. van der Maas
University of Amsterdam, The Netherlands
The study of individual differences in cognitive performance or 'intelligence' is a multidisciplinary endeavor, integrating results from both psychometric and non-psychometric research, including genetics, neuroscience, and education. Intelligence remains a puzzling concept, however. After more than a century of research, there is still no consensus on the definition of intelligence, for example, and there are still major disputes over whether intelligence is one thing (a general ability) or multiple things. In our view, it is important to arrive at a clear conception of what intelligence is. In this presentation we adhere to such a conception and present a formal model of psychometric intelligence. This model is admittedly overly simplistic, yet illuminates mechanisms that can explain some important, and well replicated phenomena in the study of intelligence. The model firstly acknowledges that the brain is a complex system, i.e., an open system consisting of many elements that interact non-linearly. Secondly, intelligence is regarded as an emergent phenomenon of that system, a phenomenon that results from intricate local interactions among those elements. In short, the model provides an alternative explanation of the positive correlations between cognitive tests, based on the idea of networks: a key modeling framework within complex system research. In this presentation, we will a) explain the origin and setup of this so-called mutualism model, b) discuss criticisms that have been raised in response to the model, c) extend the model, d) present new statistical techniques for this model, and e) discuss new developments in the network modeling of general intelligence.
Kees-Jan Kan, University of Amsterdam
Process Overlap Theory: A Cognitive Explanation of the Structure of Human Mental Abilities
ELTE Eotvos Lorand University, Budapest, Hungary
People who perform better on one kind of mental test tend to perform better on other tests as well. This result is called the positive manifold and is described with a general factor, 'g'. g, in turn, is often identified with a domain-general, within-individual cognitive mechanism. This interpretation does not sit well with findings from cognitive psychology and neuroscience that point to the domain-specific fractionation of cognition. We recently proposed an alternative explanation, process overlap theory, a cognitive theory of overlapping item response processes. The theory assumes that any item requires a number of domain-specific as well as domain-general cognitive processes and their corresponding neural mechanisms. Domain-general processes involved in executive attention are central to test performance. That is, they are activated by a large number of test items, alongside with domain-specific processes tapped by specific types of tests only. Such an overlap of executive processes explains the positive manifold as well as the hierarchical structure of cognitive abilities. The theory also accounts for a number of other, previously unexplained phenomena in differential psychology, such as the central role of fluid inductive reasoning in cognitive abilities or the higher across-domain variance in low ability groups (differentiation). Besides presenting the theory and the corresponding multidimensional item response model, recent and ongoing studies testing predictions of the theory will be discussed: 1) an analysis of differentiation in working memory capacity, 2) a simulation study of test performance based on the model, 3) a network analysis of the Wechsler scales.
Andrew R.A. Conway, Claremont Graduate University, Claremont, CA, USA
A process-based Account of the Relationship between Mental Speed and Mental Abilities
Heidelberg University, Heidelberg, Germany
Individual differences in the speed of information processing have been shown to be consistently related to individual differences in general intelligence. It is an open debate whether these associations can be explained in terms of individual differences in some brain-wide property affecting a large number of cognitive processes, or whether these advantages in processing speed reflect advantages in specific cognitive processes such as executive functions or evidence accumulation. We will demonstrate how mathematical models and psychophysiological approaches can be used to shed some light onto these questions by decomposing the time-course of information processing and relating process parameters to cognitive abilities. In particular, we will present recent research showing that more intelligent individuals show specific advantages in the speed of higher-order cognitive processes such as decision making and memory updating. Moreover, we will present results from two studies suggesting that the association between processing speed and general intelligence may be driven by a confounding variable and discuss whether structural or functional brain network connectivity may be a candidate confound explaining the association between mental speed and mental abilities. Finally, we will outline a research agenda that combines mathematical modeling and psychophysiological approaches to identify the neuro-cognitive processes giving rise to individual differences in general intelligence.
D. Hagemann Heidelberg University, Heidelberg, Germany
G. T. Frischkorn, University of Zurich, Zurich, Switzerland
A Watershed Model of Fluid Intelligence: Evidence from 3 Lifespan Cohorts
Rogier A. Kievit
University of Cambridge, Cambridge, UK
Fluid intelligence is a crucial cognitive ability that predicts key life outcomes across the lifespan. Strong empirical links exist between fluid intelligence and processing speed on the one hand, and white matter integrity and processing speed on the other. We propose a watershed model that integrates three explanatory levels in a principled manner in a single statistical model, with processing speed and working memory figuring as intermediate phenotypes between white matter microstructure and fluid abilities. Using a hierarchical structural equation model we fit this model in a large (N=555) adult lifespan (18-88) cohort from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) using multiple measures of processing speed, white matter health and fluid intelligence. We then replicated and expanded the watershed model in two developmental two cohorts: CALM (N = 551, aged 5 - 17 years) and NKI-RS (N = 335, aged 6 - 17 years). In all three samples, the watershed model fit the data well, outperforming competing accounts and providing evidence for a many-to-one mapping between white matter integrity, processing speed, working memory and fluid intelligence. Moreover, using exploratory SEM trees in the developmental cohorts, we observed that the relationship between cognitive abilities and white matter differed with age, showing a dip in strength around ages 7 - 12 years. This age-effect may reflect a reorganization of the neurocognitive architecture around pre- and early puberty.
Network Neuroscience Theory of Human Intelligence
Aaron K. Barbey
University of Illinois, Urbana-Champaign, IL, USA
An enduring aim of research in the psychological and brain sciences is to understand the nature of individual differences in human intelligence, examining the stunning breadth and diversity of intellectual abilities and the remarkable neurobiological mechanisms from which they arise. In this presentation, I survey recent neuroscience evidence to elucidate how general intelligence (g) emerges from individual differences in the network architecture of the human brain. The reviewed findings motivate new insights about how network topology and dynamics account for individual differences in g, represented by the Network Neuroscience Theory. According to this framework, g emerges from the small-world topology of brain networks and the dynamic reorganization of its community structure in the service of system-wide flexibility and adaptation. Rather than attribute individual differences in general intelligence to a single brain region, network, or the overlap among specific networks, the proposed theory instead suggests that general intelligence depends on the dynamic reorganization of brain networks - modifying their topology and community structure in the service of system-wide flexibility and adaptation. This framework sets the stage for new approaches to understanding individual differences in general intelligence, examining the global network topology and dynamics of the human brain - from the level of molecules and synapses to neural circuits, networks, and systems. By investigating the foundations of general intelligence in global network dynamics, the burgeoning field of network neuroscience will continue to advance our understanding of the cognitive and neural architecture from which the remarkable constellation of individual differences in human intelligence emerge.