Thinking positively: The genetics of high intelligence
Nicholas G. Shakeshaft a, Maciej Trzaskowski a, Andrew McMillan a, Eva Krapohl a, Michael A. Simpson a, Avi Reichenberg a,b, Martin Cederlöf c, Henrik Larsson c, Paul Lichtenstein c, Robert Plomin a,⁎
High intelligence (general cognitive ability) is fundamental to the human capital that drives societies in the information age. Understanding the origins of this intellectual capital is important for government policy, for neuroscience, and for genetics. For genetics, a key question is whether the genetic causes of high intelligence are qualitatively or quantitatively different from the normal distribution of intelligence. We report results from a sibling and twin study of high intelligence and its links with the normal distribution. We identified 360,000 sibling pairs and 9000 twin pairs from 3 million 18-year-old males with cognitive assessments administered as part of conscription to military service in Sweden between 1968 and 2010. We found that high intelligence is familial, heritable, and caused by the same genetic and environmental factors responsible for the normal distribution of intelligence. High intelligence is a good candidate for “positive genetics” — going beyond the negative effects of DNA sequence variation on disease and disorders to consider the positive end of the distribution of genetic effects.
Neuroscience and education: myths and messages
Paul A. Howard-Jones
For several decades, myths about the brain — neuromyths — have persisted in schools and colleges, often being used to justify ineffective approaches to teaching. Many of these myths are biased distortions of scientific fact. Cultural conditions, such as differences in terminology and language, have contributed to a ‘gap’ between neuroscience and education that has shielded these distortions from scrutiny. In recent years, scientific communications across this gap have increased, although the messages are often distorted by the same conditions and biases as those responsible for neuromyths. In the future, the establishment of a new field of inquiry that is dedicated to bridging neuroscience and education may help to inform and to improve these communications.
Autism as a disorder of prediction
Pawan Sinhaa,1, Margaret M. Kjelgaarda,b, Tapan K. Gandhia,c, Kleovoulos Tsouridesa, Annie L. Cardinauxa, Dimitrios Pantazisa, Sidney P. Diamonda, and Richard M. Helda,1
A rich collection of empirical findings accumulated over the past three decades attests to the diversity of traits that constitute the autism phenotypes. It is unclear whether subsets of these traits share any underlying causality. This lack of a cohesive conceptualization of the disorder has complicated the search for broadly effective therapies, diagnostic markers, and neural/genetic correlates. In this paper, we describe how theoretical considerations and a review of empirical data lead to the hypothesis that some salient aspects of the autism phenotype may be manifestations of an underlying impairment in predictive abilities. With compromised prediction skills, an individual with autism inhabits a seemingly “magical” world wherein events occur unexpectedly and without cause. Immersion in such a capricious environment can prove overwhelming and compromise one’s ability to effectively interact with it. If validated, this hypothesis has the potential of providing unifying insights into multiple aspects of autism, with attendant benefits for improving diagnosis and therapy.
STUDY ALERT: Functional brain networks contributing to the Parieto-Frontal Integration Theory of Intelligence
Functional brain networks contributing to the Parieto-Frontal Integration Theory of Intelligence
Andrei A. Vakhtin, Sephira G. Ryman, Ranee A. Flores, Rex E. Jung
The refinement of localization of intelligence in the human brain is converging onto a distributed network that broadly conforms to the Parieto-Frontal Integration Theory (P-FIT). While this theory has received support in the neuroimaging literature, no functional magnetic resonance imaging study to date has conducted a 18 whole-brain network-wise examination of the changes during engagement in tasks that are reliable measures of general intelligence (e.g., Raven’s Progressive Matrices Test; RPM). Seventy-nine healthy subjects were scanned while solving RPM problems and during rest. Functional networks were extracted from the RPM and resting state data using Independent Component Analysis. Twenty-nine networks were identified, 26 of which were detected in both conditions. Fourteen networks were significantly correlated with the RPM task. Fourteen networks were significantly correlated with the RPM task. The networks’ spatial maps and functional connectivity measures at 3 frequency levels (low, medium, & high) were compared between the RPM and rest conditions. The regions involved in the networks that were found to be task related were consistent with the P-FIT, localizing to the bilateral medial frontal and parietal regions, right superior frontal lobule, and the right cingulate gyrus. Functional connectivity in multiple component pairs was differentially affected across all frequency levels during the RPM task. Our findings demonstrate that functional brain networks are more stable than previously thought, and maintain their general features across resting state and engagement in a complex cognitive task. The described spatial and functional connectivity alterations that such components undergo during fluid reasoning provide a network-wise framework of the P-FIT that can be valuable for further, network based, neuroimaging inquiries regarding the neural underpinnings of intelligence.
An Incomplete List of Eminent Psychologists of the Modern Era
Ed Diener, University of Virginia, University of Utah, and The Gallup Organization, Washington, DC
Shigehiro Oishi, University of Virginia
JungYeun Park, University of Illinois
In the present paper, we analyzed citation impact, textbook citations, and major scientific awards to identify eminent psychologists of modern era (Post-World War II). Identifying these individuals serves educational, administrative, and scholarly purposes. Readers can more readily identify the psychologists who have made the most impact on the profession, as well as the type of contributions that receive recognition. In addition, young researchers can learn what is required if they want to achieve eminence. Finally, our analysis helps pinpoint imbalances in need of change, for example gender and ethnic disparities.
STUDY ALERT: Life-Span Changes of the Human Brain White Matter: Diffusion Tensor Imaging (DTI) and Volumetry
Life-Span Changes of the Human Brain White Matter: Diffusion Tensor Imaging (DTI) and Volumetry
Lars T. Westlye1, Kristine B. Walhovd1, Anders M. Dale2,3,4, Atle Bjørnerud5,6,7, Paulina Due-Tønnessen6, Andreas Engvig1, Ha ̊ kon Grydeland1, Christian K. Tamnes1, Ylva Østby1 and Anders M. Fjell1
Magnetic resonance imaging volumetry studies report inverted U- patterns with increasing white-matter (WM) volume into middle age suggesting protracted WM maturation compared with the cortical gray matter. Diffusion tensor imaging (DTI) is sensitive to degree and direction of water permeability in biological tissues, providing in vivo indices of WM microstructure. The aim of this cross-sectional study was to delineate age trajectories of WM volume and DTI indices in 430 healthy subjects ranging 8–85 years of age. We used automated regional brain volume segmentation and tract-based statistics of fractional anisotropy, mean, and radial diffusivity as markers of WM integrity. Nonparametric regressions were used to fit the age trajectories and to estimate the timing of maximum development and deterioration in aging. Although the volumetric data supported protracted growth into the sixth decade, DTI indices plateaued early in the fourth decade across all tested regions and then declined slowly into late adulthood followed by an accelerating decrease in senescence. Tractwise and voxel-based analyses yielded regional differences in development and aging but did not provide ample evidence in support of a simple last-in-first- out hypothesis of life-span changes.
An Evolutionary Life History Framework for Psychopathology
Marco Del Giudice
Department of Psychology, University of New Mexico, Albuquerque, New Mexico
In this article, I outline a general framework for the evolutionary analysis of mental disorders based on the concepts of life history theory. I synthesize and extend a large body of work showing that individual differences in life history strategy set the stage for the development of psychopathology. My analysis centers on the novel distinction between fast spectrum and slow spectrum disorders. I describe four main causal pathways from life history strategies to psychopathology, argue that psychopathology can arise at both ends of the fast–slow continuum of life history variation, and provide heuristic criteria for classifying disorders as fast or slow spectrum pathologies. I then apply the fast–slow distinction to a diverse sample of common mental disorders: externalizing disorders, schizophrenia and autism spectrum disorders, obsessive-compulsive disorders, eating disorders, and depression. The framework integrates previously disconnected models of psychopathology within a common frame of reference and has far-reaching implications for the classification of mental disorders.