IQ score differences across time may relfect real changes in the brain

Lay people and many professionals often express consternation when an individuals measured IQ scores are different at different times in their life.  This concern is particularly heightened in high stakes settings where differences in IQ scores can result in changes in eligibility for programs (e.g., social security disability income) or life-or-death decisions (e.g., Atkins MR/ID death penalty cases).

Factors contributing to significant IQ score differences are many (McGrew, in press a) and may include: (a) procedural or test administration errors (e.g., scoring errors; improper nonstandardized test administration; malingering; age vs. grade norms; practice effects), (b) test norm or standardization differences (e.g., norm obsolescence or the Flynn Effect; McGrew, in press b), (c) content differences across different test batteries or between different editions of the same battery, or (d) variations in a person’s performance on different occasions.

 An article "in press" (Neuroimage) by Burgaleta et al. (click here to view copy with annotated comments)  provides the important reminder that differences in IQ scores for an individual (across time) may be due to real changes in general intelligence related to real changes in brain development.  These researchers found that changes in cortical brain thickness were related to changes in IQ scores.  They concluded that "the dynamic nature of intelligence-brain relations...support the idea that changes in IQ across development can reflect meaningful general cognitive ability changes and have a neuroanatomical substrate" (viz., changes in cortical thickness in key brain regions).  The hypothesis was offered that changes in the the cortical areas of  frontoparietal brain network (see P-FIT model of intelligence) may be related to changes in working memory, which in turn has been strongly associated with general reasoning (fluid intelligence; Gf).

The cortical thickness-IQ change relation was deemed consistent with "cellular events that are sensitive to postnatal development and experience."  Possible causal factors suggested included insufficient education or social stimulation during sensitive developmental periods, as well as lifestyle, diet and nutrition, and genetic factors.

• McGrew, K. S. (in press a).  Intellectual functioning:  Conceptual issues.  In E. Polloway (Ed.), Determining intellectual disability in the courts:  Focus on capital cases.  AAIDD, Washington, DC.

•  McGrew, K. S. (in press b).  Norm obsolescence:  The Flynn Effect.  In E. Polloway (Ed.), Determining intellectual disability in the courts:  Focus on capital cases.  AAIDD, Washington, DC.

[Click on images to enlarge]

Research Bytes: Brain complexity, predicting job success, neuroscience/creativity, fluid IQ and personality

Bassett, D. S., & Gazzaniga, M. S. (2011). Understanding complexity in the human brain. Trends in Cognitive Sciences, 15(5), 200-209.

Although the ultimate aim of neuroscientific enquiry is to gain an understanding of the brain and how its workings relate to the mind, the majority of current efforts are largely focused on small questions using increasingly detailed data. However, it might be possible to successfully address the larger question of mind–brain mechanisms if the cumulative findings from these neuroscientific studies are coupled with complementary approaches from physics and philosophy. The brain, we argue, can be understood as a complex system or network, in which mental states emerge from the interaction between multiple physical and functional levels. Achieving further conceptual progress will crucially depend on broad-scale discussions regarding the properties of cognition and the tools that are currently available or must be developed in order to study mind–brain mechanisms.
Article Outline



Ziegler, M., Dietl, E., Danay, E., Vogel, M., & Buhner, M. (2011). Predicting Training Success with General Mental Ability, Specific Ability Tests, and (Un)Structured Interviews: A meta-analysis with unique samples. International Journal of Selection and Assessment, 19(2), 170-182.


Several meta-analyses combine an extensive amount of research concerned with predicting training success. General mental ability is regarded as the best predictor with specific abilities or tests explaining little additional variance. However, only few studies measured all predictors within one sample. Thus, intercorrelations were often estimated based on other studies. Moreover, new methods for correcting range restriction are now available. The present meta-analyses used samples derived from a German company in which applicants for different apprenticeships were tested with an intelligence structure test, specific ability tests as well as a structured and an unstructured interview. Therefore, intercorrelations between different assessment tools did not have to be estimated from other data. Results in the final examination, taking place at least 2 years after the original assessment, served as criterion variable. The dominant role of general mental ability was confirmed. However, specific abilities were identified that can be used as valuable additions. Job complexity moderated some of the relationships. Structured interviews were found to have good incremental validity over and above general mental ability. Unstructured interviews, on the other hand, performed poorly. Practical implications are discussed.


Sawyer, K. (2011). The Cognitive Neuroscience of Creativity: A Critical Review. Creativity Research Journal, 23(2), 137-154.

Cognitive neuroscience studies of creativity have appeared with increasing frequently in recent years. Yet to date, no comprehensive and critical review of these studies has yet been published. The first part of this article presents a quick overview of the 3 primary methodologies used by cognitive neuroscientists: electroencephalography (EEG), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI). The second part provides a comprehensive review of cognitive neuroscience studies of creativity-related cognitive processes. The third part critically examines these studies; the goal is to be extremely clear about exactly what interpretations can appropriately be made of these studies. The conclusion provides recommendations for future research collaborations between creativity researchers and cognitive neuroscientists.


Djapo, N., KolenovicDjapo, J., Djokic, R., & Fako, I. (2011). Relationship between Cattell's 16PF and fluid and crystallized intelligence. Personality and Individual Differences, 51(1), 63-67.

The aim of the study was to explore the relationship between the five global factors and 16 dimensions of Cattell’s personality model and fluid and crystallized intelligence. A total of 105 third graders (45.7% males) of three high schools participated in the research. Fluid intelligence was measured by Raven’s Advanced Progressive Matrices and crystallized intelligence was measured by the Mill Hill Vocabulary Scale. Personality traits were measured by the Sixteen Personality Factor Questionnaire. Anxiety is correlated neither with fluid nor with crystallized intelligence. Extraversion and Self-Control are negatively correlated with fluid intelligence whereas Tough-Mindedness is positively correlated with it. Independence is positively correlated with crystallized intelligence and Tough-Mindedness is negatively correlated with it. Regression analysis reveals that all broad personality factors, except anxiety, are significant predictors of fluid intelligence. When combined together, these factors account for 25% of the variance of fluid intelligence scores. The regression model with crystallized intelligence as a criterion variable is not statistically significant. The study results are consistent with the Chamorro


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Educational neuroscience: Mind Brain and Education

I just stumbled upon a new journal that appears worthy to monitor. It is a journal dealing with the field of educational neuroscience--Mind Brain and Education. Below are a select sample of article abstracts.


Blair, C. (2010). Going Down to the Crossroads: Neuroendocrinology, Developmental Psychobiology, and Prospects for Research at the Intersection of Neuroscience and Education. Mind Brain and Education, 4(4), 182-187.

The relation of stress hormones and activity in stress response systems to the development of aspects of cognition and behavior important for educational achievement and attainment is examined from the perspective of the developmental psychobiological model. It is proposed that research in neuroendocrinology supports three general conclusions, namely (1) that there is a neuroscientifically definable optimal level of stress arousal in children against which various curricula and teaching and learning activities can be examined; (2) that consideration of the time course of stress arousal indicates that optimal levels of stress arousal are temporally limited and can be matched to specific instructional activities; and (3) that alterations to stress response systems through processes of allostasis and allostatic load, particularly for children facing early psychosocial disadvantage, can impair the flexible regulation of stress response systems needed for effective learning in school.



Fischer, K. W., Goswami, U., & Geake, J. (2010). The Future of Educational Neuroscience. Mind Brain and Education, 4(2), 68-80

The primary goal of the emerging field of educational neuroscience and the broader movement called Mind, Brain, and Education is to join biology with cognitive science, development, and education so that education can be grounded more solidly in research on learning and teaching. To avoid misdirection, the growing worldwide movement needs to avoid the many myths and distortions in popular conceptions of brain and genetics. It should instead focus on integrating research with practice to create useful evidence that illuminates the brain and genetic bases as well as social and cultural influences on learning and teaching. Scientists and educators need to collaborate to build a strong research foundation for analyzing the “black box” of biological and cognitive processes that underpin learning.


Newcombe, N. S., & Frick, A. (2010). Early Education for Spatial Intelligence: Why, What, and How. Mind Brain and Education, 4(3), 102-111

Spatial representation and thinking have evolutionary importance for any mobile organism. In addition, they help reasoning in domains that are not obviously spatial, for example, through the use of graphs and diagrams. This article reviews the literature suggesting that mental spatial transformation abilities, while present in some precursory form in infants, toddlers, and preschool children, also undergo considerable development and show important individual differences, which are malleable. These findings provide the basis for thinking about how to promote spatial thinking in preschools, at home, and in children's play. Integrating spatial content into formal and informal instruction could not only improve spatial functioning in general but also reduce differences related to gender and socioeconomic status that may impede full participation in a technological society.


Sylvan, L. J., & Christodoulou, J. A. (2010). Understanding the Role of Neuroscience in Brain Based Products: A Guide for Educators and Consumers. Mind Brain and Education, 4(1), 1-7.

This article describes an experiment utilizing a research and development strategy to design and implement an innovative school for the future. The development of Cramim Elementary School was a joint effort of researchers from Tel-Aviv University and the staff of the school. The design stage involved constructing a new theoretical framework that defined school as a knowledge system, based on the state of the art, interdisciplinary study of the nature of humans, and the nature of knowledge. A new school design emerged based on this theoretical framework and the school was opened in 1995. Action research followed for 8 years and the results indicated that the school has emerged as a learning organization and successfully integrated knowledge technologies into the learning processes of both students and teachers. Differentiated teaching strategy resulted in a significant increase in achievements (+11% in maths, literacy, and science; +10% in literacy in kindergarten; persistence of higher achievement in junior high schools). The greatest beneficiaries were low-achieving students. As the school is a highly complex system, individual variables contributing to the increased effectiveness could not be isolated. The article's conclusion is that experimental schools are a productive strategy to bring about changes, but unless these schools are part and parcel of the culture of the mainstream education system culture, they are destined to remain isolated cases.


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iPost: The basics of brain development--nice overview article

See link below





http://www.springerlink.com/content/g551601215g44j30/



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