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Kevin S. McGrew, PhD
Educational Psychologist
Director Institute for Applied Psychometrics (IAP)
Yet more support for the importance of understanding brain networks. I have also suggested the importance of the interplay of the salience network, the default network and the central executive control network in my MindHub Pub 2 white paper....in an attempt to explain the possible mechanisms of a neurotechnology that appears to make demands on attentional control. Report can be found here....http://www.themindhub.com/research-reports
"Disturbances in the salience network may be a common etiology underlying many psychiatric disorders."
What is the Flynn Effect, and how does it change our understanding of IQ?
Shenk, David
Wiley Interdisciplinary Reviews: Cognitive Science: Vol. 8 Issue 1-2 – 2017: e1366
Are you intelligent — or rational? It's appealing to think that "all it takes is a lot of practice," but the factors behind elite performance are more complicated than that.…
There is a new expert survey out which, amongst other things, queries the world's top psychometrics experts on the future of the FLynn effect (Flynn + Lynn – clever). James…
The Flynn Effect is important to understand; it is better understood now than ever before, but there is more to research; and it is probably more limited in its…
Learning Disabilities, Attention-deficit Hyperactivity Disorder, and Executive Functioning: Contributions from Educational Psychology in Progressing Theory, Measurement, and Practice
Newton, Kristie J.; Sperling, Rayne A.; Martin, Andrew J.
Contemporary Educational Psychology: Articles in press
High pressure settings compromise working memory and decrease cognitive performance.
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Those with higher working memory show greatest pressure-induced cognitive deficits.
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Attentional control alters relation of working memory to performance under pressure.
Previous
research has shown that the higher one's working memory capacity, the
more likely his/her performance is to be negatively impacted by
performance pressure. In the current research we examined potential
explanations for this finding by assessing the relation between
pressure-induced performance deficits (i.e. “choking under pressure”) in
math-based problem solving and individual differences in both working
memory (as assessed via complex span tasks) and attentional control (as
assessed via two measures from an Eriksen Flanker task). We find higher
working memory only relates to “choking under pressure” when individuals
were low in attentional control. These results further elucidate the
mechanism by which high-pressure scenarios can lead to errors in
performance and carry implications for developing effective intervention
strategies to prevent poor performance in high-stakes situations.
This is WAY cool. A promising method that, IMHO, might be applied to routine required repeated drawings in the elderly during regular physical exams. Also, would it be possible to develop fractal-based diagnostic norms from geometric drawings from standardize psychological drawing tests used in clinical practice? That is, fractal analyze all norm based drawings from a test, develop what is "normative" by age, and then flag drawings of clients that are divergent from typical? Need to file this for interesting possible new test development methods.
What paint can tell us: A fractal analysis of neurological changes in seven artists. Forsythe, Alex; Williams, Tamsin; Reilly, Ronan G. Neuropsychology: Vol. 31 Issue 1 – 2017: 1 - 10
Pathways to School Readiness: Executive Functioning Predicts Academic and Social-Emotional Aspects of School Readiness
Mann, Trisha D.; Hund, Alycia M.; Hesson-McInnis, Matthew S.; Roman, Zachary J.
Mind, Brain, and Education: Articles in press
Curriculum-Based Measurement as the Emerging Alternative: Three Decades Later
Fuchs, Lynn S.
Learning Disabilities Research & Practice: Articles in press
I just ran across this statement in a recent article (see below). It served as a reminder of something I have always preached, but from-time-to-time, tend to forget as I analyze cognitive ability test data, post research articles, or suggest hypotheses regarding test score differences---be it here at this blog, in a journal article, book, book chapter, or professional presentation. The point being that we must remain vigilant in remembering the "individual" in individual differences research.
The privileged unit of analysis in psychology is the individual (Nesselroade, Gerstorf, Hardy, & Ram, 2007). Nevertheless, many data-analytic approaches coarsely aggregate data and tacitly assume group-average models to hold and to be interpreted in lieu of more fine-grained and, ultimately, person-specific models. For example, when a group of persons show an average increase of performance in a learning task, this does not mean that all persons follow a pattern of change similar to this average. In fact, none of the persons may be well represented by the average trend. In a similar vein, Tucker (1966) argued that the consideration of differences instead of averages will allow us to gain more information about the nature of basic functions underlying behavior. Ever since, researchers have been questioning coarse aggregation of data across persons (e.g., Lamiell, 1981; Nesselroade & Molenaar, 1999) as the estimates of averaged effects may not be representative of any single individual. In fact, strong inference about intra-individual variation from interindividual variation is only possible under the ergodic assumption (Molenaar, 2004), which assumes that the group model represents each individual's dynamics (homogeneity) and that those dynamics have constant characteristics in time (stationarity). In the same vein, Simpson (1951) pointed out that a statistical relationship observed in a population could be reversed within subgroups that form the population. For instance, “It may be universally true that drinking coffee increases one's level of neuroticism; then it may still be the case that people who drink more coffee are less neurotic” (Borsboom, Kievit, Cervone, & Hood, 2009, p. 72). Simpson's paradox may arise whenever inferences are drawn across different explanatory levels, for example, from populations to the individual, or from cross-sectional data to intraindividual change over time (see Kievit, Frankenhuis, Waldorp, & Borsboom, 2013, for further illustrations). Hence, there still is a need for focusing on individuals or subgroups of individuals to more accurately model individual process idiosyncrasies and similarities across persons. Particularly, in light of large-scale empirical data sets, aggregation is more likely to lead to models with low informative value about individual underlying processes as it is often difficult to expand prior hypotheses to account for the large number of potential explanatory variables.
Quote is from this article (click on image to enlarge)
Some feel that spontaneous thought occurring without specific stimulation is closest to understanding how we define ourselves. These seemingly random self-produced…
Is computer gaming associated with cognitive abilities? A population study among German adolescents
Gnambs, Timo; Appel, Markus
Intelligence: Articles in press
Overlap Between the General Factor of Personality and Emotional Intelligence: A Meta-Analysis.
van der Linden, Dimitri; Pekaar, Keri A.; Bakker, Arnold B.; Schermer, Julie Aitken; Vernon, Philip A.; Dunkel, Curtis S.; Petrides, K. V.
Psychological Bulletin: Articles in press
In a recent article by Robert Colom (2016) in the Spanish Journal of Psychology, I was reminded of an important quote by one of the leaders in Intelligence over the past 50 years.....Dr. Doug Detterman
In the farewell editorial note published by D. K. Detterman after being editor of the journal ‘Intelligence' for four decades he wrote: “from very early, I was convinced that intelligence was the most important thing of all to understand, more important than the origin of the universe, more important than climate change, more impor-tant than curing cancer, more important than anything else. That is because human intelligence is our major adaptive function and only by optimizing it will we be able to save ourselves and other living things from ultimate destruction. It is as simple as that”.
Therapeutic geometry porn. 1. Breaking down the surface area of a sphere. It all makes sense now. 2. How sine and cosine are related in 3D coordinates.…
President Barack Obama fist-bumps the robotic arm of Nathan Copeland during a tour at the White House Frontiers Conference at the University of Pittsburgh, Oct.…
This is an excellent overview of mind wandering and brain networks (especially the default mode network
The science of mind wandering
Some feel that spontaneous thought occurring without specific stimulation is closest to understanding how we define ourselves. These seemingly random self-produced…
A bit over four years ago I wrote a glowing review of Daniel Kahneman's Thinking, Fast and Slow. I described it as a "magnificent book" and "one of the…
I just learned of this special issue in Psychological Methods. I am looking forward to reading many of the articles as the idea of "big data" analysis in psychology is important. I am particularly looking forward to reading the article co-authored by Jack McArdle on SEM trees. I am not sure I will understand it, but I know Jack does tremendous work. He was the first person to introduce me to SEM methods many years ago (during the WJ-R project; he taught me SEM, very gently, with a program called COSAN..and then I graduated to LISREL), and he was an awesome teacher---he could make complex stat methods conceptually clear. I also then learned of decision-tree methods (CART, MAR) from Jack, and believe they should be used more in psychological research. This PM issue should be well received by the quantoid readers of this blog.
Research that falls under the breadth of the topic of human intelligence is extensive.
For decades I have attempted to keep abreast with intelligence-related research, particularly research that would help with the development, analysis, and interpretation of applied intelligence tests. I frequently struggled with integrating research that focused on brain-behavior relations or networks, neural efficiency, etc. I then rediscovered a simple three-level categorization of intelligence research by Earl Hunt. I modified it into a four-level model, and the model is represented in the figure above.
In this "intelligent" testing series, primary emphasis will be on harnessing information from the top "psychometric level" of research to aid in test interpretation. However, given the increased impact of cognitive neuropsychological research on test development, often one must turn to level 2 (information processing) to understand how to interpret specific tests.
This series will draw primarily from the first two levels, although there may be times were I import knowledge from the two brain-related levels.
To better understand this framework, and put the forthcoming information in this series in proper perspective, I would urge you to view the "connecting the dots" video PPT that I previously posted at this blog.
Here it is. The next post will start into the psychometric level information that serves as the primary foundation of "intelligent" intelligence testing.
Expertise and individual differences: the search for the structure and acquisition of experts' superior performance
Ericsson, K. Anders
Wiley Interdisciplinary Reviews: Cognitive Science: Articles in press
What is the Flynn Effect, and how does it change our understanding of IQ?
Shenk, David
Wiley Interdisciplinary Reviews: Cognitive Science: Articles in press
New test uses brain's electrical activity to pinpoint reading challenges early, increasing chances for success in school 
