Friday, December 30, 2016

Research Byte: Learning Disabilities, Attention-deficit Hyperactivity Disorder, and Executive Functioning: Contributions from Educational Psychology in Progressing Theory, Measurement, and Practice via BrowZine

Intro to a special issue

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



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Thursday, December 29, 2016

Research Byte: A closer look at who "chokes under pressure" - importance of attentional control (AC)

Volume 5, Issue 4, December 2016, Pages 470–477
Working Memory in the Wild: Applied Research in Working Memory

A Closer Look at Who “Chokes Under Pressure”



Highlights

High pressure settings compromise working memory and decrease cognitive performance.
Those with higher working memory show greatest pressure-induced cognitive deficits.
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.

Wednesday, December 28, 2016

Research Byte: What paint can tell us: A fractal analysis of neurological changes in seven artists. via BrowZine

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

10.1037/neu0000303

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Research Byte: Pathways to School Readiness: Executive Functioning Predicts Academic and Social-Emotional Aspects of School Readiness via BrowZine

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



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Research Byte: Curriculum-Based Measurement as the Emerging Alternative: Three Decades Later via BrowZine

Curriculum-Based Measurement as the Emerging Alternative: Three Decades Later
Fuchs, Lynn S.
Learning Disabilities Research & Practice: Articles in press



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Tuesday, December 27, 2016

Remembering the "individual" in individual differences research: A quote to note

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)



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What do IQ researchers really think about the Flynn Effect?

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Monday, December 26, 2016

The science of mind wandering



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…

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Research Bytes: The Flynn effect in the Czech Republic via BrowZine

The Flynn effect in the Czech Republic
Laciga, Jiří; Cígler, Hynek
Intelligence: Articles in press



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Research Bytes: Is computer gaming associated with cognitive abilities? A population study among German adolescents via BrowZine

Is computer gaming associated with cognitive abilities? A population study among German adolescents
Gnambs, Timo; Appel, Markus
Intelligence: Articles in press



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Saturday, December 24, 2016

Research Bytes: Overlap Between the General Factor of Personality and Emotional Intelligence: A Meta-Analysis. via BrowZine

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



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Thursday, December 22, 2016

Penn study confirms 'sniff test' may be useful in diagnosing early Alzheimer's disease

File under Go in CHC taxonomy

Penn study confirms 'sniff test' may be useful in diagnosing early Alzheimer's disease

PHILADELPHIA–Tests that measure the sense of smell may soon become common in neurologists' offices. Scientists…

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Brain scan hints at first simple test for concussion



Brain scan hints at first simple test for concussion

Small study suggests long-sought biological marker for brain injuries. A test that records the way the brain processes sound might provide a simple…

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Research Bytes: Self-beliefs: Strong correlates of mathematics achievement and intelligence via BrowZine

Self-beliefs: Strong correlates of mathematics achievement and intelligence
Stankov, Lazar; Lee, Jihyun
Intelligence: Articles in press



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Wednesday, December 21, 2016

Album: Comic explainer: how memory works (12 Pictures)

I particularly like the analogy of SnapChat app representing working memory



Comic explainer: how memory works (12 Pictures)
http://theconversation.com/comic-explainer-how-memory-works-64485

Related topics: Comics

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Brain-training app Peak sells majority stake to French publisher Hachette



Brain-training app Peak sells majority stake to French publisher Hachette

From Technology, a Flipboard magazine by Flipboard Newsdesk

Something of a sleeper hit, the makers of Peak — a subscription-based service specifically designed to entertain a user while…

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Tuesday, December 20, 2016

Detterman on the importance of human intelligence

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”.


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Monday, December 19, 2016

White matter structure in the brain predicts cognitive function at ages 1 and 2

More evidence that white matter matters

White matter structure in the brain predicts cognitive function at ages 1 and 2

A new study led by UNC School of Medicine researchers concluded that patterns of white matter microstructure present at…

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17 Mathematical GIFs That Are Deeply Soothing

Just cool.

17 Mathematical GIFs That Are Deeply Soothing

From BuzzFeed on Flipboard

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.…

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Progress in BRAIN Initiative Research



Progress in BRAIN Initiative Research

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.…

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Sunday, December 18, 2016

What IQ researchers really think about race and intelligence



What IQ researchers really think about race and intelligence

I am still settling in at unz.com so please forgive me if I forget my lines and bump into the furniture, because the stage is much larger…

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Friday, December 16, 2016

Drawing path diagrams of structural equation models (SEM) for publication - ahoi data



Drawing path diagrams of structural equation models (SEM) for publication - ahoi data

Visualisation of structural equation models is done with path diagrams. They are an important means to give your…

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Wednesday, December 14, 2016

The science of mind wandering - excellent overview


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…

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Tuesday, December 13, 2016

Re-reading Kahneman’s Thinking, Fast and Slow



Re-reading Kahneman's Thinking, Fast and Slow

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…

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Friday, December 09, 2016

Special Education Case at Supreme Court Could Prove Costly for Schools

A complex issues with pro's and con's on both positions.  Will be interesting to see what SCOTUS decides

Special Education Case at Supreme Court Could Prove Costly for Schools

A fifth-grader with autism at an elementary school in Dubuque, Iowa. Advocates for disabled students say the absence of national…

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Tuesday, December 06, 2016

Big data in psychology: Special issue of Psychological Methods

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.

 

Update -- Psychological Methods - Volume 21, Issue 4

A new issue is available for the following APA journal:


Big data in psychology: Introduction to the special issue.
Page 447-457
Harlow, Lisa L.; Oswald, Frederick L.

A practical guide to big data research in psychology.
Page 458-474
Chen, Eric Evan; Wojcik, Sean P.

A primer on theory-driven web scraping: Automatic extraction of big data from the Internet for use in psychological research.
Page 475-492
Landers, Richard N.; Brusso, Robert C.; Cavanaugh, Katelyn J.; Collmus, Andrew B.

Mining big data to extract patterns and predict real-life outcomes.
Page 493-506
Kosinski, Michal; Wang, Yilun; Lakkaraju, Himabindu; Leskovec, Jure

Gaining insights from social media language: Methodologies and challenges.
Page 507-525
Kern, Margaret L.; Park, Gregory; Eichstaedt, Johannes C.; Schwartz, H. Andrew; Sap, Maarten; Smith, Laura K.; Ungar, Lyle H.

Tweeting negative emotion: An investigation of Twitter data in the aftermath of violence on college campuses.
Page 526-541
Jones, Nickolas M.; Wojcik, Sean P.; Sweeting, Josiah; Silver, Roxane Cohen


Theory-guided exploration with structural equation model forests.
Page 566-582
Brandmaier, Andreas M.; Prindle, John J.; McArdle, John J.; Lindenberger, Ulman

Finding structure in data using multivariate tree boosting.
Page 583-602
Miller, Patrick J.; Lubke, Gitta H.; McArtor, Daniel B.; Bergeman, C. S.

Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.
Page 603-620
Chapman, Benjamin P.; Weiss, Alexander; Duberstein, Paul R.

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Monday, December 05, 2016

Human intelligence research four-levels of explanation: Connecting the dots - an Oldie-But-Goodie (OBG) post

Click on image to enlarge.

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.



Saturday, December 03, 2016

Research Byte: Expertise and individual differences: the search for the structure and acquisition of experts’ superior performance via BrowZine

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



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Research Bytes: Individual differences in human brain development via BrowZine

Individual differences in human brain development
Brown, Timothy T.
Wiley Interdisciplinary Reviews: Cognitive Science: Articles in press



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Research Bytes: What is the Flynn Effect, and how does it change our understanding of IQ? via BrowZine

What is the Flynn Effect, and how does it change our understanding of IQ?
Shenk, David
Wiley Interdisciplinary Reviews: Cognitive Science: Articles in press



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Princeton-led team finds new method to improve predictions

For my quant readers.

Princeton-led team finds new method to improve predictions

Researchers at Princeton, Columbia and Harvard have created a new method to analyze big data that better predicts outcomes in health care,…

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