Thursday, January 28, 2016

"Intelligent" intelligence testing with the W IV Tests of Cognitive Ability #3: Within-CHC assessment trees - a Gf "tease"

I have decided to temporarily skip the planned third installment in this series, and provide a "tease" for a small fraction of the "intelligent" testing material I will be positing in this series.  I will post an introduction to "intelligent" intelligence testing is (as per Kaufman and as applied to the WJ IV COG/OL) after this tease post.

One feature of Alan Kaufman's "intelligent" testing with the Wecshler series has been the provision of supplemental test groupings--groups of tests that may measure a shared common ability, but a group that is not one of the test's published clusters or indexes.

I have developed what I call "Within-CHC domain assessment and interpretation trees" for all 7 CHC domains in the WJ IV COG.  I developed these assessment trees by reviewing and integrating the following sources of information.

Close examination of the CFA results in the WJ IV Technical Manual (TM)

Close examination of the EFA, cluster analysis and MDS results in WJ IV TM

Additional unpublished EFA, CFA, cluster analysis and MDS (2D & 3D) completed post-WJ IV publication (across ages 6-19)

Review of supplemental/clinical groupings for the WJ, WJ-R and WJ III (e.g., McGrew, 1986; 1984--my two WJ COG books)

Extensive unpublished “Beyond CHC”  analysis of the WJ III data

Theoretical and clinical considerations

Below is the within-Gf assessment tree.  Click on images to enlarge for clear viewing.

(Note.  Since making this original post, I have now added a tabular version of the above information below.  Also, a clean PDF copy of both images can be found here.)

The dark arrows with bold font labels designate the Gf clusters provided by the WJ IV.  You will see Gf, Gf-Ext, and Quantitative Reasoning.  The dashed lines suggest other tests that might be important to inspect when evaluating a person's Gf abilities.  Note the line from Gf-Ext to the Visualization test.  It is labeled Gf-Ext 4/Gf+Gv hybrid.  This label is not in bold, indicating that it is not a cluster with score norms.  Close inspection of all data analyses of the WJ IV norm data found the Visualization test tending to "hang out" or near the primary Gf tests.  Also, as reported by Carroll (1993), sometimes Gv and Gf tests frequently would form a Gf/Gv hybrid factor (it is well known that some times factor analysis has a hard time differentiating Gf and Gv indicators).  This grouping  suggests that examiners should look to see if the Visualization test is consistent with the other Gf tests....which may reflect more shared Gf variance than anything specific to the Visualization test.

Also notice the Quantitative Reasoning-Ext (RQ) supplemental grouping,  This suggests that if the Quantitative Reasoning score is either high or low, on should inspect the Number Matrices and Applied Problems tests from the ACH battery---they, at times, will "follow" the scores on the Quantitative Reasoning  cluster.

Finally, one set of CFA models in the WJ IV TM suggested a possible Gf-Verbal vs Gf-Quantitative split.  The Verbal Reasoning supplemental grouping consists of the Concept Formation, Analysis-Synthesis, Oral Vocabulary, and Passage Comprehension tests.  Below the is a section of the CFA results that support the possible Gf-Verbal and Gf-Quantitative distinction.  This information is in the WJ IV Technical Manual.  This information suggests that the TM can be your "friend."  It contains considerable valuable information regarding tests that are not part of a cluster, but that showed evidence of some shared variance with a possible published cluster, or new clinical supplemental test groupings I will present.

Relevant Gf broad and narrow definitions are below:

Fluid reasoning (Gf): The use of deliberate and controlled focused attention to solve novel “on the spot” problems that cannot be solved solely by using prior knowledge (previously learned habits, schemas, or scripts).  Reasoning that depends minimally on learning and acculturation.
  • Induction (I): The ability to infer general implicit principles or rules that govern the observed behavior of a phenomenon or the solution to a problem.  Rule discovery.
  • General sequential reasoning (RG): The ability to reach logical conclusions from given premises and principles, often in a series of two or more sequential steps.  Deductive reasoning.
  • Quantitative reasoning (RQ): The ability to reason, either with induction or deduction, with numbers or mathematical relations, operations and algorithms.
Given that I know that people tend to not to devour technical manuals like I do, my assessment trees are aids that incorporate all of this information in visual-graphic form--saving you from having to extract this potential interpretation-relevant information from the TM.

Stay tuned.  Some of the within-CHC assessment trees suggest many more test groupings to consider for clinical interpretation (than this Gf example.)

I, Kevin McGrew, am solely responsible for this content.  The information presented here (and in this series) does not necessarily reflect the views of my WJ IV coauthors or that of the publisher of the WJ IV. 

Click on images to enlarge

Wednesday, January 27, 2016

Research Byte: Neurocognitive and Behavioral Predictors of Math Performance in Children With and Without ADHD via BrowZine

Neurocognitive and Behavioral Predictors of Math Performance in Children With and Without ADHD
Antonini, T. N.; Kingery, K. M.; Narad, M. E.; Langberg, J. M.; Tamm, L.; Epstein, J. N.
Journal of Attention Disorders, Vol. 20 Issue 2 – 2016: 108 - 118


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Research Byte: Mathematical Problem-Solving Abilities and Chess | SAGE Open

Mathematical Problem-Solving Abilities and Chess | SAGE Open

Abstract Chess is thought to be a game demanding high cognitive abilities to be played well. Although many studies proved the link between…

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Research Byte: The Frequency of Rapid Pupil Dilations as a Measure of Linguistic Processing Difficulty

Cool assessment concept.

The Frequency of Rapid Pupil Dilations as a Measure of Linguistic Processing Difficulty

by Vera Demberg, Asad SayeedWhile it has long been known that the pupil reacts to cognitive load, pupil size has…

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Research Byte: Siblings' sex is linked to mental rotation performance in males but not females via BrowZine

Siblings' sex is linked to mental rotation performance in males but not females
Frenken, Hannah; Papageorgiou, Kostas A.; Tikhomirova, Tatiana; Malykh, Sergey; Tosto, Maria G.; Kovas, Yulia
Intelligence, Vol. 55 – 2016: 38 - 43


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Sunday, January 24, 2016

"Intelligent" testing with the WJ IV Tests of Cognitve Ability #2: Connecting the dots of relevant intelligence research

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.

Friday, January 22, 2016

"Intelligent" testing with the WJ IV Tests of Cognitive Ability: #1: The big picture perspective

Today I am launching a new series called "Intelligent" testing with the WJ IV Tests of Cognitive Ability (credit goes to Dr. Alan Kaufman for coining this term and approach to clinical intelligence test interpretation).

Since the WJ IV was published in 2014, after spending more than a year traveling the country introducing the WJ IV battery to various professional groups, it has become clear that users are hungry for more advanced interpretation material, particularly for the cognitive ability tests.

I have been busy the past 6 months completing all kinds of new analyses, reading literature, and revisiting the information in the technical manual.  As a result, I have developed some new advanced interpretation material for the cognitive (and related oral language) tests.  I will be presenting a large portion of this material at my half-day workshop at NASP in New Orleans in February.

Before presenting the new material, I first believe it is important that individuals have a proper "big picture" perspective on the strengths and limitations of intelligence testing.  I have presented this big picture material in a brief YouTube video I posted previously.  It is provided (again) below.  There will be one more additional "big picture" post prior to delving into the WJ IV specific information.

Stay tuned.  The scheduling of these posts will be on a "as I have time" basis.

Research Byte: Processing speed differences between 70- and 83-year-olds matched on childhood IQ via BrowZine

Processing speed differences between 70- and 83-year-olds matched on childhood IQ
Deary, Ian J.; Ritchie, Stuart J.
Intelligence, Vol. 55 – 2016: 28 - 33


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Tuesday, January 19, 2016

Research Byte: Study suggests writing places greater demands on working memory than reading and listening

Logo of advcogpsychAbout ACPSubscribeSumit a manuscriptACP Journal
Adv Cogn Psychol. 2015; 11(4): 147–155.
Published online 2015 Dec 31. doi:  10.5709/acp-0179-6
PMCID: PMC4710969

Writing, Reading, and Listening Differentially Overload Working Memory Performance Across the Serial Position Curve


Previous research has assumed that writing is a cognitively complex task, but has not determined if writing overloads Working Memory more than reading and listening. To investigate this, participants completed three recall tasks. These were reading lists of words before recalling them, hearing lists of words before recalling them, and hearing lists of words and writing them as they heard them, then recalling them. The experiment involved serial recall of lists of 6 words. The hypothesis that fewer words would be recalled overall when writing was supported. Post-hoc analysis revealed the same pattern of results at individual serial positions (1 to 3). However, there was no difference between the three conditions at serial position 4, or between listening and writing at positions 5 and 6 which were both greater than recall in the reading condition. This suggests writing overloads working memory more than reading and listening, particularly in the early serial positions. The results show that writing interferes with working memory processes and so is not recommended when the goal is to immediately recall information.
Keywords: working memory, reading, listening, writing, serial recall

Monday, January 18, 2016

F.T.C.’s Lumosity Penalty Doesn’t End Brain Training Debate

F.T.C.'s Lumosity Penalty Doesn't End Brain Training Debate

From The New York Times on Flipboard

A few years ago, Jennifer Perrine saw a television ad for Lumosity, an online brain training program, and decided she'd give it a try. Her…

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Sunday, January 17, 2016

Beyond Born versus Made: A New Look at Expertise - S Kaufman provides link to article

I made a "research byte" post about this article this past week.  Scott Barry Kaufman has also posted a comment, and, if you want to read the article, he has provided a link to the complete article in PDF>

Beyond Born versus Made: A New Look at Expertise

Why are some people so much more successful than other people in music, sports, games, business, and other complex domains? This question is the…

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Saturday, January 16, 2016

Research byte: Excellent article on multiple causes of expertise development

This is an excellent integrative review of the various causes (multiple--no single cause) of the development of expertise in different domains. I love the "big picture" model integration figure (it belongs in the Gv Gallery Hall of Fame). My only complaint is that the review failed to recognize the very relevant and important work of Richard Snow on the development of aptitude...which uses a similar big picture integrative model that touches on many of the same explanatory variables.

Click on images to enlarge



Research byte: ADHD students may not have slower cognitive speed--but may be more variable (inconsistent) in cognitive speed

Interesting new meta-analysis. File under Gt in CHC taxonomy of human abilities. Click on images to enlarge for easier reading



Friday, January 15, 2016

Intelligence 'networks' discovered in brain for the first time

Intelligence 'networks' discovered in brain for the first time

Scientists from Imperial College London have identified for the first time two clusters of genes linked to human intelligence.

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STUDY ALERT: On the reception and detection of pseudo-profound bullshit

STUDY ALERT: On the reception and detection of pseudo-profound bullshit

On the reception and detection of pseudo-profound bullshit Gordon Pennycook, James Allan Cheyne, Nathaniel Barr, Derek J.…

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Tuesday, January 12, 2016

Research byte: Beyond born vs made - A new look at expertise

Beyond Born versus Made: A New Look at Expertise


Why are some people so much more successful than other people in music, sports, games, business, and other complex domains? This question is the subject of one of psychology's oldest debates. Over 20 years ago, Ericsson, Krampe, and Tesch-Römer (1993) proposed that individual differences in performance in domains such as these largely reflect accumulated amount of “deliberate practice.” More controversially, making exceptions only for height and body size, Ericsson et al. explicitly rejected any direct role for innate factors (“talent”) in the attainment of expert performance. This view has since become the dominant theoretical account of expertise and has filtered into the popular imagination through books such as Malcolm Gladwell's (2008) Outliers. Nevertheless, as we discuss in this chapter, evidence from recent research converges on the conclusion that this view is not defensible. Recent meta-analyses have demonstrated that although deliberate practice accounts for a sizeable proportion of the variance in performance in complex domains, it consistently leaves an even larger proportion of the variance unexplained and potentially explainable by other factors. In light of this evidence, we offer a “new look” at expertise that takes into account a wide range of factors.


  • Cognitive ability;
  • Deliberate practice;
  • Expert performance;
  • Expertise;
  • Genetics;
  • Individual differences;
  • Intelligence;
  • Skilled performance;
  • Talent

1. Introduction

No one can deny that some people are vastly more skilled than other people in certain domains. Consider that the winning time for the New York City Marathon in 2014—just under 2 h and 11 min—was more than 2 h better than the average finishing time ( Or consider that Jonas von Essen, en route to winning the 2014 World Memory Championships, memorized 26 decks of cards in an hour (
What are the origins of this striking variability in human expertise?1 Why are some people so much better at certain tasks than other people? One particularly influential theoretical account attempts to explain individual differences in expertise in terms of deliberate practice (e.g., Boot and Ericsson, 2013, Ericsson, 2007, Ericsson et al., 1993, Ericsson et al., 2005 and Keith and Ericsson, 2007). Here, we describe the mounting evidence that challenges this view. This evidence converges on the conclusion that deliberate practice is an important piece of the expertise puzzle, but not the only piece, or even necessarily the largest piece. In light of this evidence, we offer a “new look” at expertise that takes into account a wide range of factors, including those known to be substantially heritable.
The rest of the chapter is organized into the following sections. We describe the deliberate practice view (Section 2) and then review evidence that challenges it (Section 3). Then, we review evidence for factors other than deliberate practice that may also account for individual differences in expertise (Section 4). We then describe an integrative approach to research on expertise (Section 5). Finally, we summarize our major findings and comment on directions for future research (Section 6).

2. The Deliberate Practice View

The question of what explains individual differences in expertise is the topic of one of psychology's oldest debates. One view is that experts are “born.” This view holds that although training is necessary to become an expert, innate ability—talent—limits the ultimate level of performance that a person can achieve in a domain. Nearly 150 years ago, in his book Hereditary Genius, Francis Galton (1869) argued for this view based on his finding that eminence in domains such as music, science, literature, and art tends to run in families, going so far as to conclude that “social hindrances cannot impede men of high ability, from becoming eminent [and] social advantages are incompetent to give that status, to a man of moderate ability” (p. 41). The opposing view is that experts are “made.” This view argues that if talent exists at all, its effects are overshadowed by training. John Watson (1930), the founder of behaviorism, championed this view when he guaranteed that he could take any infant at random and train him to become “any type of specialist [he] might select...regardless of his talents” (p. 104).
The modern era of scientific research on expertise traces back to the 1940s and the research of the Dutch psychologist Adriaan de Groot (1946/1978). Himself an internationally competitive chess player, de Groot investigated the thought processes underlying chess expertise using a “choice-of-move” paradigm in which he gave chess players chess positions and instructed them to verbalize their thoughts as they considered what move to make. From analyses of their verbal reports, de Groot discovered that there was no association between skill level and the number of moves ahead a player thought in advance of the current move. Instead, he found evidence for a perceptual basis of chess expertise. As de Groot put it, the grandmaster “immediately ‘sees’ the core of the problem in the position” whereas the weaker player “finds it with difficulty—or misses it completely” (p. 320). de Groot attributed this ability to a “connoisseurship” (p. 321) that develops through years of experience playing the game.
Nearly 30 years later, de Groot's (1946/1978) work was the inspiration for Chase and Simon's (1973a) classic study of chess expertise, which marks the beginning of cognitive psychologists' interest in expertise. Testing three chess players—a master, an intermediate-level player, and a beginner—Chase and Simon found that there was a positive relationship between chess skill and memory for chess positions, but only when they were plausible game positions. When the positions were random arrangements of pieces, there was almost no effect of chess skill on memory. Based on these findings, Chase and Simon (1973b) concluded that although “there clearly must be a set of specific aptitudes...that together comprise a talent for chess, individual differences in such aptitudes are largely overshadowed by immense individual differences in chess experience. Hence, the overriding factor in chess skill is practice” (p. 279).
The experts-are-made view has held sway in the scientific literature ever since. Over 20 years ago, in a pivotal article, Ericsson et al. (1993) proposed that individual differences in performance in complex domains (music, chess, sports, etc.) largely reflect differences in the amount of time people have spent engaging in deliberate practice, which “includes activities that have been specially designed to improve the current level of performance” (p. 368). In the first of two studies, Ericsson et al. recruited violinists from a Berlin music academy and asked them to estimate the amount of hours per week they had devoted to deliberate practice since taking up the violin. The “best” violinists had accumulated an average of over 10,000 h of deliberate practice by age 20, which was about 2500 h more than the average for the “good” violinists and about 5000 h more than the average for the least accomplished “teacher” group. In a second study, Ericsson et al. found that “expert” pianists, who were selected to be similar in skill level to the good violinists in the first study, had accumulated an average of over 10,000 h of deliberate practice by age 20, compared to only about 2000 h for “amateur” pianists (see Ericsson, 2006; for further discussion of these results).
Ericsson et al. (1993) concluded that “high levels of deliberate practice are necessary to attain expert level performance” (p. 392). More controversially, they added:
Our theoretical framework can also provide a sufficient account of the major facts about the nature and scarcity of exceptional performance. Our account does not depend on scarcity of innate ability (talent) and hence agrees better with the earlier reviewed findings of poor predictability of final performance by ability tests. We attribute the dramatic differences in performance between experts and amateurs-novices to similarly large differences in the recorded amounts of deliberate practice.
Ericsson et al., (1993, p. 392), emphasis added
Ericsson et al. further claimed that “individual differences in ultimate performance can largely be accounted for by differential amounts of past and current levels of practice” (p. 392), and stated:
We agree that expert performance is qualitatively different from normal performance and even that expert performers have characteristics and abilities that are qualitatively different from or at least outside the range of those of normal adults. However, we deny that these differences are immutable, that is, due to innate talent. Only a few exceptions, most notably height, are genetically prescribed. Instead, we argue that the differences between expert performers and normal adults reflect a life-long period of deliberate effort to improve performance in a specific domain.
(p. 400)
Note to users:
Corrected proofs are Articles in Press that contain the authors' corrections. Final citation details, e.g., volume and/or issue number, publication year and page numbers, still need to be added and the text might change before final publication.
Although corrected proofs do not have all bibliographic details available yet, they can already be cited using the year of online publication and the DOI , as follows: author(s), article title, Publication (year), DOI. Please consult the journal's reference style for the exact appearance of these elements, abbreviation of journal names and use of punctuation.
When the final article is assigned to volumes/issues of the Publication, the Article in Press version will be removed and the final version will appear in the associated published volumes/issues of the Publication. The date the article was first made available online will be carried over.

Monday, January 11, 2016

Your brain is a time machine: An oldie-but-goodie (OBG) post

This is an OBG (oldie-but-goodie) post I originally made on the IM-HOME blog

Time and space are the two fundamental dimensions of our lives. All forms of human behavior require us to process and understand information we receive from our environment in either spatial or temporal patterns. Even though mental timing (temporal processing) research is in a stage of infancy (when compared to spatial processing) important insights regarding the human brain clock have emerged.

Below is a list (albeit incomplete) of some of the major conclusions regarding the human brain clock. The sources for these statements come from my review of the temporal processing and brain clock literature during the past five years. Most of this information has been disseminated at the Brain Clock blog or the Brain Clock Evolving Web of Knowledge (EWOK). The goal of this post is to provide a Readers Digest summary of the major conclusions. This material can serve as a set of "talking points" at your next social event where you can impress your friends and family as you explain why you use the high-tech IM "clapper" (with a cowbell tone no less) either as a provider or as client.

Our brains measure time constantly. It's hard to find any complex human behavior where mental timing is not involved. Timing is required to walk, talk, perform complex movements and coordinate information flow across the brain for complex human thought. Think about moving your arm and hand to grasp a coffee cup. The messages to perform this task originate in your brain, which is not directly connected to your arm, hands and fingers. The ability to perform the necessary motor movements is possible only because the mind and extremities are connected via timing. Precisely timed neural messages connect your brain and extremities. You are a time machine.

Humans are remarkably proficient at internally perceiving and monitoring time to produce precisely timed behaviors and thinking. “We are aware of how long we have been doing a particular thing, how long it has been since we last slept, and how long it will be until lunch or dinner. We are ready, at any moment, to make complex movements requiring muscle coordination with microsecond accuracy, or to decode temporally complex auditory signals in the form of speech or music. Our timing abilities are impressive…” (Lewis & Walsh, 2005, p. 389).

To deal with time, humans have developed multiple timing systems that are active over more than 10 orders of magnitude with various degrees of precision (see figure below from Buhusi & Meck, 2005). These different timing systems can be classified into three general classes (viz., circadian, interval, and millisecond timing), each associated with different behaviors and brain structures and mechanisms. The fastest timing system (millisecond or interval timing) is involved in a numerous human behaviors such as speech and language, music perception and production, coordinated motor behaviors, attention, and thinking. This fast interval timing system is the most important timing system for understanding and diagnosing clinical disorders and for developing and evaluating effective treatment interventions for educational and rehabilitation settings. It is this timing system, and the relevant research, that is relevant to understanding Interactive Metronome. (Note.  See my conflict of interest statement at this blog.  I have an ongoing consulting relationship with IM).

Although there is consensus that the human brain contains some kind of clock, the jury is still out on the exact brain mechanisms and locations. It is also not clear whether there is one functional master clock or a series of clocks deployed in different brain areas. The areas of the brain most consistently associated with milli-second interval mental timing are the cerebellum, anterior cingulate, basal ganglia, the dorsolateral prefrontal cortex, right parietal cortex, motor cortex, and the frontal-striatal loop. That is a mouthful of technical brain terms. But, if you can memorize them and have them roll of your tongue with ease you will “shock and awe” your family and friends. Most of these areas of the brain are illustrated below. Now, if you really want to demonstrate your expertise, get your own illustrated “brain-in-a-pocket”. These images were generated by the free 3D Brain app available for your iPhone or iPad. Even cooler is the fact that you can rotate the images with your finger! You can give neuroanatomy lessons anytime…anywhere!

Research suggests that mental interval timing is controlled by two sub-systems. The automatic timing system processes discrete-event (discontinuous) timing in milliseconds. The cognitively-controlled timing system deals with continuous-event timing (in seconds) that requires controlled attention and working memory. Both systems are likely involved in IM. For example, the synchronized clapping requires motor planning and execution, functions most associated with the automatic timing system. However, the cognitive aspects of IM (focus, controlled attention, executive functions) invoke the cognitively controlled timing system. Aren’t these brain images awesome?

The dominant model in the brain clock research literature is that of a centralized internal clock that functions as per the pacemaker–accumulator model. Briefly, this is a model where an oscillator beating at a fixed frequency generates tics that are detected by a counter. For now I am just going to tease you with an image of this model. You can read more about this model at the Brain Clock blog.

Research suggests that the brain mechanisms underlying mental timing can be fine-tuned (modified) via experience and environmental manipulation. Modifiability of mental interval timing and subsequent transfer suggest a domain-general timing mechanism that, if harnessed via appropriately designed timing-based interventions, may improve human performance in a number of important cognitive and motor domains.