Beyond IQ: Mining the “no-mans-land” between Intelligence and IQ: Journal of Intelligence special issue

I am pleased to see the Journal of Intelligence addressing the integration of non-cognitive variables (personality; self-beliefs; motivational constructs; often called the “no-mans land” between intelligence and personality— I believe this catchy phrase was first used by Stankov) with intellectual constructs to better understanding human performance. I have had a long-standing interest in such comprehensive models as reflected by my articulation of the Model of Academic Competence and Motivation (MACM) and repeated posting of “beyond IQ” information at my blogs.

Joel Schneider and I briefly touched in this topic in our soon to be published CHC intelligence theory update chapter. Below is the select text and some awesome figures crafted by Joel.

Our simplified conceptual structure of knowledge abilities is presented in Figure 3.10. At the center of overlapping knowledge domains is general knowledge—knowledge and skills considered important for any member of the population to know (e.g., literacy, numeracy, self-care, budgeting, civics, etiquette, and much more). The bulk of each knowledge domain is the province of specialists, but some portion is considered important for all members of society to know. Drawing inspiration from F. L. Schmidt (2011, 2014), we posit that interests and experience drive acquisition of domain-specific knowledge.

In Schmidt's model, individual differences in general knowledge are driven largely by individual differences in fluid intelligence and general interest in learning, also known as typical intellectual engagement (Goff & Ackerman, 1992). In contrast, individual differences in domain-specific knowledge are more driven by domain-specific in-terests, and also by the “tilt” of one's specific abilities (Coyle, Purcell, Snyder, & Richmond, 2014; Pässler, Beinicke, & Hell, 2015). In Figure 3.11, we present a simplified hypothetical synthesis of several ability models in which abilities, interests, and personality traits predict general and specific knowledge (Ackerman, 1996a, 1996b, 2000; Ackerman, Bowen, Beier, & Kanfer, 2001; Ackerman & Heggestad, 1997; Ackerman & Rolfhus, 1999; Fry & Hale, 1996; Goff & Ackerman, 1992; Kail, 2007; Kane et al., 2004; Rolfhus & Ackerman, 1999; Schmidt, 2011, 2014; Schneider et al., 2016; Schneider & Newman, 2015; Woodcock, 1993; Ziegler, Danay, Heene, Asendorpf, & Bühner, 2012).

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Can emotional intelligence (Gei) be trained: A meta-analysis

Can emotional intelligence be trained? A meta-analysis

Please cite this article as: Mattingly, V., Human Resource Management Review (2018), https://doi.org/10.1016/j.hrmr.2018.03.002

Victoria Mattingly, Kurt Kraiger

Keywords: Emotional intelligence, Training Meta-analysis

A B S T R A C T

Human resource practitioners place value on selecting and training a more emotionally in-telligent workforce. Despite this, research has yet to systematically investigate whether emo-tional intelligence can in fact be trained. This study addresses this question by conducting a meta-analysis to assess the effect of training on emotional intelligence, and whether effects are mod-erated by substantive and methodological moderators. We identified a total of 58 published and unpublished studies that included an emotional intelligence training program using either a pre-post or treatment-control design. We calculated Cohen's d to estimate the effect of formal training on emotional intelligence scores. The results showed a moderate positive effect for training, regardless of design. Effect sizes were larger for published studies than dissertations. Effect sizes were relatively robust over gender of participants, and type of EI measure (ability v. mix-edmodel). Further, our effect sizes are in line with other meta-analytic studies of competency-based training programs. Implications for practice and future research on EI training are discussed.

See prior Gei posts here and here.


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AJT CHC Intelligence Test launch in Jakarta -measure of 9 broad CHC abilities

Yesterday’s AJT CHC cognitive test launch in Jakarta was a big success. I was taken aback by the special “event” flavor. Extremely professional. As I’ve stated before, the AJT is based on an Indonesia norm sample of 4,800 and will be one if the most comprehensive intelligence tests in the world (on par with the WJ IV COG). It measures 9 broad CHC domains (Gf, Gc, Gwm, Ga, Gv, Gs, Gl, Gr, and some of Gp-separate from cognitive). This has been the most personally rewarding and important project I have worked on in my 40+ years in psychology and education. It is bringing the core concept of individual differences to the education system of the fourth largest country in the world.

George and Laurel Tahija (see picture below), and their YDB foundation, are the visionaries behind this project and other projects focused on trying to help unique learners in their country. In my five years on this project I can say that I’ve never worked with so many nice people . It was a grand effort by many. I am very impressed how together we built such a comprehensive and technically sound battery of tests from scratch. I have developed a fondness for Indonesia and the people of this wonderful country. The genuine warmth and enthusiasm of the participants was personally moving.

For more information check out these two links (one; two)

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The importance of differential psychology for school learning: 90% of school achievement variance is due to student characteristics

This is why the study of individual differences/differential psychology is so important. If you don’t want to read the article you can watch a video of Dr. Detterman where he summarizes his thinking and this paper.

Education and Intelligence: Pity the Poor Teacher because Student Characteristics are more Significant than Teachers or Schools. Article link.

Douglas K. Detterman

Case Western Reserve University (USA)

Abstract

Education has not changed from the beginning of recorded history. The problem is that focus has been on schools and teachers and not students. Here is a simple thought experiment with two conditions: 1) 50 teachers are assigned by their teaching quality to randomly composed classes of 20 students, 2) 50 classes of 20 each are composed by selecting the most able students to fill each class in order and teachers are assigned randomly to classes. In condition 1, teaching ability of each teacher and in condition 2, mean ability level of students in each class is correlated with average gain over the course of instruction. Educational gain will be best predicted by student abilities (up to r = 0.95) and much less by teachers' skill (up to r = 0.32). I argue that seemingly immutable education will not change until we fully understand students and particularly human intelligence. Over the last 50 years in developed countries, evidence has accumulated that only about 10% of school achievement can be attributed to schools and teachers while the remaining 90% is due to characteristics associated with students. Teachers account for from 1% to 7% of total variance at every level of education. For students, intelligence accounts for much of the 90% of variance associated with learning gains. This evidence is reviewed


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CHC intelligence theory update: Live chat or later YouTube viewing from #pscyhedpodcast this Sunday evening

I am looking forward to talking about the Cattell-Horn-Carroll (CHC) model of intelligence on the #psychedpodcast this sunday evening.

I will present material largely based on the forthcoming CHC chapter coauthored with Dr. Joel Schneider.  Tune it....it shall be fun. Or, watch the discussion later on YouTube, and eventually as an audio podcast on iTunes

BB (blatant brag): McGrew CHC 2009 article in Intelligence #1 (2008-2015) and top #10 all time

This was a pleasant surprise. I knew my 2009 Intelligence article was cited frequently but I never knew it was number one from 2008-2015 and it made the top 10 all time list for the journal Intelligence. I believe this is a reflection of the impact the CHC taxonomy has had. This should make my mom proud. Here is a link to the original article.

Bibliometric analysis across eight years 2008–2015 of Intelligence articles: An updating of Wicherts (2009). Article link.

Bryan J. Pesta

Abstract

I update and expand upon Wicherts' (2009) editorial in Intelligence. He reported citation counts of papers pub-lished in this journal from 1977 to 2007. All these papers are now at least a decade old, and many more new articles have been published since Wichert's analysis. An updated study is needed to help (1) quantify the journal's more recent impact on the scientific study of intelligence, and (2) alert researchers and educators to highly-cited articles; especially newer ones. Thus, I conducted a bibliometric analysis of all articles published here from 2008 to 2015. Data sources included both the Web of Science (WOS), and Google Scholar (GS). The eight-year set comprised 619 articles, published by 1897 authors. The average article had 17.0 (WOS), and 32.9 (GS) citations overall (2.75, and 5.33 citations per year, respectively). These metrics compare favorably with those from other psychology journals. In addition, a list of the most prolific authors is provided. Also reported is a list showing many articles in this set with counts greater than one hundred, and an updated top 25 list for the history of this journal.


“Also noteworthy is that nine of the articles in the old list (not shown here) dropped off the new list. Of their replacements, only three of the nine were published within the last decade: Deary, Strand, Smith, and Fernandes (2007); McGrew (2009), and Strenze (2007). The McGrew (2009) paper is again notable. It is the only article in my newer set (2008–2015) to make the all-time list. The paper ranks ninth on the all-time list with 281 citations, just eight years after being published.”


More recent Google Scholar citation info indicates that the article is still going strong from 2016-2017.

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Research Byte: Strong working memory (WM)--fluid intelligence (Gf) relationship not due to time allowed on both sets of tasks

Very good article that does not support Chuderski's research that had suggested a relationship between time on task (not the same as cognitive processing speed-Gs) and fluid reasoning or working memory. The current study reinforces the very high (but not 1.0) effect size from working memory to Gf. However, how much time an individual (at least for young adults) spends on working memory or fluid tasks does not explain the strong WM--Gf relation. Generalization to children and the elderly cannot be made without further research.

What I find particularly interesting is the authors hypothesis that one possible general mechanism explanation for the WM-->Gf link is temporal based processing of information. This is consistent with the temporal power resolution hypotheses (or temporal g) of Rammsayer and colleagues and a large body of research I have reported at the Brain Clock blog. If you visit that link, pay particular attention to the MindHub Pub2 that presents a three-level hypothesized model for understanding the IM effect. Note that at the lowest neurocognitive and biological level of intelligence research, I have hypothesized that temporal g (and not Jensen's reaction time g) may be one of the key domain-general mechanisms driving critical cognitive abilities, especially working memory and fluid intelligence.

As per the recent four-level reductionistic framework (see brief 10 minute video explanation) I have offered to organize intelligence related research (adapted from Earl Hunt's work), the current study links research at the psychometric, information processing, and neurocognitive and biological (neural efficiency) levels.

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Working memory training (n-back task) improves fluid intelligence (Gf) 3-4 IQ points

Interesting meta-analysis suggestingthat working memory training, via the n-back task, over a relatively short period of time, can improve fluid intelligence/reasoning (Gf). Conservative estimate of 3-4 Gf IQ points improvement. Click on images to enlarge.









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Motor skill acquisition meta-analysis: File under Gp (broad motor domain) in CHC model

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McGrew (2009) CHC article # 1 cited article in Intelligence since 2008

Warning, this is a blow-my-own-horn post.

Today I visited the ISIR journal of Intelligence web page---the premiere journal for intelligence scholars. I was pleased to see that my 2009 publication, "CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research" has been, according to Scopus, the number one cited article in the journal since 2008. I am humbled and thankfull. If it was not for Doug Detterman's invitation to write this invited article, I would not now have this honor. The last time I checked, many many months ago, it was at #4. This would make my mom and dad proud. A copy of the article can be downloaded here.

 

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CHC theory tipping point passed: Inroads in mainstream intelligence research

In 2005 I unilaterally claimed that the Cattell-Horn-Carroll (CHC) theory of cognitive abilities had reached the "tipping point" in school psychology--it had become the consensus psychometric framework from which new intelligence tests are developed, old ones are revised, and non-CHC batteries are analyzed. Later in 2007 I again revisited my "tipping point" claim by analyzing the use of keywords in the National Association of School Psychologists (NASP) general service listserv. At that time I concluded that the actual tipping point occurred (in school psychology) sometime between 2001 and 2003.

Today I decided to see if the school psychology CHC tipping point had spilled over and gained traction in more mainstream psychology. In particular, I was interested in how often the terms "CHC" or "Cattell-Horn-Carroll" were present in articles in THE premiere journal outlet for the heavy hitters in the field of intelligence research--the journal Intelligence.

So...I went to the journal's web page and used the above two terms/phrases and asked for a search of "all fields" for the journal. Below is what I found.

Prior to 2004 there was NOT ONE article in Intelligence that mentioned CHC or Cattell-Horn-Carroll theory. However, since 2004 there have been at least 21 publications that reference this model of intelligence.

It is my opinion that CHC theory clearly reached a tipping point somewhere between 2001-2003 and it is now making strong inroads as one of the most supported models of the structure of human intelligence in the field of intelligence research.

Don't you just love good data? [If the images below look small--double click on them and they should eventually become larger in your browser]















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CNTRICS: Consensus-based cognitive measurement in schizophrenia--a model worth examinig

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Schizophrenia is not my cup of tea...but good cognitive measurement is. Thus, I was intrigued when doing my weekly "IQs Recent Literature of Interest" searching when I stumbled across an intriguing set of articles in the Schizophrenia Bulletin [Vol. 35 (1) 2009].

I was impressed to find that a group of scientists studying a common disorder (schizophrenia) had engaged in a consensus-building process to identify common sets of cognitive measures to use across their various research labs. This was all part of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) initiative. What a good model for improving the quality of research across researchers!

Maybe this model consensus-building activity could be adapted by those of us studying intelligence, the IQ Brain Clock, and cognitive related disorders in education and rehabilitation. Instead of our constant problem in comparing research studies with different measures used by different researchers, we could, at a minimum, at least establish a core set of "marker" measures to embed in each others favorite research batteries. Yes....at times I can be naive....but I believe in the power of consensus-building to improve research...and, more importantly, the probability of improving the quality of life for individuals with cognitive-related deficits and learning disorders. I've made a related plea for the adaptation of a common cognitive nomenclature/taxonomy (CHC theory) in many articles/chapters, most recently in the journal Intelligence.

Below are the abstracts. I've provided a link to the editorial introductory article. If anyone is interested in reading one or more of the other articles, articles that focus on measuring executive control, working memory, social cognitive and affective measures, promising paradigms, and control of attention, let me know...and I'd send a copy, but only in exchange for a guest blog post. The articles are worth a read, if for on other reason, for the nifty way many of the tasks discussed are presented via visual figures (see example of stroop task at the top of this post)--nice stuff.

Below are the abstracts:

Selecting Paradigms From Cognitive Neuroscience for Translation into Use in Clinical Trials: Proceedings of the Third CNTRICS Meeting (click here to read introductory editorial)

• This overview describes the goals and objectives of the third conference conducted as part of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) initiative. This third conference was focused on selecting specific paradigms from cognitive neuroscience that measured the constructs identified in the first CNTRICS meeting, with the goal of facilitating the translation of these paradigms into use in clinical trials contexts. To identify such paradigms, we had an open nomination process in which the field was asked to nominate potentially relevant paradigms and to provide information on several domains relevant to selecting the most promising tasks for each construct (eg, construct validity, neural bases, psychometrics, availability of animal models). Our goal was to identify 1–2 promising tasks for each of the 11 constructs identified at the first CNTRICS meeting. In this overview article, we describe the on-line survey used to generate nominations for promising tasks, the criteria that were used to select the tasks, the rationale behind the criteria, and the ways in which breakout groups worked together to identify the most promising tasks from among those nominated. This article serves as an introduction to the set of 6 articles included in this special issue that provide information about the specific tasks discussed and selected for the constructs from each of 6 broad domains (working memory, executive control, attention, long-term memory, perception, and social cognition).

CNTRICS Final Task Selection: Executive Control

• The third meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) was focused on selecting promising measures for each of the cognitive constructs selected in the first CNTRICS meeting. In the domain of executive control, the 2 constructs of interest were ‘‘rule generation and selection’’ and ‘‘dynamic adjustments in control.’’ CNTRICS received 4 task nominations for each of these constructs, and the breakout group for executive control evaluated the degree to which each of these tasks met prespecified criteria. For rule generation and selection, the breakout group for executive control recommended the intradimensional/ extradimensional shift task and the switching Stroop for translation for use in clinical trial contexts in schizophrenia research. For dynamic adjustments in control, the breakout group recommended conflict and error adaptation in the Stroop and the stop signal task for translation for use in clinical trials. This article describes the ways in which each of these tasks met the criteria used by the breakout group to recommend tasks for further development.

CNTRICS Final Task Selection: Working Memory

• The third meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) was focused on selecting promising measures for each of the cognitive constructs selected in the first CNTRICS meeting. In the domain of working memory, the 2 constructs of interest were goal maintenance and interference control. CNTRICS received 3 task nominations for each of these constructs, and the breakout group for working memory evaluated the degree to which each of these tasks met prespecified criteria. For goal maintenance, the breakout group for working memory recommended the AX-Continuous Performance Task/Dot Pattern Expectancy task for translation for use in clinical trial contexts in schizophrenia research. For interference control, the breakout group recommended the recent probes and operation/ symmetry span tasks for translation for use in clinical trials. This article describes the ways in which each of these tasks met the criteria used by the breakout group to recommend tasks for further development.

CNTRICS Final Task Selection: Social Cognitive and Affective Neuroscience–Based
Measures

• This article describes the results and recommendations of the third Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia meeting related to measuring treatment effects on social and affective processing. At the first meeting, it was recommended that measurement development focuses on the construct of emotion identification and responding. Five Tasks were nominated as candidate measures for this construct via the premeeting web-based survey. Two of the 5 tasks were recommended for immediate translation, the Penn Emotion Recognition Task and the Facial Affect Recognition and the Effects of Situational Context, which provides a measure of emotion identification and responding as well as a related, higher level construct, context-based modulation of emotional responding. This article summarizes the criteria-based, consensus building analysis of each nominated task that led to these 2 paradigms being recommended as priority tasks for development as measures of treatment effects on negative symptoms in schizophrenia.

Perception Measurement in Clinical Trials of Schizophrenia: Promising Paradigms
From CNTRICS

• The third meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) focused on selecting promising measures for each of the cognitive constructs selected in the first CNTRICS meeting. In the domain of perception, the 2 constructs of interest were gain control and visual integration. CNTRICS received 5 task nominations for gain control and three task nominations for visual integration. The breakout group for perception evaluated the degree to which each of these tasks met prespecified criteria. For gain control, the breakout group for perception believed that 2 of the tasks (prepulse inhibition of startle and mismatch negativity) were already mature and in the process of being incorporated into multisite clinical trials. However, the breakout group recommended that steady-state visualevoked potentials be combined with contrast sensitivity to magnocellular vs parvocellular biased stimuli and that this combined task and the contrast-contrast effect task be recommended for translation for use in clinical trial contexts in schizophrenia research. For visual integration, the breakout group recommended the Contour Integration and Coherent Motion tasks for translation for use in clinical trials. This manuscript describes the ways in which each of these tasks met the criteria used by the breakout group to evaluate and recommend tasks for further development.

CNTRICS Final Task Selection: Control of Attention

• The construct of attention has many facets that have been examined in human and animal research and in healthy and psychiatrically disordered conditions. The Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) group concluded that control of attention—the processes that guide selection of taskrelevant inputs—is particularly impaired in schizophrenia and could profit from further work with refined measurement tools. Thus, nominations for cognitive tasks that provide discrete measures of control of attention were sought and were then evaluated at the third CNTRICS meeting for their promise for future use in treatment development. This article describes the 5 nominated measures and their strengths and weaknesses for cognitive neuroscience work relevant to treatment development. Two paradigms, Guided Search and the Distractor Condition Sustained Attention Task, were viewed as having the greatest immediate promise for development into tools for treatment research in schizophrenia and are described in more detail by their nominators.

Technorati Tags: psychology, school psychology, educational psychology, cognition, neuropsychology, neuroscience, schizophrenia, CNTRICS, cognition, intelligence, CHC, CHC theory, Cattell-Horn-Carroll

Timing post over at IQ's Corner

FYI cross-blog post. Check out post I just made at IQ's Corner regarding rapid auditory processing (timing based), auditory processing (Ga) as per the CHC theory of cognitive abilities, and the general internal brain clock research.

More on olfaction and age-related cognitive decline

Thanks to the Brain Blog for yet another empirical study that demonstrates the importance of olfactory abilities (Go) in relationship to age-related cognitive decline (see prior post).

Technorati Tags: psychology, educational psychology, neuropsychology, neuroscience, CHC theory, intelligence, Go, olfactory abilities, olfaction, Alzheimers

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CHC (Cattell-Horn-Carroll) listserv n=900+

I'm pleased to announce that the IAP-CHC listerv has recently surpassed the n=900+ membership threshold. Only approximately 100 more members and we shall reach a critical mass of n=1000. If you are a routine reader of IQs Corner Blog, you might want to join the CHC listserv in order to monitor/participate in ongoing CHC and intellectual assessment chatter.

Visit the link above to learn more about the CHC listerv. Below is a brief description.

• This is an unmoderated discussion list regarding theoretical, research,
  measurement assessment issues related to the CHC theory of
  intelligence. It is also the FYI list for the Institute for Applied
  Psychometrics,llc (IAP)

Technorati Tags: psychology, educational psychology, school psychology, neuropsychology, neuroscience, listserv, CHC, Cattell Horn Carroll, intelligence, IQ tests, IQ scores

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More on Go (olfactory abilities)

Thanks to Omni Brain for the FYI post regarding the "oflactometer" from the Berkely Olfactory Research Project (BORP). In prior posts I've highlighted emerging research that implicates Go (General olfactory abilities) as an important component of Cattell-Horn-Carroll (CHC theory of cognitive abilities).

Technorati Tags: psychology, educational psychology, school psychology, neuropsychology, neuroscience, CHC theory, CHC, Cattell Horn Carroll, Go, olfactory, olfacdtion, smell, cognition, intelligence

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Neuroscience of smell and taste

Thanks to Mind Hacks for the tip re: an article in Nature about our senses of smell and flavor. This is a nice follow-up to my recent post (The nose "knows") re: recent research on the importance of Go (olfactory) abilities in the CHC taxonomy of human cognitive abilities.

Technorati Tags: psychology, educational psychology, school psychology, neuropsychology, neuroscience, cognition, cognitive, intelligence, IQ, smell, olfaction, olfactory, Go, taste

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