Showing posts with label general intelligence. Show all posts
Showing posts with label general intelligence. Show all posts

Sunday, June 07, 2026

Research Alert: Generation Intelligence (Gen I): A Five-Intelligence Framework for Understanding Generational Cognitive, Emotional, Social, Spiritual, and AI Readiness Profiles

Interesting food for thought.

PDF copy of article available here at Research Gate.

Abstract

Standard generational analysis focuses on birth-year cohorts and broad demographic patterns. This paper introduces Generation Intelligence (Gen I), a multidimensional framework that refocuses the analytical lens on the formative window of ages 8 to 12, the period of peak neuroplasticity, social identity formation, and communication technology imprinting. Across six living generations (Silent, Baby Boomer, Generation X, Millennial, Generation Z, and Generation Alpha), the framework applies five intelligence dimensions, Cognitive Intelligence (IQ), Emotional Intelligence (EQ), Social Intelligence (SQ), Spiritual Intelligence (SpQ), and AI Readiness (AQ),to produce a generational intelligence matrix. The framework synthesizes established developmental psychology, the Strauss-Howe generational cycle theory, and Pew Research Center longitudinal data with emerging research on digital media's cognitive effects and AI's developmental implications. Findings suggest that each generation's distinctive intelligence profile is predictable from its formative communication environment, and that the full intelligence spectrum, not any single generation's contribution alone, is required to address the complex challenges of the 21st century. Practical implications for education, organizational leadership, human development practice, and AI governance are discussed.


Keywords: generational intelligence, emotional intelligence, spiritual intelligence, AI readiness,  formative development, neuroplasticity, intergenerational leadership, Gen I.

Click on image to enlarge for easy reading







Thursday, May 07, 2026

AI Brief: Is the Intellectual Functioning Component of AAIDD’s 12th Manual Satisficing?

AI Brief:  Is the Intellectual Functioning Component of AAIDD's 12th Manual Satisficing?

 (McGrew, 2021)




 

Dr. Kevin McGrew with assist from Google NotebookLM

 

In a commentary published in Intellectual and Developmental Disabilities, Kevin S. McGrew  evaluated the intellectual functioning section (prong 1) of the AAIDD’s 12th edition manual (2021) for diagnosing intellectual disabilities (ID). He commends the organization for finally adopting the Cattell-Horn-Carroll (CHC) theory, which aligns the manual with modern scientific consensus on cognitive abilities. However, the author expresses significant concern about the manual’s contradictory guidance on part scores, arguing that its ambiguous stance could lead to legal and diagnostic confusion. McGrew also highlights various technical measurement issues and numerous copyediting errors that he believes undermine the manual's status as an authoritative resource. He suggests that while the manual is satisfactory in its theoretical shift, it does not provide the precise clarity needed for high-stakes clinical and judicial settings.

 

_______________________

 

In his review of the 12th edition of the American Association on Intellectual and Developmental Disabilities (AAIDD) manual, McGrew (2021; link for downloading article) evaluates whether the "Intellectual Functioning Component" (aka.,prong 1 of a three-prong definition of intellectual disability—ID) provides a "satisficing"—or satisfactory and sufficient—solution for practitioners and scholars.[1] McGrew draws on over 45 years of experience in school psychology and intelligence research, theory, and test development. In addition, he draws on his expert work and consultation (since 2009) on Atkins intellectual disability (ID) death penalty cases in legal settings. McGrew provides an evaluation of the AAIDD’s manual's prong 1 (intellectual functioning) theoretical grounding, technical guidance, and professional polish. He does not evaluate the other two ID prongs (adaptive behavior and age of onset).


Advancement in Intelligence Theory

McGrew awards the manual a Grade B+ for its formal adoption of the Cattell-Horn-Carroll (CHC) theory of intelligence. This shift aligns the AAIDD manual with the contemporary consensus taxonomy of cognitive abilities, moving away from outdated models. However, McGrew notes that the manual "muddies the CHC waters" by giving preferential treatment to fluid (Gf) and crystallized (Gc) intelligence while neglecting other broad CHC abilities like learning efficiency (Gl), working memory (Gwm), retrieval fluency (Gr), auditory processing (Ga), visual-spatial processing (Gv), and processing speed (Gs). He suggests that a visual-graphic model of the CHC hierarchical model would have been a beneficial addition for users.


Measurement and Organizational Challenges

The manual receives a Grade B- for its treatment of major measurement issues. While it provides adequate coverage of such measurement issues as the standard error of measurement (SEM), confidence intervals, and the Flynn effect (aka., norm obsolescence), McGrew criticizes the lack of a topic index, which makes finding specific guidance very frustrating. For instance, practice effects are obscurely placed under "progressive error" in the glossary, and the Flynn effect is curiously categorized under "Making a Retrospective Diagnosis," despite being relevant to historical and current intellectual assessments.


The Part-Score Controversy

The most critical evaluation—a Grade C—is reserved for the manual's handling of part scores. McGrew identifies three primary failures in this area:

 

      Inconsistency: The manual contradicts itself by advising against the use of part scores as proxies for general intelligence (psychometric g) while simultaneously suggesting that their valid use requires 3–6 subtests of Gf and Gc.


      Variance with Other Authorities: This "just say no to part scores" stance conflicts with other major authoritative sources, such as the DSM-5, which acknowledges that highly discrepant subtest scores may invalidate an overall IQ score.


      Scientific and Legal Tensions: McGrew argues that the manual fails to address the "General-2-individual" (G2i) legal principle which acknowledges that group-based scientific research (e.g., suggesting full-scale scores are always superior) may not apply to every unique individual case—the G2i principle conundrum is that scientists generalize; but courts must particularize to an individual. He warns that without clearer guidance; legal entities may fill the void with "remedies of dubious quality.”


Editorial Quality and Professionalism

McGrew gives the manual a Grade D for style and substance, citing at least 20 copyedit errors in the sections relevant to the intellectual functioning prong alone. These include misspellings of prominent researchers, incorrect terminology like "test e-norms," and frequent "misplaced italics.” He contends that such preventable errors tarnish the manual’s status as an "authoritative" and "definitive" source for diagnosing intellectual disabilities.


Conclusion

McGrew concludes that while the endorsement of CHC theory is a significant positive revision, the manual’s obfuscation regarding part scores and its numerous editorial flaws represent major missed opportunities. He emphasizes that practitioners cannot wait another decade for the next edition to offer more robust guidance, particularly in high-stakes legal and diagnostic settings. He concludes that while he may be a "tough grader," his critiques are intended to push AAIDD toward more robust and clearer guidance in future editions or supplements


[1] Nobel laureate Herb Simon advanced the behavioral economics concept of satisficing (Simon, 1956)—the idea that, although we may aspire to optimal solutions, real-world constraints often require us to settle on what is both satisfactory and sufficient (hence, the portmanteau term satisficing).


Friday, February 06, 2026

Research alert: The network architecture of general intelligence in the human connectome - #g #intelligence #cognitive #cognitive #NNT #brainnetworks #schoolpsychology #schoolpsychologists

Click on images to enlarge for easy readability 



“In press” article available here.  As per the old Verizon cell phone ad - “its the network.”


ABSTRACT 

Advances in network neuroscience challenge the view that general intelligence (g) emerges from a primary brain region or network. Network Neuroscience Theory (NNT) proposes that g arises from coordinated activity across the brain's global network architecture. We tested predictions from NNT in 831 healthy young adults from the Human Connectome Project. We jointly modeled the brain's structural topology and intrinsic functional covariation patterns to capture its global topological organization. Our investigation provided evidence that g (1) engages multiple networks, supporting the principle of distributed processing; (2) relies on weak, long-range connections, emphasizing an efficient and globally coordinated network; (3) recruits regions that orchestrate network interactions, supporting the role of modal control in driving global activity; and (4) depends on a small-world architecture for system-wide communication. These results support a shift in perspective from prevailing localist models to a theory that grounds intelligence in the global topology of the human connectome. 

Tuesday, January 06, 2026

Interesting, reasonably accurate recent video on IQ/intelligence testing

I just stumbled across a relatively new video covering the history and several major issues regarding intelligence testing and IQ scores.  Two scholars that I respect (Dr. Cecil Reynolds; Dr. Stuart Ritchie) are featured in the video.  I did see some spelling errors in the subtitles (Dr. Ian Dearie instead of Dr. Ian Deary; Benet instead of Binet; using capital G when referencing Spearman's concept of general intelligence, which is always noted with an italic font small g; etc) and heard several statements that made me cringe slightly.  

Also, it left the impression that fluid and crystallized intelligence (and a lessor extent quantitative ability) are the primary recognized broad cognitive abilities measured by intelligence tests.  It did not acknowledge contemporary CHC theory as the consensus taxonomy of human cognitive abilities.  Also, it left the impression that IQ tests are "bubble in" multiple choice tests.  This may be true for group tests, but it is not the case with individually administered intelligence tests.

Overall, it is a reasonable video to share with others as an introduction, possibly in college courses where the concept of intelligence and IQ testing is being introduced.  It did a good job of covering the historical bad uses of IQ tests (e.g., discrimination; cultural bias, eugenics movement, etc.) 

The complete video is approximately 35 minutes.  It did freeze up for me at the 17 minute mark when it was going to display an ad....but I simply restarted the video and quickly moved to that point and then it continued.


 

 

 

Sunday, November 30, 2025

IQs Corner: #Cloze test performance and #cognitive abilities: A comprehensive #meta-analysis - #intelligence #Grw #Gc #CHC #g #Gf #schoolpsychologists #schoolpsychologt

This is an open access article that can be read/downloaded at this link.

Click on image to enlarge



Abstract

Cloze tests have a long history and have been used to measure various abilities, including intelligence, reading comprehension, and language proficiency. To locate cloze tests within a nomological network of cognitive abilities, we conducted a multilevel random effects meta-analysis covering 110 years of research. Studies were eligible if they provided a measure of association between a cognitive fill-in-the-blank test and any cognitive ability test. We synthesized manifest correlations from 89 studies (N = 37,912, k = 634) and found an average correlation of r = .54 (95% CI [.49, .59], k = 485) with crystallized intelligence, r = .48 (95% CI [.42, .54], k = 69) with fluid intelligence, and r =.61 (95% CI [.46, .77], k = 32) with general intelligence. While today's application of the typical cloze is to measure reading comprehension, our results revealed a similarly strong association with a broad range of crystallized abilities. Of the key moderators we investigated—text base, administration mode, deletion pattern, and response type—only the response type showed a significant effect. Sensitivity analyses supported the robustness of our findings. We conclude by revisiting the origin of the cloze test and highlighting the need for systematic studies on how different cloze test designs affect construct validity. Whereas the meta-analytic database predominantly originates from language research, where cloze tests are entrenched as markers of language proficiency, we propose reframing cloze tests as a versatile intelligence test format—just like multiple-choice tests constitute a testing method—that can be tailored to assess various specific cognitive abilities.

Thursday, October 16, 2025

IQs Corner pub alert: CHC theory of cognitive abilities used to define and evaluate AI - #AI #CHC #intelligence #schoolpsychology #schoolpsychologists #IQ #EDPSY

An exciting new paper from the Dan Hendryks et al. at the Center for AI Safety  The center is  a nonprofit with the mission “to reduce societal-scale risks from artificial intelligence.”  In this just released paper, they propose a modified CHC theory definition/framework for evaluating AI: 
  • AGI is an AI that can match or exceed the cognitive versatility and proficiency of a well-educated adult.”
Given my extensive research and publications regarding the Cattell-Horn-Carroll (CHC) theory of cognitive abilities, I was pleasantly surprised when Dan reached out for my comments and suggested revisions to the paper.  

I was extremely impressed as Dan and his group had been involved in a deep dive in the CHC literature and had developed, without my involvement, an ingenuous internet-based CHC set of “test” items that can be submitted to different AI agents (GPT-4, GPT-5, Grok) to assess their CHC broad ability domain performance (to evaluate the extent to which AI agents demonstrate the “cognitive versatility and proficiency of a well-educated adult”).  I had zero involvement in the conceptualization or development of the AI modified/adapted CHC assessment framework and resulting CHC AI metrics. 

I want to express my appreciation to Dan for including me among the list of over 24 authors.  I’m very excited to monitor future developments by Dan and his group, as well as to see the impact of the CHC theory model on AI.

Links to secure copies of the paper (in various formats and social media platforms) are listed at the bottom of this post.  

Note.  Click on all images to enlarge for easy reading

Abstract

The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most em-pirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains—including reasoning, memory, and perception—and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly “jagged” cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 58%) concretely quantify both rapid progress and the substantial gap remaining before AGI.

Modified CHC model for evaluating AI agents

Click on image to enlarge.


As mentioned in the abstract, the paper reports on the CHC AGI capabilities of GPT-4 and GPT-5 in the following figure.  Click on images to enlarge.


I was pleased to see (on page 14 of the PDF paper) the following “intelligence as processor” figure which is based on work by myself and Joel Schneider. The model in Figure 3 (below) is based on Kevin S. McGrew and W. Joel Schneider. CHC theory revised: A visual-graphic summary of Schneider and McGrew's 2018 CHC update chapter. MindHub / IAPsych working paper, 2018.  http://www.iapsych.com/mindhubpub4.pdf 

The Schneider & McGrew (2018) heuristic CHC information processing model is below the Figure 3 figure.  Click on images to enlarge.




Dan Hendrycks and the Center AI Safety provide brief overviews describing this work on LinkedIn as well as Twitter/X (both that can be monitored for comments).

A PDF copy of the paper can be downloaded here.  A clickable web-based version of the paper can be accessed here.

Exciting stuff !!

Friday, June 06, 2025

Research Byte: General Ability (#g) Level Moderates Cognitive–#Achievement Relations for #Mathematics (#WJIV)—#WJIV #WJV #schoolpsychology #mathematics #SPED #EDPSYCH

[Blogmaster comment:   First…COI info…I’m a coauthor of the WJ IV and WJ V.  Second, regular readers may have noticed that I’ve been MIA on my various social media outlets the past 2-3 months.  I needed a break after spending the last five years working on the WJ V.  I also needed to attend to some family issues.  I plan to restart my sharing of interesting new research and FYI opinion posts].

Click on image to enlarge


New pub in Journal of Intelligence.  Click here to view and download (open access).

General Ability Level Moderates Cognitive–Achievement Relations for Mathematics 

by 
Christopher R. Niileksela
  
Jacob Robbins
 
Daniel B. Hajovsky
 
Abstract

Spearman’s Law of Diminishing Returns (SLODR) suggests general intelligence would be a stronger predictor of academic skills at lower general ability levels, and broad cognitive abilities would be stronger predictors of academic skills at higher general ability levels. Few studies have examined how cognitive–mathematics relations may vary for people with different levels of general cognitive ability. Multi-group structural equation modeling tested whether cognitive–mathematics relations differed by general ability levels for school-aged children (grades 1–5 and grades 6–12) using the Woodcock-Johnson Third Edition (n = 4470) and Fourth Edition (n = 3891) standardization samples. Results suggested that relationships between cognitive abilities and mathematics varied across general ability groups. General intelligence showed a stronger relative effect on mathematics for those with lower general ability compared to those with average or high general ability, and broad cognitive abilities showed a stronger relative effect on mathematics for those with average or high general ability compared to those with lower general ability. These findings provide a more nuanced understanding of cognitive–mathematics relations.

Saturday, February 15, 2025

Book Nook: #Presidential age and #intelligence. #Executivefunctions and the #executive office

Talk about timely…given all the talk about the age of our current (Trump) and prior (Biden) presidents.  


About this book (from publisher web page)

This book on presidential age is not about Alzheimer's Disease and associated pathologies of the aging brain. It is instead about the normally aging brain. Brains don’t simply develop and maintain their functionality into older adulthood unless otherwise impaired by neurocognitive disease. Were this the case, this book might be about leveraging prodromal biomarkers of neurodegenerative diseases to screen prospective presidential candidates. Instead, the normal decline age brings to all human brains begs a different type of book—and a broader and more blanketed warning about electing increasingly older presidents.


Table of contents below.  It is clear from the breadth of coverage that this is a serious attempt to corral critical age-related cognitive abilities research in the context of executive decision making (e.g., being President)…which makes it clear that the assessment of intelligence is well beyond the quick and very limited MoCA screener that our current president likes to brag (incorrectly) about as an indication of his great intelligence.  

The book is due out the first week of March, 2025.  Thus, I have not read any of the chapters upon which to base an opinion.  I shall be ordering a copy.

Click on images to enlarge for easy reading.






Thursday, December 12, 2024

Research byte: Prediction of human #intelligence (#g #Gf #Gc) from #brain (#network) #connectivity - #CHC

Choosing explanation over performance: Insights from machine learning-based prediction of human intelligence from brain connectivity 

PNAS Nexus, Volume 3, Issue 12, December 2024, pgae519,
Online and PDF download available at this link:  https://doi.org/10.1093/pnasnexus/pgae519

Abstract

A growing body of research predicts individual cognitive ability levels from brain characteristics including functional brain connectivity. The majority of this research achieves statistically significant prediction performance but provides limited insight into neurobiological processes underlying the predicted concepts. The insufficient identification of predictive brain characteristics may present an important factor critically contributing to this constraint. Here, we encourage to design predictive modeling studies with an emphasis on interpretability to enhance our conceptual understanding of human cognition. As an example, we investigated in a preregistered study which functional brain connections successfully predict general, crystallized, and fluid intelligence in a sample of 806 healthy adults (replication: N = 322). The choice of the predicted intelligence component as well as the task during which connectivity was measured proved crucial for better understanding intelligence at the neural level. Further, intelligence could be predicted not solely from one specific set of brain connections, but from various combinations of connections with system-wide locations. Such partially redundant, brain-wide functional connectivity characteristics complement intelligence-relevant connectivity of brain regions proposed by established intelligence theories. In sum, our study showcases how future prediction studies on human cognition can enhance explanatory value by prioritizing a systematic evaluation of predictive brain characteristics over maximizing prediction performance (emphasis added).