Research Alert: Specific (CHC) cognitive abilities are highly heritable independent of general intelligence (g)

This sound and important article includes a link to the original 2022 article in Intelligence (open access) and also a brief video summary of the study. 

 

Specific cognitive abilities are highly heritable independent of general intelligence 
https://www.psypost.org/specific-cognitive-abilities-are-highly-heritable-independent-of-general-intelli/

Research Alert: Looking into working memory through micro eye movements—“mind leaks” via the eyes

Quick email-based FYI research alert.

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Interesting article on how the study of small eye movements during activity working memory task performance can shed light on possible mental processes…..I like the concept of “mind leaks” via eye movements.  Good news….this is an open access article available at link below👍

 

Looking into working memory through micro eye movements: Trends in Cognitive Sciences 
https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(26)00128-2

Pardon typos and spelling errors-Message may be sent from iPhone and I've always had spelling problems :)

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Kevin S. McGrew, PhD
Educational & School Psychologist
Director
Institute for Applied Psychometrics (IAP)
https://www.themindhub.com
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Research Alert: Combining Psychology With Artificial Intelligence: What Could Possibly Go Wrong?

Quick email-based Research Alert FYI.  This is an open access article👍

 

Combining Psychology With Artificial Intelligence: What Could Possibly Go Wrong? - Iris van Rooij, Olivia Guest, 2026 
https://journals.sagepub.com/doi/10.1177/09637214261438379

 

Abstract

The current AI hype cycle combined with psychology’s various crises make for a perfect storm. Psychology, on the one hand, has a history of weak theoretical foundations, a neglect for computational and formal skills, and a hyperempiricist privileging of experimental tasks and testing for effects. Artificial intelligence, on the other hand, has a history of conflating artifacts for theories of cognition, or even minds themselves, and its engineering offspring likes to move fast and break things. Many of our contemporaries now want to combine the worst of these two worlds. What could possibly go wrong? Quite a lot. Does this mean that psychology and artificial intelligence can best part ways? Not at all. There are very fruitful ways in which the two disciplines can interact and theoretically contribute to cognitive science, for instance, by studying the scope and limits of computational models of human cognition. But to reap the fruits, one needs to understand how to steer clear of potential traps.

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AI Brief: The ascent of individual variance in education—the increasing importance of individual differences

This is another IQs Corner AI Brief.  

Prepared by Dr. Kevin McGrew with major assist from Google NotebookLM.  (Click here for brief explanation of how IQs Corner creates AI Briefs from article PDFs).  

For the first time I’m also experimenting with the Google NotebookLM feature of creating an AI generated infographic (Beta) from the research article—click on the image to enlarge for easy viewing.

The Ascent of Individual Variance in Global Education

 

The article "The growing role of individual differences: A cross-National Study of achievement variance reallocation from grade 4 to 8," published in the journal Intelligence (Eriksson et al., 2026; click here to acquire open access PDF copy), explores how the determinants of student achievement shift as children transition from late childhood (approximately age 10) to early adolescence (approximately age 14). The researchers, led by Kimmo Eriksson, sought to determine whether environmental factors, such as the quality of a national school system, become more influential over time through compounding advantages, or if individual learning characteristics grow in importance as academic material becomes more complex.

 

Theoretical Framework

 

The study tested three competing theoretical perspectives on achievement development between Grade 4 and Grade 8:

• Skills-Beget-Skills: Suggests early academic advantages create cascading benefits, predicting that high-quality national systems should lead to compounding advantages and an increase in the proportion of variance attributable to countries.
• Opportunity-to-Learn (OTL): Emphasizes exposure to content and predicts that variance at the school and class levels should increase as curricula become more specialized and students are sorted into different tracks.
• Individual Differences + Institutional Response: The authors’ integrated framework proposes that developmental processes create new individual-level variance, while educational systems respond by sorting students into different classes (tracking/streaming), thereby reallocating that variance to the class level.

Methodology

 

The researchers utilized data from the Trends in International Mathematics and Science Study (TIMSS) across three cohorts (2011–2015, 2015–2019, and 2019–2023). Their analysis involved dozens of countries and two primary methods:

1. Systematic Variance Decomposition: A four-level partition of achievement variance across countries, schools within countries, classes within schools, and individual students.
2. Cross-National Analysis: A formal model examining the relationship between individual characteristics (proxied by within-country relative standing) and educational system quality (proxied by country mean achievement).

Key Findings

 

The results across all cohorts and both subjects (mathematics and science) consistently supported the Individual Differences + Institutional Response hypothesis (H3) and directly contradicted the Skills-Beget-Skills hypothesis.

• Decrease in Country Influence: The proportion of achievement variance attributable to the country level decreased substantially (by 4–11 percentage points) as students moved from Grade 4 to Grade 8.
• Increase in Class-Level Importance: The proportion of variance at the class level increased substantially (by 3–7 percentage points). The class level was unique in benefiting from both the creation of new variance (through differentiated instruction) and the movement of variance (through ability-based sorting).
• Compensatory Advantage: The cross-national analysis revealed that the "slope" relating individual characteristics to system quality was shallower in Grade 8 than in Grade 4. This means that while students in weaker systems need higher individual characteristics to reach a certain achievement level (e.g., 500 points), this compensatory requirement is smaller in Grade 8, indicating that individual traits are increasingly pulling students ahead regardless of their national system's quality.

Conclusions and Implications

 

The authors conclude that stable individual characteristics affecting learning capacity—such as cognitive abilities, motivation, and self-regulation—become more influential as students mature. These traits are further magnified through interaction with educational environments, such as the "Matthew effect," where high-performing students elicit more challenging opportunities and resources. For educational practice, these findings suggest that pedagogical strategies may need to accommodate a wider range of learning profiles as students progress through school. Furthermore, the study cautions researchers that interventions targeting specific early skills may experience "fadeout" if they do not address the underlying learning capacities that become increasingly determinative during adolescence.

 

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.

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