Highly recommended AI paper regarding human & AI capabilities. HCQM: A dual-use human capability assessment and prescriptive target for engineering synthetic cognitive architectures (AI)

I recently exchanged emails and zoomed with Kameron Green who works in the AI space regarding his proposed Human Capability Quotient Map (HCQM).  I was thoroughly impressed with his proposed dual-use meta-taxonomy of human competence taxonomies (e.g., CHC; executive function; self-regulation; motivation; etc) into a grand overarching ability and capacity taxonomy.  As stated in the paper “HCQM is positioned to support broad human capability assessment while also providing a partial prescriptive target, at the capacity layer, for engineering synthetic cognitive architectures.”  

IMHO his grand model is one of the most comprehensive proposed high-level integrations of human individual difference constructs and emerging AI competencies I have read.  Although long, I urge those interested in human individual difference domains and AI evaluation frameworks to read his working paper. The paper is relevant to those studying individual differences in humans and those working on models by which to evaluate AI.  

The paper can be found (and downloaded) at the link provided by Kameron (click here).  He has also shared access to his paper at his LinkedIN profile (which is the URL associated with the hyper-link with his name in the first sentence of this post).

I’m sharing this link/paper with his permission.

 

Abstract

The field of intelligence research has long been characterized by fragmentation, with distinct traditions examining general cognitive abilities (Carroll, 1993; McGrew, 2009), emotional and social competencies (Salovey & Mayer, 1990; Goleman, 1995), creativity (Guilford, 1950), metacognition (Flavell, 1979), grit and adaptability (Duckworth et al., 2007), and more recent constructs such as digital intelligence (Park, 2019) and systems thinking (Senge, 1990; Meadows, 2008). While each line of inquiry has yielded valuable insights and measurement tools, the absence of an integrated taxonomy limits holistic assessment of human potential and the principled design of synthetic cognitive architectures. This paper proposes the Human Capability Quotient Map (HCQM) as a hierarchical synthesis that organizes eight top-level domains (General Cognitive Intelligence, Executive/Self-Regulatory Intelligence, Emotional & Social Intelligence, Creative & Innovation Intelligence, Motivational & Adaptive Intelligence, Learning & Knowledge Intelligence, Digital & Technological Intelligence, and Systems & Strategic Intelligence) into a coherent framework. Each domain includes subcomponents and observable indicators drawn from established psychometric, psychological, and cognitive-science literature.

HCQM makes three contributions: the integration itself, a coverage-asymmetry observation, and an explicitly dual-use framing; the two durable differentiators are the integration and the dual-use framing. HCQM is positioned to support broad human capability assessment while also providing a partial prescriptive target, at the capacity layer, for engineering synthetic cognitive architectures, with the explicit limitation that full architectural prescriptiveness requires a companion specification document (§6.6). These are applied to a coverage asymmetry: the human-capability tradition treats motivational, affective, cultural, and adversity capability as first-class (contemporary CHC, for example, now includes emotional intelligence as a broad ability), whereas contemporary AI architecture and evaluation frameworks largely do not. Unlike purely evaluative taxonomies such as DeepMind's 2026 Measuring Progress Toward AGI: A Cognitive Framework (Burnell et al., 2026), which identifies 10 cognitive faculties for benchmarking AGI progress, HCQM brings these dimensions to bear as a specification target. Unlike engineering-oriented architectural frameworks such as CoALA (Sumers et al., 2024), which specifies memory modules, action spaces, and decision loops for language agents, HCQM specifies the capacity surface those structural slots are expected to implement. The dimensions the AI frameworks omit are not a miscellaneous remainder: they concentrate in the motivational, adaptive, and metacognitive capacities (persistence under failure, strategy revision, self-monitoring, calibration) that govern whether a long-horizon autonomous agent is reliable, as distinct from whether it is capable. HCQM's distinctive AI-facing contribution is to organize these as a first-class reliability-and-autonomy layer and specify it at the capacity level (§5.2); a worked example maps a documented long-horizon agent failure pattern to the layer it omits. HCQM consolidates and ports existing constructs rather than claiming to discover them.

Drawing on CHC theory (Carroll, 1993; Schneider & McGrew, 2018), multiple-intelligences and triarchic models (Gardner, 1983; Sternberg, 1985), cultural intelligence (Earley & Ang, 2003), computational thinking (Wing, 2006), and modern cognitive architectures for language agents (Sumers et al., 2024), HCQM offers a synthesis rather than a novel discovery. We outline design principles, detail the hierarchical structure, discuss applications in human development and AI engineering, and acknowledge limitations, including the need for empirical validation, operationalized assessment instruments, and an explicit evaluation protocol. This paper aims to stimulate cross-disciplinary dialogue and guide future instrument development and architectural design.

Keywords: human capabilities, intelligence taxonomy, cognitive architecture, AGI evaluation, capability-grounded architectures, autonomous agents, agent reliability, long-horizon agents, metacognition, grit, cultural intelligence, systems thinking, CHC theory, CoALA.

Click on image to enlarge for easy viewing

AI Brief: The trilogy-of-the-mind individual difference construct (cognitive, conative, affective) “band is getting back together” as CAMML

I am currently working to expand my skill set by incorporating AI tools. Although adapting to new technologies can be challenging, leveraging these resources offers significant benefits for professional growth.

This AI Brief was produced by requesting Google NotebookLM—recommended by Dr. Adam Lockwood—to generate a narrative summary of my 2022 PDF article describing the Cognitive-Affective-Motivation-Model of Learning (CAMML). While I found the first results promising, I made more edits to enhance its accuracy and informativeness. My next goal is to use AI to summarize multiple articles, find similarities and differences, and potentially create comparative tables (again following guidance graciously provided by Dr. Lockwood).

These incremental steps mark my transition toward utilizing AI to support one of my primary professional interests: producing informative blog and social media posts aimed at professionals such as school psychologists and special education teachers working with students who often are marginalized in educational contexts. The goal is to help bridge the gap between theory, technology, research, and practical application.

 

Feedback is encouraged and may be directed to iqmcgrew@gmail.com or via the social media platform (LinkedIn, Twitter/X, BlueSky) comment feature where this blog post was discovered. I’m hoping to add AI Briefs as a regular feature of IQs Corner Blog and associated social media platforms.

 

 

AI Brief: The trilogy-of-the-mind individual difference construct (cognitive, conative, affective) “band is getting back together” as CAMML

 

Dr. Kevin McGrew with assist from Google NotebookLM

 

The Cognitive-Affective-Motivation Model of Learning (CAMML; McGrew, 2002)[1] is a proposed theoretical framework designed to integrate contemporary motivational, affective, and cognitive constructs into a unified model for the practice of school psychology. The central thesis of the framework is that school psychologists must move beyond a narrow focus on intelligence (general intelligence or psychometric g in particular) to embrace an updated "trilogy-of-the-mind" model, which views intellectual functioning as the inseparable interaction of cognition, conation (motivation/volition), and affect.

Theoretical Foundations and the Rebirth of Conation

 

The CAMML framework is heavily rooted in the seminal work of Richard Snow, specifically his research on aptitude trait complexes. McGrew argues that the field of school psychology has historically neglected Snow’s broader definition of aptitude—which includes personality and motivational differences alongside cognitive abilities—and instead, has favored a restricted view of aptitude as synonymous with IQ or psychometric g.

 

CAMML seeks to resurrect conation (the proactive part of motivation connecting cognition and affect to behavior) as a core pillar of intellectual functioning. By "standing on the shoulders of giants" like Snow, Spearman, and Wechsler, the model asserts that cognitive processes cannot be understood in isolation from the "nonintellectual" (conative) factors that drive and direct them. For example, David Wechsler defined intelligence as "the aggregate or global capacity of the individual to act purposefully, to think rationally, and to deal effectively with his environment." While this is the core of Wechsler’s definition, he also strongly emphasized this capacity is influenced by non-intellective (conative) variables such as drive, persistence, interest, emotional states, and personality traits.

 

Structural Components of the CAMML Framework

 

The model organizes individual differences characteristics into three functional categories, which could be called the 3-D model.

 

●      Affective “Dispositions”: These are distal-to-learning traits, primarily represented by the Big 5 personality traits (specifically Openness and Conscientiousness) and their associated social-emotional facets (e.g, curiosity, creativity, persistence, focus, determination). These personality traits act as dispositions that indirectly influence learning through more proximal mechanisms.

 

●      Motivational "Drivers": Motivation is conceptualized as the initiation of behavior, formed by achievement orientations (e.g., goals, interests) and self-beliefs (e.g., self-efficacy, self-concept). These constructs are typically domain-specific (e.g., math) and work in synergistic "complexes" to energize a student's readiness to act.[2]

 

●      Volitional "Directors": Volition, or self-regulated learning (SRL), represents the post-decisional phase of action. These are the mechanisms that direct, control, and regulate behavior toward goals once a commitment to learn has been made.

 

A more detailed list of the 3-D CAMML domain constructs and definitions is available here. See figure below for a visual representation of the major affective and conative MACM constructs (click on image to enlarge for easy viewing and reading).

 

The "Crossing the Rubicon" Investment Model

 

The functional heart of CAMML is the "Crossing the Rubicon" model, which illustrates the pathway from initial desire to engaged motivated learning. In this model:

 

1. Pre-decisional Phase: Achievement orientations and self-beliefs drive or prepare the learner to start a wish—>want—> intention sequence, that eventually eventuates in motivated action.

2. Commitment: When the learner "crosses the Rubicon," they are making a firm commitment to motivated action through cognitive engagement.

3. Action Phase: Volitional (SRL) strategies steer the cyclical process via action results feedback while the learner invests cognitive abilities (such as those defined by CHC theory) to acquire knowledge.

4. Outcomes: This personal investment of fluid cognitive processes (Cattell's general gf that subsumes broad Gf, Gv, Ga, Gwm, Gl, Gr, and Gs abilities) during learning results in the development of crystallized knowledge systems (Cattell’s general gc that subsumes broad Gc, Grw, Gq, and Gkn abilities).

 

See figure below for visual representation of “the CAMML crossing the Rubicon model of motivated learning” (click on image to enlarge for easy viewing and reading).

 

Implications for School Psychology Practice

 

CAMML advocates for a paradigm shift in assessment and intervention. It suggests that school psychologists should transition from routine, comprehensive cognitive testing toward more time-efficient selective, referral-focused cognitive assessments combined with the assessment of key conative (non-cognitive) characteristics that contribute to learning aptitude complexes. This approach prioritizes identifying manipulable instructional levers, such as a student's motivational orientation (e.g., intrinsic motivation, interests, goal orientation), self-beliefs (e.g., locus of control, self-efficacy, growth or competence mindset), rather than relying exclusively on cognitive ability scores (especially full-scale IQ or g) that have proven hard to modify.

 

The framework provides a "whole-child" perspective to better address the nuances of individual differences, particularly as students move from the traditional “industrial” model of education (i.e., regularly scheduled, structured, in-class teacher-directed learning) to more of an “information-age” paradigm of education—a paradigm that requires a fuller expression of independent motivated self-regulated learning (SRL).

PS - other CAMML related posts on this blog can be found by clicking here.

[1] All relevant references can be found in McGrew (2022).

[2] The motivation constructs included in the CAMML framework are drawn from earlier efforts to develop the McGrew Model of Achievement Competence Motivation (MACM). A detailed explanation of the evolution and development of the MACM model is available elsewhere (McGrew et al., 2004). A series of recent MACM PowerPoint® modules is available here.

Beyond IQ Series #9. Definition and learning implications of intrinsic motivation

Background comment regarding this series

Interest in social-emotional learning and resiliency training (click here and here for just two examples) in education has shown a recent uptick on activity. Given this activity, IQs Corner is starting a series to explain the previously articulated Model of Academic Competence and Motivation (MACM), which was a model ahead of it's time (IMHO). The imporance of non-cognitive (conative) characteristics in learning have been articulated since the days of Spearman, the father of the construct of general intelligence. Richard Snow's work on the concept of "aptitude," which integrates cognitive and conative individual difference variables, is the foundation of the Beyond IQ MACM. Non-cognitive (cognitive) characteristics of learners are important for learning and are more manipulable (more likely to be modified via intervention) than intelligence. Thus, the MACM components make sense as potential levers for improving school learning and pursuing more well rounded life-long learners. This material comes a larger set of materials on the web (click here).

Current MACM Series Installment

This ninth installment in the Beyond IQ series addresses intrinsic motivation [All installments in this series (and other related posts and research) can be found by clicking here].

___________________________________________________________________

Intrinsic Motivation: Definition and Conceptual Background

When a person engages in an activity because they are interested in and enjoy the activity (e.g., they perform the activity for the sake of doing it—for the enjoyment, fun orpleasure) and not because the activity will produce a reward or result in the avoidance of a negative consequence.

Intrinsic motivation describes an individual who engages in an activity because they are interested in and enjoy the activity (e.g., they perform the activity for the sake of doing it—for the enjoyment, fun, or pleasure) and not because the activity will produce a reward or result in the avoidance of a negative consequence (Eccles & Wigfield, 2002; Grolnick, Gurland, Jacob, & Decourcey, 2002; Linnenbrink & Pintrich, 2002a; Snow et al., 1996; Standage, Duda, & Ntoumanis, 2003). The converse (extrinsic motivation) is present when an individual engages in an activity for other (e.g., reward) or instrumental reasons (e.g., means to an end). Intrinsic motivation “energizes important growth- fostering behaviors, such as seeking out challenges, exercising skills, and pursuing one’s interests (Deci & Ryan, 1985)” (Reeve, Nix, & Hamm, 2003, p. 375). As such, intrinsic motivation is frequently mentioned as a causal contributor to self-determination. High intrinsic motivation orientation is often considered as an indicator of the highest levels of self- determination (d'Ailly, 2003; Reeve et al., 2003; Standage et al., 2003).

Early motivation research suggested that intrinsic motivation was not trait-like in nature, but rather, was situation-specific and alterable (Harter, 1981). This “state” interpretation of intrinsic motivation suggests that a student’s intrinsic motivation is amenable to environmental manipulation. Researchers are now treating intrinsic motivation as less of a situation-specific state and more of a trait-like characteristic (Eccles & Wigfield, 2002). The highly correlated components of the trait-intrinsic motivation (as would be described in an academic context) are: (a) academic learning driven by curiosity and interest; (b) a preference for hard or challenging academic tasks; and (c) a striving for competence and independent mastery (Eccles & Wigfield, 2002; Harter, 1981; Newman, 2000). Of the 3 characteristics, the first (curiosity- driven learning) is the core concept of intrinsic motivation.

Intrinsic Motivation: Learning Implications

The intrinsic motivation literature (Covington & Dray, 2002; Eccles & Wigfield, 2002; Gresham &Elliott, 1984; Grolnick et al., 2002; Newman, 2000; Wehmeyer, 1996; Wehmeyer, 1999) suggests the following general implications:

Intrinsic motivation has been argued to be of particular salience for students with disabilities given that, historically, the special education delivery service model has been grounded primarily in efforts to identify and remediate student deficits.

High levels of trait-like intrinsic motivation have been associated with a variety of positive student behaviors and educational outcomes. It is hypothesized that high intrinsic motivation helps students care about their learning, which subsequently increases their striving towards high achievement. High intrinsic motivation has also been associated with positive emotional experiences, higher levels of cognitive engagement, lower levels of anxiety, higher perceptions of competence, and a higher use of a variety of positive self- regulatory behaviors (e.g., adaptive help-seeking, learning strategies, meta-cognitive strategies). Clearly, students low in intrinsic motivation are at risk for educational failure.

According to stage-environment fit theory, when students enter a developmental stage characterized by a greater need for autonomy (e.g., pre-adolescence), the typical educational environment actually reduces opportunities for self-initiated behavior and independent thinking vis-à-vis a greater emphasis on external (e.g., teacher) control. The result can be a reduction in subject matter interest and intrinsic motivation. A logical extension is that education/learning environments should strive to provide the best possible “fit” between a student's learning environment and their developmental and level of intrinsic motivation.

According to stage-environment fit theory and research, the use of normative grading (an emphasis on tangible rewards that are limited in quantity), during a time when a student is entering a stage characterized by a need for autonomy, may produce increased social comparison and feelings of competitiveness. The net result is a hypothesized reduction in intrinsic motivation.

Absolute evaluation or grading standards, which increase the explicit link between degree of expended effort and achievement rewards, tend to increase a student’s sense of intrinsic motivation. Merit-based grading systems are postulated to be more desirable when a student transitions into a stage characterized by the need for more autonomy and independence.

Additional learning environment characteristics associated with higher levels of intrinsic motivation include (Grolnick et al., 2002):

Learning tasks should be optimally challenging—“just above the current level of ability” (Grolnick, 2002, p. 155).
Learning should minimize excessive use of “rewards” which tend to shift the focus from an internal to external cause of behavior.

Learning environments that provide for autonomy, involvement, and support in a non-controlling manner (in contrast to an environment with strong external controls) are associated with students who display greater intrinsic motivation, which in turn influences the development of more self-regulated learning via internalization.

Learning environments should minimize external (adult) pressure to behave in particular ways and to solve problems for others (e.g., for teacher, mom, or dad). A focus on helping students to solve problems and tasks for themselves (with support) is more desirable. Students should not be motivated to perform out of sense of obligation or coercion (Wehmeyer, 1992).

- iPost using BlogPress from Kevin McGrew's iPad

intelligence IQ tests IQ testing IQ scores CHC intelligence theory CHC theory Cattell-Horn-Carroll human cognitive abilities psychology school psychology individual differences cognitive psychology neuropsychology neuroscience psychology special education educational psychology psychometrics psychological assessment psychological measurement IQs Corner general intelligence Beyond IQ aptitude school learning social emotional learning resiliency training CASEL motivation

Generated by: Tag Generator

Beyond IQ Project: Ed Psych article abstracts

A recent issue of the Journal of Educational Psychology (2008, Vol. 100, 8) had a number of articles dealing with constructs included in the Beyond IQ projects Model of Academic Competence and Motivation (MACMM). Below are the iAbstracts (images captured and emailed from myiPhone). If any reader would like to read one of the articles (I would provide a copy of the pdf file), in exchange for a guest blog post summary to this bog, please contact the blogmaster (iapsych@charter.net)

MACMM Self-concept and Self-beliefs

MACMM Self-efficacy and Self-Regulated Learning

MACMM Achievement goal-orientation (next three)

Technorati Tags: psychology, educational psychology, school psychology, neuropsychology, Journal of Educational Psychology, motivation, self-beliefs, self-efficacy, self-confidence, goal orientation, MACMM, Beyond IQ, IQs Corner, academic competence

Beyond IQ Byte # 5: Intrinsic motivation

Here is Byte # 5 from the Beyond IQ project, a project that outlines a proposed Model of Academic Competence and Motivation (MACM). Today's construct spotlight is on "achievement goal orientation."

Intrinsic Motivation

When a person engages in an activity because they are interested in and enjoy the activity (e.g., they perform the activity for the sake of doing it—for the enjoyment, fun or pleasure) and not because the activity will produce a reward or result in the avoidance of a negative consequence.

Intrinsic motivation describes an individual who engages in an activity because they are interested in and enjoy the activity (e.g., they perform the activity for the sake of doing it—for the enjoyment, fun, or pleasure) and not because the activity will produce a reward or result in the avoidance of a negative consequence (Eccles & Wigfield, 2002; Grolnick, Gurland, Jacob, & Decourcey, 2002; Linnenbrink & Pintrich, 2002a; Snow et al., 1996; Standage, Duda, & Ntoumanis, 2003). The converse (extrinsic motivation) is present when an individual engages in an activity for other (e.g., reward) or instrumental reasons (e.g., means to an end). Intrinsic motivation “energizes important growth- fostering behaviors, such as seeking out challenges, exercising skills, and pursuing one’s interests (Deci & Ryan, 1985)” (Reeve, Nix, & Hamm, 2003, p. 375). As such, intrinsic motivation is frequently mentioned as a causal contributor to self-determination. High intrinsic motivation orientation is often considered as an indicator of the highest levels of self- determination (d'Ailly, 2003; Reeve et al., 2003; Standage et al., 2003).

Early motivation research suggested that intrinsic motivation was not trait-like in nature, but rather, was situation-specific and alterable (Harter, 1981). This “state” interpretation of intrinsic motivation suggests that a student’s intrinsic motivation is amenable to environmental manipulation. Researchers are now treating intrinsic motivation as less of a situation-specific state and more of a trait-like characteristic (Eccles & Wigfield, 2002). The highly correlated components of the trait-intrinsic motivation (as would be described in an academic context) are: (a) academic learning driven by curiosity and interest; (b) a preference for hard or challenging academic tasks; and (c) a striving for competence and independent mastery (Eccles & Wigfield, 2002; Harter, 1981; Newman, 2000). Of the 3 characteristics, the first (curiosity- driven learning) is the core concept of intrinsic motivation.


Educational Implications: The intrinsic motivation literature (Covington & Dray, 2002; Eccles & Wigfield, 2002; Gresham &Elliott, 1984; Grolnick et al., 2002; Newman, 2000; Wehmeyer, 1996; Wehmeyer, 1999) suggests the following general implications:

Intrinsic motivation has been argued to be of particular salience for students with disabilities given that, historically, the special education delivery service model has been grounded primarily in efforts to identify and remediate student deficits.

High levels of trait-like intrinsic motivation have been associated with a variety of positive student behaviors and educational outcomes. It is hypothesized that high intrinsic motivation helps students care about their learning, which subsequently increases their striving towards high achievement. High intrinsic motivation has also been associated with positive emotional experiences, higher levels of cognitive engagement, lower levels of anxiety, higher perceptions of competence, and a higher use of a variety of positive self- regulatory behaviors (e.g., adaptive help-seeking, learning strategies, meta-cognitive strategies). Clearly, students low in intrinsic motivation are at risk for educational failure.

According to stage-environment fit theory, when students enter a developmental stage characterized by a greater need for autonomy (e.g., pre-adolescence), the typical educational environment actually reduces opportunities for self-initiated behavior and independent thinking vis-à-vis a greater emphasis on external (e.g., teacher) control. The result can be a reduction in subject matter interest and intrinsic motivation. A logical extension is that education/learning environments should strive to provide the best possible “fit” between a student's learning environment and their developmental and level of intrinsic motivation.

According to stage-environment fit theory and research, the use of normative grading (an emphasis on tangible rewards that are limited in quantity), during a time when a student is entering a stage characterized by a need for autonomy, may produce increased social comparison and feelings of competitiveness. The net result is a hypothesized reduction in intrinsic motivation.

Absolute evaluation or grading standards, which increase the explicit link between degree of expended effort and achievement rewards, tend to increase a student’s sense of intrinsic motivation. Merit-based grading systems are postulated to be more desirable when a student transitions into a stage characterized by the need for more autonomy and independence.

Additional learning environment characteristics associated with higher levels of intrinsic motivation include (Grolnick et al., 2002):

• Learning tasks should be optimally challenging—“just above the current level of ability” (Grolnick, 2002, p. 155).

• Learning should minimize excessive use of “rewards” which tend to shift the focus from an internal to external cause of behavior.

• Learning environments that provide for autonomy, involvement, and support in a non-controlling manner (in contrast to an environment with strong external controls) are associated with students who display greater intrinsic motivation, which in turn influences the development of more self-regulated learning via internalization.

Learning environments should minimize external (adult) pressure to behave in particular ways and to solve problems for others (e.g., for teacher, mom, or dad). A focus on helping students to solve problems and tasks for themselves (with support) is more desirable. Students should not be motivated to perform out of sense of obligation or coercion (Wehmeyer, 1992).

Technorati Tags: psychology, educational psychology, school psychology, neuropsychology, motivation, MACM, Beyond IQ, intrinsic motivation, conative, aptitude