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
