An integrated hierarchical taxonomy of human capabilities — designed for both human development assessment and synthetic cognitive architecture.
HCQM (Human Capability Quotient Map) is a research framework that integrates existing capability research from cognitive science, psychology, and intelligence studies into a single hierarchical taxonomy spanning eight domains and 33 constituent capabilities: General Cognitive, Executive/Self-Regulatory, Emotional & Social, Creative & Innovation, Motivational & Adaptive, Learning & Knowledge, Digital & Technological, and Systems & Strategic.
The framework is intended for two complementary purposes:
- Human development — systematic, multi-dimensional capability assessment to support targeted growth planning across cognitive, emotional, motivational, and systems-thinking dimensions.
- Synthetic cognitive architecture — a partial capacity-layer specification for AI systems, identifying the capability surface a generally intelligent agent must cover, with particular attention to the motivational, metacognitive, and adaptive capacities that govern long-horizon agent reliability.
HCQM does not claim to discover new capabilities. Its contribution is the integration of existing, peer-reviewed capability traditions into a single hierarchical structure, and the application of that structure as a dual-use specification target.
Existing capability frameworks are fragmented. Classical cognitive taxonomies (CHC theory, ACT-R, Soar, the Common Model of Cognition) cover cognitive ability and executive function. Specialized frameworks (EQ, CQ, AQ, DQ, Grit, systems thinking) exist independently. Contemporary AI-evaluation frameworks (the Hernández-Orallo and Vold (2019) catalogue, CoALA, DeepMind 2026, Hendrycks 2025, OECD 2025) focus primarily on cognitive ability and largely omit motivational, emotional, cultural, and adversity-related dimensions.
HCQM addresses this gap by proposing a single hierarchical taxonomy that integrates these traditions and identifies the coverage asymmetry between the human-capability tradition and contemporary AI frameworks — particularly the motivational, adaptive, and metacognitive capacities that govern autonomous agent reliability over long horizons.
Current public version: v1.0 (publication release, 2026-06-12)
v1.0 is the clean publication release. All external reviewer feedback is integrated. Acknowledgments finalized: Dr. Kevin McGrew (Institute for Applied Psychometrics) + two anonymous reviewers. v0.8 is retained for reference.
v1.0 DOI (current): 
See whitepaper/HCQM_v1.0.md for the full paper.
whitepaper/
HCQM_v1.0.md current version (publication release)
HCQM_v0.8.md prior release (reviewer-revised draft, retained for reference)
HCQM_v0.6.md prior public release (retained for archive)
HCQM.png Figure 1 — HCQM schematic (8 domains, 33 capabilities)
HCQM-comparison.png Figure 2 — five-framework coverage comparison
HCQM-v0.1.md original v0.1 framework tree (priority anchor)
docs/
thesis.md research thesis and positioning document
related-work.md structured literature review by domain
positioning.md explicit comparison vs. adjacent frameworks
ROADMAP.md versioning plan and milestone tracking
CONTRIBUTING.md contribution guidelines
CHANGELOG.md version history
CITATION.cff citation metadata
LICENSE CC BY 4.0
- v1.0 — publication release (current, 2026-06-12): Clean publication copy. All external reviewer feedback integrated. Acknowledgments finalized. Internal scaffolding removed.
- v0.8 — reviewer-revised draft: Five-framework comparative analyses. 33 constructs. CHC terminology updated to Schneider and McGrew (2018). Retained for reference.
- v1.1 — subcomponent refinements (Q3 2026): Episodic/procedural memory constructs, cognitive resource allocation, hallucination/retrieval precision, capability-to-instrument mapping table.
- Architecture whitepaper (planned, Q3–Q4 2026): HCQM as a prescriptive capacity-layer specification for synthetic cognitive architectures, with module decomposition and integration model.
- v2.0 — empirical validation (2027+): Full instrument battery, psychometric validation, predictive validity studies, cross-cultural validation.
See ROADMAP.md for full detail.
Kameron M. Green ORCID: 0009-0002-8350-3641 Independent researcher working at the intersection of human capability taxonomy and AI cognitive architecture. MS in Computer Science (AI concentration), University of Nebraska at Omaha.
If you reference HCQM, please cite the most recent archived version. Metadata is in CITATION.cff.
Citing v1.0 (current release):
Green, K. M. (2026). HCQM: A Hierarchical Capability Map for Assessing and Developing Human and Synthetic Intelligence (v1.0). Zenodo. https://doi.org/10.5281/zenodo.20668273
Citing v0.8 (prior release):
Green, K. M. (2026). HCQM: A Hierarchical Capability Map for Assessing and Developing Human and Synthetic Intelligence (v0.8). Zenodo. https://doi.org/10.5281/zenodo.20632044
Citing v0.1 (priority anchor):
Green, K. M. (2026). HCQM: Human Capability Quotient Map (v0.1.1). Zenodo. https://doi.org/10.5281/zenodo.19587600
BibTeX (v0.1 priority anchor):
@software{green_hcqm_2026,
author = {Green, Kameron M.},
title = {HCQM: Human Capability Quotient Map},
version = {0.1.1},
year = {2026},
publisher = {Zenodo},
doi = {10.5281/zenodo.19587600},
url = {https://doi.org/10.5281/zenodo.19587600}
}
Update the version and DOI to the most recent Zenodo archive when citing.
This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material with appropriate attribution.
The author thanks Dr. Kevin McGrew (Institute for Applied Psychometrics) for substantive feedback on the treatment of Cattell–Horn–Carroll theory, and two anonymous reviewers for feedback on the cognitive-architecture and AI-evaluation sections.
This is an active research project. Issues, discussions, and critique are welcome via GitHub Issues. The author is particularly interested in:
- Critique of the HCQM structure from researchers in cognitive science, psychology, or AI
- Pointers to capability traditions not yet represented in the framework
- Discussion of the reliability-and-autonomy layer (§5.2) and its mapping to agent evaluation