intelligence-radar/compendium/01-maps-of-intelligence.md

Imported exploration, 2026-09-13. Source claims have not been independently verified by this repository. See integration review.

Maps of intelligence

Version 0.1 · 13 September 2026

Humans have invented several mutually incompatible but productive ways to talk about intelligence. Intelligence Radar treats them as maps, not as a single territory.

1. Psychometric / difference map

Claim: Cognitive performances covary. A general factor g plus group factors (verbal, spatial, processing speed) describe individual differences.

What it is good for: Predicting educational and occupational outcomes; grounding “general” as a statistical regularity rather than a slogan.

Limits: g is a covariance structure, not a mechanism. It does not say how a brain or a model produces competence. Transfer to machines is metaphorical unless the same battery is used.

2026 note: Network Neuroscience Theory treats g as a property of global connectome topology (distributed processing, weak long-range links, modal control hubs, small-world communication) rather than a single region. That is a live empirical program, not settled doctrine.

2. Cognitive-architecture map

Claim: Intelligence is a set of processes — perception, working memory, attention, planning, learning, language, motor control — arranged in an architecture (ACT-R, SOAR, dual-process, global workspace, predictive processing).

What it is good for: Task analysis and decomposition. This is the map the Task Router inherits when it splits a job into subtasks.

Limits: Architectures multiply. “System 1 / System 2” is a useful cartoon that current reasoning models have made commercially real without proving the cartoon.

2026 note: Complementary-intelligence programs distinguish cognitive AI (built to model human processes) from machine AI (optimized for scale). Integration routes being discussed: shared embeddings, instruction encoding, training agents on cognitive-AI data, and coevolving agents.

3. Computational / Bayesian map

Claim: Intelligence is approximate rational inference under resource limits: priors, likelihoods, programs, search.

Champions: Tenenbaum and the probabilistic-programs tradition; the “language is not all you need” line that wants LLMs as language modules attached to a world-model / program-induction core.

What it is good for: Explaining few-shot concept learning, causal reasoning, and why pure next-token prediction still fails some structured tasks.

Limits: Full Bayesian program learning does not yet scale to frontier product systems. The engineering winning streak is still large transformers plus RL and tools.

4. Connectionist / scaling map

Claim: Competence emerges from gradient-trained networks at sufficient scale of data, parameters, and compute, plus the right post-training (instruction, preference, RL, tools).

What it is good for: Predicting that many new skills appear as a function of scale and that evaluation sets saturate. This is the map the AI industry actually ships against.

Limits: Poor sample and energy efficiency versus brains; weak accounts of agency, grounded world models, and long-horizon reliability; benchmarks leak.

2026 note: Alternative architectures (latent-space recurrent “brain-inspired” models such as Pathway BDH) are trying to move reasoning off the token tape. Watch these as potential cost-curve breakers, not as proven replacements.

5. Neuroscientific / mechanistic map

Claim: Intelligence is what certain recurrent, sparse, multi-scale biological circuits do — especially prefrontal control interacting with specialized systems — under severe energy and developmental constraints.

2026 note: Proposals for a “foundational brain model of intelligence” want connectome + cell types + recurrent dynamics + multi-task training, with PFC as the control core. Separate work (Ganguli and others) calls for a science of intelligence that unifies physics of complex systems, neuroscience, and AI.

Limits: We still lack a causal, multi-scale model that predicts both a mouse navigating and GPT-class language.

6. Consciousness / sentience map (disputed)

Claim variants: Integrated information; global workspace broadcast; higher-order thought; life-first / enactive views that deny substrate-neutral computation.

Radar rule: Track the dispute and any policy consequence (welfare of models, “critical” capability designations). Do not treat a lab’s anthropomorphic demo as evidence of sentience.

7. Collective / economic map

Claim: A lot of “what humanity knows” lives in institutions, prices, papers, and code, not in individual skulls. AI changes the cost of contributing to that collective.

What it is good for: The weekly digest’s economic, legal, and political sections. Capability without deployment, capital, chips, or permission is not a social fact.

Working synthesis used by this facility

For routing work to models, Intelligence Radar uses a capability stack, not a theory of mind:

1. Perception and representation
2. Knowledge and retrieval
3. Reasoning and search
4. Tool and environment action
5. Planning over long horizons
6. Coordination (multi-agent / human-in-the-loop)
7. Reliability under distribution shift and adversarial pressure

A task is scored by which layers it actually needs. Benchmarks are chosen to hit those layers. Theories from sections 1–7 explain why a layer is hard; they do not replace the score.

A 2026-10-04 research lead, Richard Socher and The Eureka Machine, describes scientific discovery as a loop of records, physical models, simulation, and laboratory measurement. It is an engineering recipe for collective intelligence. It is not a revision of the seven layers above. Two secondary accounts of Socher's own dimension list disagree with each other. Neither account replaces this stack.

Changelog

• 2026-09-13: v0.1 initial map.
• 2026-10-04: pointer to the unverified Socher lead. The seven-layer stack is unchanged.