Talent: Predispositions Framework

A framework of 72 brain-based predispositions in 10 groups links intelligence, creativity, style, and memory to innovation and wise decision making.

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Sep 07, 2025

Innovation, as this framework models it, is driven less by learned “skills” than by 72 stable brain-tendency predispositions organized into 10 capability groups that span the whole pipeline from problem finding to wise, values-aligned decision making. The architecture blends factor-analytic intelligence research, executive-control and memory systems, creativity science, cognitive-style theory, and neurodiversity findings so we can talk concretely about what the brain tends to do by default and where that tendency creates market value. The CHC backbone (broad abilities like Gf, Gv, Gq, Gs, Gc), creative propulsion and investment decision lenses, and style-based representation preferences give us a common language for vocational fit and team design.

We start where innovation actually begins: problem discovery and opportunity sensing. Here, J. P. Guilford’s The Nature of Human Intelligence supplies “sensitivity to problems,” the anomaly-radar that turns “something’s off” into pointed questions, while Robert J. Sternberg’s Wisdom, Intelligence, and Creativity Synthesized adds metacomponents that recognize/define problems and choose strategies, plus propulsion types (redefinition, forward/advance incrementation, integration, redirection, reinitiation) that describe how people try to move fields. Riding & Rayner’s Cognitive Styles and Learning Strategies contributes the Wholist–Analytic grain-size bias that explains why some people sweep the whole landscape before zooming in, and Simon Baron-Cohen’s The Pattern Seekers contributes a systemizing drive—the “if-and-then” rule hunger that fuels mechanism-first problem framing. Together, these dispositions explain who consistently picks consequential problems early and frames them so they’re actually solvable.

Once the right problem is named, knowledge acquisition and self-directed learning takes over. Sternberg again gives us knowledge-acquisition components (selective encoding, comparison, combination) for compressing novelty into schemas. The hippocampal mechanisms in Jerry W. Rudy’s The Neurobiology of Learning and Memory distinguish pattern separation (store look-alike episodes without interference) from pattern completion (reconstruct the whole from a fragment), explaining clean indexing and rapid retrieval under partial data. Brown, Roediger & McDaniel’s Make It Stick adds the behavioral “toolchain” (retrieval, spacing, interleaving, metacognitive monitoring) that some learners intuitively deploy. Brock & Fernette Eide’s Dyslexic Advantage (New Edition) contributes a narrative encoding bias—scene-based, episodic reasoning that excels at coherence and future simulation—while Riding & Rayner explain the self-translation habit (text↔diagram) that reduces cognitive load by recoding inputs into one’s dominant representational style. This cluster predicts speed-to-understanding and reliable transfer.

Generating options is its own engine: generative creativity & concept propulsion. Here, Guilford separates ideational fluency (how many), flexibility (how many kinds), and originality (how rare), so we can see why some people flood the option space, others jump categories, and others still produce statistically infrequent associations. Sternberg contributes selective combination (compose the right parts) and selective comparison (map deep analogies), plus the propulsion spectrum (e.g., redirection, reinitiation) that distinguishes incremental edge-pushers from clean-slate starters. His investment theory (from Creativity: From Potential to Realization, ed. Sternberg, Grigorenko & Singer) reframes creativity as a decision to buy low and sell high in idea markets, linking cognition to risk, persuasion, and timing. This cluster identifies who repeatedly originates concepts that are both novel and adoptable.

To turn ideas into explanations and predictions, analytical modeling & systems reasoning depends on the CHC spine in John B. Carroll’s Human Cognitive Abilities: Gf (induction/relational integration), Gv (visualization/mental rotation), Gq (quantitative reasoning), Gwm/Gsm (working memory), and Gs (processing speed). Richard J. Haier’s The Neuroscience of Intelligence adds the P-FIT account—efficient fronto-parietal integration—that explains rapid abstraction ↔ hypothesis-testing loops and cross-domain transfer. Baron-Cohen’s systemizing drive supplies the motivation to extract lawful rules, while Riding explains why people who prefer cognitive complexity can keep multivariate, nonlinear structures active without collapse, and a parsimony bias keeps models lean enough to generalize. This is the “mechanism builder” zone: APIs, equations, simulations, and causal diagrams that behave at scale.

Innovation compounds only when experimentation, evidence & causal inference are strong. Sternberg’s metacomponents operationalize doubt—hypothesis → test → monitor → evaluate—while his emphasis on evaluating one’s products nurtures falsification seeking (trying to break one’s own ideas). Rudy explains error-driven learning (prediction mismatch yields new learning, not erasure), which we convert into fast, context-aware feedback loops. The causal-graph instinct merges systemizing with Gf to prefer mechanisms over correlations, and propulsion clarifies dispositions like replication (solidify the baseline) and advance incrementation (push farther than consensus tolerates). This cluster separates teams that believe from teams that know—and know when to scale or kill a bet.

All of that runs on perception, imagery & representation, where Riding & Rayner give us the Verbaliser–Imager and Wholist–Analytic axes that shape how minds code and lay out information. Stanislas Dehaene’s The Number Sense contributes the approximate number system (ANS) for fast, ratio-sensitive magnitude judgments and subitizing for exact small-set tracking—distinct mechanisms that anchor sanity checks, dashboards, and rapid scene parsing. Darold A. Treffert’s Islands of Genius documents detail hyperacuity (a local-feature bias often seen in autistic and acquired savant profiles), while CHC Gv covers deep spatial visualization and imagery vividness/control explains why some people can prototype internally at high fidelity. Howard Gardner’s Frames of Mind supports the spatial and linguistic ends that often power diagram-first and language-first modeling cultures. This group explains why “the same information” lands so differently for different brains—and how to make it land.

Next come attention, control & cognitive energy, where CHC Gs (speed) and Gwm/executive attention (maintenance, disengagement, interference control) set throughput and stability, and set-shifting agility determines how costly it is to change rules mid-flight. A cognitive tempo (impulsivity–reflectivity) calibrates speed-accuracy trade-offs under uncertainty, and exploration–exploitation balance reflects neuromodulator-tuned novelty seeking vs. lock-in. Riding’s style research reappears as load management via translation (diagram↔text; whole↔parts) to keep complex work within cognitive bandwidth. This cluster explains who can stay locked onto a goal for hours, who can pivot instantly without losing the plot, and who keeps working memory “clean” when contexts switch.

Great ideas still fail without execution, scaling & operationalizing. Sternberg’s metacomponents become the plan-monitor-evaluate control loop for roadmaps, decision gates, and course corrections. Proceduralization—from cortico-striatal habit systems summarized in learning/memory texts—chunks repeated steps into fast, low-variance SOPs, freeing working memory for exceptions. Pair Gs with systemizing and you get a throughput-optimization bias (Little’s Law instincts; love of rule-clean pipelines). Delayed gratification explains who sustains multi-quarter technical arcs; analytic style yields an error-taxonomy instinct (first diagnose, then fix), and rising Gc (Carroll) turns tacit wins into shared playbooks that lift the median. This is how reliability, speed, and scale emerge without burning quality.

Finally, decision architecture, values & risk makes sure we are not just efficient but right. Sternberg’s WICS in Wisdom, Intelligence, and Creativity Synthesized defines wise reasoning as applying intelligence and creativity, mediated by values, to pursue a common good, balancing intra/inter/extrapersonal interests over short/long terms while choosing to adapt to, shape, or select environments. His propulsion spectrum and investment theory explain risk calibration for creative leadership (how contrarian to be, how long to hold, when to rotate) and the acceptance–rejection set-point (replicate/extend vs. redirect/restart vs. integrate). Gardner strengthens the interpersonal/intrapersonal lens for social legitimacy, and Eide adds a dynamic, interconnected reasoning strain that helps simulate long-horizon consequences. This capstone cluster ensures our bets are ethically and strategically sound—and adopted.

Taken together, the sources integrate seamlessly: Guilford for problem sensitivity and divergent production; Sternberg for triarchic control, wisdom, propulsion, and investment; Carroll for CHC structure; Haier for P-FIT network efficiency; Riding & Rayner for representational style and translation; Rudy and Make It Stick for memory mechanisms and high-yield learning behaviors; Dehaene for numerical cognition; Baron-Cohen for systemizing; Treffert for islands of genius; Gardner for spatial/linguistic and social intelligences; and Sternberg (ed.), The Nature of Human Intelligence (2018) as a modern umbrella. The result is a vocationally actionable map of 72 distinct predispositions across ten groups that lets you staff, scope, and steer work so people spend more of their time where their brains do exceptional work by default—and partner for the rest.

Summary

1) Problem discovery & opportunity sensing

Sensitivity to Problems— anomaly- and consequence-radar that flags gaps, risks, or latent needs from minimal cues; turns “something’s off” into pointed questions and better briefs.
Metacomponents—Problem Definition — executive control that recognizes a real problem, specifies it, selects a solution strategy, and plans monitoring/evaluation.
Field Independence / Disembedding — pulls signal out of clutter; isolates structure from distracting context across code, data, diagrams, and dense prose.
Wholist Orientation — big-picture, gist-first sweep that maps adjacencies and stakeholders before drilling down; powerful for horizon scanning.
Creative Propulsion—Redefinition — reframes what the problem is so progress becomes possible without leaving the domain.
Creative Propulsion—Forward Incrementation — pushes the field one step beyond the current frontier within its existing rules/notation.
Systemizing / Pattern-Seeking — drive to find lawful “if-then” structure and build controllable systems; rule discovery + mechanistic modeling.
Creative Propulsion—Integration — fuses rival or partial approaches into a new coherent framework, standard, or platform.

2) Knowledge acquisition & self-directed learning

Knowledge-Acquisition Components — selective encoding (what matters), comparison (link to prior), and combination (compose) that compress novelty into schemas.
Hippocampal Pattern Separation — stores similar episodes with clean boundaries; prevents interference and concept “smear.”
Hippocampal Pattern Completion — reconstructs whole memories from fragments; rapid fill-in under partial data.
Verbal Abstraction / Crystallized Intelligence (Gc) — dense semantic networks (vocabulary, concepts, relations) that provide precise meaning and quick access to domain knowledge.
Self-Translation Habit — automatic recoding of inputs into your dominant code (text↔diagram) to cut load and boost retention/transfer.
Tacit Knowledge Uptake / Practical Intelligence — absorbs unwritten rules and “if–then” productions of real contexts; performance beyond explicit instruction.
Narrative Encoding Bias (Eide; “N” in MIND) — scene-building memory that organizes knowledge as episodes for coherence, explanation, and future simulation.

3) Generative creativity & concept propulsion

Ideational Fluency — high-rate production of possibilities under minimal constraint; expands the search space.
Flexibility— shifts categories/approaches; explores multiple conceptual classes to avoid local minima.
Originality — statistically infrequent associations/solutions; remote recombinations that create distinctiveness.
Selective Combination — picks relevant elements and composes them into novel, workable configurations (“aha” synthesis).
Selective Comparison — maps new problems onto prior structures via deep analogies; principled transfer across domains.
Propulsion—Redirection — changes the direction of the field’s trajectory (not just its speed).
Propulsion—Reinitiation — restarts from a new origin when the current line stalls; new primitives, tools, and norms.
Investment Theory — decision tendency to “buy low/sell high” in the market of ideas: pursue unpopular bets, persuade the field, then rotate.

4) Analytical modeling & systems reasoning

Fluid Reasoning (Gf) — induction/relational integration with novel info; builds rules and hypotheses from sparse patterns.
Spatial Transformation (Gv) — mental rotation and structural visualization; reasoning with diagrams, flows, and 3D forms.
Working-Memory Updating (Gw/Gwm) — maintain, revise, and purge task-relevant representations under distraction; core to complex modeling.
Quantitative Reasoning (Gq) — formal magnitude/ratio reasoning; turns scenarios into equations, parameters, and constraints.
P-FIT Network Efficiency — efficient fronto-parietal integration that speeds abstraction ↔ hypothesis testing.
Systemizing Drive — rule extraction and mechanism building across technical/organizational systems (appears here as modeling motivation).
Cognitive Complexity Preference — comfort with multivariate, nonlinear structure; keeps many contingencies active without collapse.
Parsimony Bias (Model Compression) — seeks the smallest model that predicts well; disciplined variable pruning and principle finding.

5) Experimentation, evidence & causal inference

Hypothesis-Testing Set — operationalizes doubt: write falsifiable claims, choose tests, monitor, and evaluate.
Falsification-Seeking Evaluation — tries to break one’s own ideas; prioritizes disconfirmation and robustness over confirmation.
Error-Driven Learning Sensitivity — high gain on prediction-error signals; learns fast from expectation violations, respecting context specificity.
Causal-Graph Intuition (Systemizing + Gf) — favors mechanisms over correlations; designs interventions and instrumentation to test causal links.
Evidence Weighting & Continuous Monitoring — live updating of belief/strategy as data arrive; guards against noise-driven over/under-reactions.
Replication Orientation — consolidates truth by re-running and generalizing surprising results; clarity over novelty.
Advance Incrementation Bias — pushes farther along the current vector than consensus tolerates; “too early” contributions that age well.

6) Perception, imagery & representation

Verbaliser–Imager Preference — stable representational code (words vs. pictures) that shapes memory, learning, and explanation style.
Wholist–Analytic Orientation — habitual grain size (whole vs. parts) that structures dashboards, documents, and debugging.
Imagery Vividness & Control — high-fidelity, steerable mental images; internal prototyping and inspection before building.
Spatial Visualization Depth (Gv: VZ/SR) — transform/predict 3D structure from sparse 2D/graphical inputs; topology and interference reasoning.
Approximate Number System (ANS) Acuity — parietal magnitude mapping that yields quick, ratio-sensitive magnitude judgments.
Subitizing Capacity — rapid, exact small-number tracking (1–3) for fast scene parsing and updates under time pressure.
Detail Hyperacuity / Local-Feature Bias — local-first perceptual style (weak central coherence) enabling micro-precision and anomaly detection.

7) Attention, control & cognitive energy

Set-Shifting Agility (Cognitive Flexibility) — low cost of switching rules/strategies; pivots based on new constraints without perseveration.
Sustained Attention Endurance — long-duration goal maintenance with interference control; durable vigilance and follow-through.
Processing Speed (Gs) — fast execution of simple/overlearned ops; queue velocity and iteration rate.
Impulsivity–Reflectivity (Cognitive Tempo) — default speed–accuracy setting under uncertainty; quick bets vs. careful commits.
Working-Memory Interference Control — resist proactive interference; protect new rules from contamination by old ones.
Exploration–Exploitation Balance — neuromodulator-tuned tendency to search vs. harvest; calibrates novelty seeking and lock-in.
Cognitive-Load Management via Style — deliberate translation/format choice (diagram↔text; whole↔parts) to keep load in bounds.

8) Social-cognitive & communicative innovation

Tacit Political Knowledge (Practical Intelligence) — unspoken “how things get done” know-how (timing, coalitions, procedures) that moves ideas through real orgs.
Audience Reframing (Propulsion—Redefinition) — changes the lens so the same facts lead to different judgments and action.
Mode Translation / Code-Switching — recasts content between verbal/visual and whole/parts codes to fit audience cognition.
Social Judgment / Sagacity (Wisdom) — balances self/others/society across time; selects legitimizing means to desirable ends.
Paradigm-Integration Brokerage (Propulsion—Integration) — reconciles rival camps into shared frameworks, APIs, and standards.
Contextual Adapt–Shape–Select (Triarchic practical) — meta-choice among fitting the environment, changing it, or exiting to one that fits.
Style–Team Complementarity Sense — sees WA×VI style gaps and composes teams/artifacts that neutralize friction.

9) Execution, scaling & operationalizing

Planning–Monitoring–Evaluation (Metacomponents) — plan the work, instrument reality, evaluate, and revise; the control loop behind reliable delivery.
Schema Automation (Proceduralization) — cortico-striatal chunking that turns explicit steps into fast, low-variance habits/SOPs.
Throughput-Optimization Bias (Gs × Systemizing) — preference for rule-clean pipelines and cycle-time/WIP reduction; queueing mindset.
Tolerance for Delayed Gratification — motivational stamina for long, ambiguous arcs; capacity to invest now for compounding later.
Error-Taxonomy Instinct (Analytic Style) — default to classify, route, and contain failure modes; “first diagnose, then fix.”
Knowledge Crystallization (Gc Growth) — converts tacit wins into sharable playbooks, names, and checklists that raise team base rate.

10) Decision architecture, values & risk

Wise Reasoning (Balance Theory of Wisdom) — values-mediated selection of ends/means for the common good; balances stakeholders and horizons.
Risk Calibration for Creative Leadership (Investment/Propulsion) — decides how contrarian to be, how long to hold, and when to rotate, given field dynamics.
Integrative Complexity — differentiates competing models and then integrates them into coherent, constraint-respecting decisions.
Paradigm Acceptance–Rejection Set-Point (Propulsion Spectrum) — personal default to replicate, extend, redirect, restart, or integrate—and how hard to push.
Ethical Foresight & Guardrails — anticipates externalities and pre-commits to constraints/oversight so innovation remains legitimate.
Cultural Reframing Facility — adapt/shape/select across cultures; localize narratives and metrics without losing core values.
Crowd-Defying Endurance (Investment Conviction) — evidence-conditioned persistence on non-consensus theses through skepticism, then exit at adoption.

Talents

Group 1: Problem discovery & opportunity sensing

Purpose. This cluster captures how a brain notices, frames, and selects problems worth solving before any heavy analysis or execution begins. It’s the upstream engine for innovation pipelines: if you pick the wrong problem, no level of brilliance downstream will rescue the outcome.
Why it’s essential. In fast, uncertain markets, advantage comes from (1) spotting latent needs early, (2) defining solvable problem statements, and (3) shaping opportunities others miss. These predispositions reliably predict which ideas get traction and which die in committee.

1) Sensitivity to Problems

Definition (from source). Guilford described this factor as “the ability to anticipate or be sensitive to the needs of or the consequences of a given situation in meaningful terms.” Human cognitive abilities _ a s…
(Guilford also located “sensitivity to problems” within CMI—Cognition of Semantic Implications, with marker tasks like Seeing Problems, Apparatus Test, Pertinent Questions.)

Gist (3 lines).
• Quickly senses that “something’s off” in systems, plans, products, or processes.
• Generates pointed questions that surface hidden constraints and second-order effects.
• Prefers real-world, consequential problem spaces over puzzle-like tasks.

How unique is it / what’s unique to look for. Rare combination of threat/opportunity radar plus practical foresight; shows up as a reflex to list failure modes or downstream impacts from minimal cues (often before others see them). Factor-analytic work distinguished SP from other fluencies.

Skills it tends to define (downstream).

Root-cause spotting and early risk surfacing
Writing “pertinent questions” that reframe briefs
Scenario sketching (near-term effects from trends)
Usability critique & failure-mode enumeration
Internal red-teaming / pre-mortems

Professions that use it most (and why).

Product management / UX research – continuous detection of unmet needs and friction.
Operations & reliability engineering – anticipates points of failure.
Policy analysis / risk – maps consequences of interventions.
Venture building – senses where markets are about to “leak.”
These roles live or die by early detection of gaps and knock-on effects—SP is tailor-made.

2) Metacomponents—Problem Definition

Definition (from source). In Sternberg’s framework, metacomponents are higher-order processes used “to plan, monitor, and evaluate performance,” including “recognizing the existence of a problem, defining the nature of the problem, [and] deciding on a strategy.”

Gist (3 lines).
• Turns messy ambiguity into a crisp, solvable brief.
• Chooses methods and constraints deliberately before doing.
• Self-monitors and course-corrects while solving.

How unique is it / what to look for. Distinct from IQ-like “power”—it’s executive orchestration of cognition. You see it in people who rewrite unclear tasks into workable specs and keep the entire solve loop on rails.

Skills it tends to define.

Problem scoping & requirement setting
Decision framing and criteria design
Method selection (which tool for which problem)
Milestone planning & self-monitoring
Post-mortem evaluation loops

Professions that use it most (and why).

Strategy / management consulting – defines problems clients stated vaguely.
Tech lead / staff engineer – converts fuzzy epics into architecture and plans.
Scientific PI / research lead – operationalizes questions and methods.
Metacomponents are the meta-control layer these roles rely on.

3) Field Independence / Disembedding

Definition (from source). Field-(in)dependence concerns “individual dependency on a perceptual field when analysing a structure or form.” It generalizes from perception to disembedding in problem solving.

Gist (3 lines).
• Can pull the signal out of noisy, contextual “fields.”
• Sees the actual structure beneath surface clutter.
• Works well with diagrams, data, code, legal text—anything dense.

How unique is it / what to look for. A highly distinctive “x-ray” tendency: rapidly isolates relevant substructures and ignores misleading context; measured historically with rod-and-frame / embedded-figures paradigms.

Skills it tends to define.

Schema extraction from messy datasets
Refactoring (code, processes, contracts)
Model boundary setting & variable isolation
Interface/architecture decomposition
Clean abstraction & naming

Professions that use it most (and why).

Data science / quant analysis – identify latent structure in noise.
Software architecture – disentangle coupled components.
Forensic accounting / due diligence – find the pattern others miss.
All require ignoring context pull to extract true structure. Cognitive styles and learning s…

4) Wholist–Analytic Style → Opportunity-Scanning Bias

Definition (from source). The wholist–analytic dimension is the “tendency for the individual to process information in parts or as a whole.”

Gist (3 lines).
• Wholists default to broad, integrative scene-setting before zooming in.
• They sweep widely for relevance/signals across domains.
• Great for horizon-scanning and adjacencies.

How unique is it / what to look for. Uniquely high “global-context first” bias—often sees cross-domain implications and multi-stakeholder fit before details. (Riding’s work integrates this with verbal–imagery preferences and decision style.)

Skills it tends to define.

Landscape mapping & trend synthesis
Portfolio/thesis formation (what’s in/out)
Multi-stakeholder framing
Assumption surfacing across silos
Early go/no-go heuristics

Professions that use it most (and why).

Venture capital / corporate strategy – whole-system thesis building.
Design research / service design – maps the end-to-end service ecology.
Policy & urban planning – balances many interlocking constraints.

5) Creative Propulsion: Redefinition

Definition (from source). In propulsion theory, “redefinition shifts the problem posed so that a different yet related problem is solved.”

Gist (3 lines).
• Changes the question to unlock movement.
• Reframes goals so progress becomes possible.
• Yields surprising, workable briefs.

How unique is it / what to look for. The signature is problem-pivoting without leaving the domain—rarely taught, often innate to reframers who dislike dead-ends.

Skills it tends to define.

Reframing workshops & facilitation
Constraint re-authoring (rules that liberate)
Alternative metrics/OKRs design
“Jobs-to-be-done” reinterpretation
Negotiating new definitions of success

Professions that use it most (and why).

Product strategy – turn stalled features into valuable adjacent wins.
Mediation / negotiation – redefine issues to find agreement space.
Creative direction – re-briefs that unlock better creative territory.

6) Creative Propulsion: Forward Incrementation

Definition (from source). “Forward incrementation moves a field forward within its current notational system by extending work” already begun.

Gist (3 lines).
• Pushes the existing paradigm to its next logical step.
• Loves cumulative progress, not overthrow.
• Recognizes “near-frontier” moves others overlook.

How unique is it / what to look for. A precise edge-pusher—comfortable living at the current frontier’s boundary conditions without changing the language of the field.

Skills it tends to define.

Road-mapping adjacent nexts
Incremental innovation design (10–30% lifts)
Technical debt pay-down that enables new capability
Standards-compliant extension writing
Benchmark chasing & micro-optimizations

Professions that use it most (and why).

Applied R&D / performance engineering – squeezes measurable gains.
Product iteration teams – compounding improvements.
Process excellence / Lean – kaizen-style forward steps.

7) Systemizing / Pattern-Seeking

Definition (from source). Systemizing is the drive to analyze or build rule-based systems to predict/control events

Gist (3 lines).
• Compulsively discovers lawful structure in data and mechanisms.
• Prefers explicit rules, testable regularities, and invariants.
• Creates models/automation to control variability.

How unique is it / what to look for. Strong, trait-like motivation to formalize domains; often linked to STEM excellence and some autistic phenotypes—distinct from general ability because it’s a drive plus an analysis style.

Skills it tends to define.

Causal model building & controllability analysis
Ontology/schema design
Automating repeatability / MLOps pipelines
Invariant detection & rule extraction
Formal verification / test design

Professions that use it most (and why).

Software / ML engineering – turn phenomena into reliable systems.
Quant finance / econometrics – formalize patterns for prediction/control.
Industrial automation / robotics – make processes ruleful and stable.

8) Creative Propulsion: Integration

Definition (from source). Integration “combines elements of disparate approaches to produce syntheses that go beyond stage-setting contributions.”

Gist (3 lines).
• Fuses partial solutions into a new, coherent whole.
• Resolves trade-offs by inventing third ways.
• Produces frameworks others can build on.

How unique is it / what to look for. A rare synthesizer signature—gravitates to boundary zones, sees compatibilities between “competing” paradigms, produces integrative standards.

Skills it tends to define.

Cross-paradigm translation (bridge concepts)
Interface & protocol design
Conflicts-to-complements patterning
Reference-architecture writing
Common-language / taxonomy creation

Professions that use it most (and why).

Platform / ecosystem architecture – integrates many teams’ solutions.
Standards bodies / policy design – synthesize stakeholders into usable policy.
Interdisciplinary research – reconcile methods into joint advances.

Group 2: Knowledge acquisition & self-directed learning

Purpose. This cluster covers the brain’s learning machinery: how we pick what to encode, how we store it so it won’t interfere, how we reconstruct it on cue, and how we turn raw exposure into usable, situated know-how.
Why it’s essential. In an innovation economy, speed-to-understanding is leverage. People who (a) filter relevance, (b) build clean episodic indexes, (c) abstract verbal/conceptual structure, (d) recode inputs into their preferred representational format, (e) soak up tacit rules, and (f) “think in episodes” get up the curve faster, transfer better, and outlearn their roles.

9) Knowledge-Acquisition Components

Definition (from source). In the triarchic/successful intelligence framework, knowledge-acquisition components “are used to learn how to solve problems or simply to acquire declarative knowledge in the first place,” including selective encoding (what matters), selective comparison (link to prior knowledge), and selective combination (compose a coherent solution).

Gist (3 lines).
• Filters signal from noise, hooks new info to old, then composes it into a usable whole.
• Especially active when information is novel, sparse, or messy.
• Distinct from executing a plan (performance) or managing the plan (metacomponents).

How unique it is / what to look for. A distinctive learning fingerprint: people who intuitively triage sources, surface analogs, and then “click together” fragments into a schema—often visible in how they annotate, cross-reference, and summarize.

Skills it tends to define.

Rapid literature triage & synthesis (selective encoding)
Schema building from cross-domain analogies (selective comparison)
Framing one-pagers/briefs from disparate notes (selective combination)
Cold-start learning: turning vague docs into working models
Designing personal knowledge systems (tags, ontologies, zettelkasten)

Professions that use it most (and why).

Equity/market research, investigative journalism — constant filtering + rapid schema formation.
Founders/PMs — must compress noisy inputs into action-ready briefs.
Academic & applied research leads — convert novelty into testable models fast.

10) Hippocampal Pattern Separation

Definition (from source). The hippocampus supports pattern separation—keeping similar episodes distinct by indexing them to different neuronal ensembles, thereby reducing interference.

Gist (3 lines).
• Stores look-alike experiences as non-confusable memories.
• Prevents “conceptual smear” between adjacent cases, versions, or contexts.
• Enables fine discrimination under similarity and time pressure.

How unique it is / what to look for. People strong here naturally keep versions clean—they notice subtle deltas between near-duplicates, avoid overgeneralization, and show low false-alarm rates when details differ.

Skills it tends to define.

Version control in head (A/B/C cases never blur)
Edge-case identification in requirements & data labeling
Differential diagnosis / root-cause segregation
Fine-grained threat or fraud discrimination under noise
Experimental condition hygiene (avoids leakage/contamination)

Professions that use it most (and why).

Clinical diagnosis, QA/safety, security/fraud analytics — success = distinguishing “near neighbors.”
Data curation/ML eval — prevents label bleed and metric illusions.

11) Hippocampal Pattern Completion (Rudy)

Definition (from source). With a partial cue, the hippocampal index can replay the entire stored episode—a process called pattern completion.

Gist (3 lines).
• From fragments, reconstructs the full scene or concept.
• Powers “I’ve seen this before” leaps when inputs are partial.
• Crucial for recall under pressure or degraded data.

How unique it is / what to look for. A hallmark of content-addressable memory: rapid “fill-in-the-blanks” retrieval; people retrieve the whole protocol/story/product behavior from tiny traces.

Skills it tends to define.

Incident response from incomplete telemetry
Forensic reconstruction & post-mortems
Autocomplete of specs from minimal cues (pattern libraries)
Fast recall of “playbooks” in live operations
Context restoration for broken customer journeys

Professions that use it most (and why).

SRE/DevOps, emergency medicine, cybersecurity — decisions must be made on partial data.
Search/recommendation, memory-dependent UX — leverage fragment→full retrieval.

12) Verbal Abstraction / Crystallized Intelligence (Gc)

Definition (from source). Gc reflects the extent to which an individual has learned and profited from culture and education—an “end product” of experience—distinct from fluid reasoning.

Gist (3 lines).
• Dense semantic networks: words, concepts, definitions, relations.
• High precision in meaning; fast access to domain knowledge.
• Grows with reading, discourse, and varied exposure.

How unique it is / what to look for. Deep semantic lattices—people who spontaneously define, contrast, and contextualize terms, and detect misuse of concepts in real time.

Skills it tends to define.

Standards/ontology authoring and term hygiene
Policy/legal/clinical guideline interpretation
High-fidelity summarization & explanation
Query/design spec wording that prevents ambiguity
“Concept debugging” (fixing fuzzy definitions in teams)

Professions that use it most (and why).

Law/policy/standards, scientific editing — precision of meaning is the product.
Customer education/enablement — translate complexity into crisp semantic frames.

13) Self-Translation Habit

Definition (from source). Translation strategies “recast information… into a mode that makes it easier to process and understand” (e.g., text→diagram for imagers; picture→verbal for verbalisers).

Gist (3 lines).
• Automatic recoding into your dominant representational code.
• Reduces cognitive load and boosts retention/transfer.
• A style-aware “learning toolchain,” not a preference slogan.

How unique it is / what to look for. A meta-learning reflex: these people routinely redraw, reword, or restructure inputs to suit their brain’s pipeline—often with measurable speed/accuracy benefits.

Skills it tends to define.

Building personal diagrams/tables from prose (or vice versa)
Writing “glossaries + visuals” that stick for teams
Low-load briefing decks from dense research
Rapid curriculum or doc localization across audiences
Designing UI/UX artifacts that mirror cognitive style

Professions that use it most (and why).

Design research, tech writing, knowledge ops — success = making info fit brains.
Enablement/education — translate formats to unlock learning at scale.

14) Tacit Knowledge Uptake / Practical Intelligence

Gist (3 lines).
• Picks up unwritten rules, workflows, and politics quickly.
• Encodes them as actionable “if–then” productions.
• Predicts performance above/beyond IQ in many roles.

How unique it is / what to look for. A situated learning drive: people infer “how things really get done” by watching outcomes and micro-cues, building private playbooks fast.

Skills it tends to define.

Rapid context modeling (adapt/shape/select environments)
Stakeholder mapping & unspoken-rule navigation
Process hacking (how to get approvals/resources quickly)
Mentoring-by-scenarios and shadow learning
Anticipatory escalation/coalition building

Professions that use it most (and why).

Management/sales/ops leadership — effectiveness depends on unwritten procedures.
Military/first-line supervision — scenario-based “if–then”s outperform abstract rules.

15) Narrative Encoding Bias / N-Strengths

Definition (from source). Narrative reasoning (“N” in MIND): ability to construct connected “mental scenes” from episodic fragments to recall the past, explain the present, and simulate futures; many dyslexic individuals show a profoundly narrative character of reasoning and memory.

Gist (3 lines).
• Stores and thinks in episodes/scenes, not disembodied facts.
• Excels at coherence-building, explanation, and scenario play.
• Fuels communication, persuasion, and design for humans.

How unique it is / what to look for. A scene-construction bias: they default to stories, personas, motives, and cause-effect arcs—even in technical material—often yielding superior coherence and recall.

Skills it tends to define.

Customer-journey storytelling & service blueprinting
Case-based reasoning (medicine, law, support)
Vision narratives / product strategy storytelling
Teaching via analogies and scenario-based learning
Incident/decision reviews written as causal narratives

Professions that use it most (and why).

Product marketing, policy advocacy, teaching — story is the lever for understanding & action.
Clinical medicine, UX research — case histories and journeys are the data structure.

Group 3: Generative creativity & concept-propulsion

16) Ideational Fluency

Definition (from source). Within Guilford’s divergent-production family, ideational fluency is a fluency factor distinct from flexibility and originality—indexed by tasks requiring many ideas in response to open prompts (e.g., Topics, Thing Categories).

Gist (3 lines).
• Rapid, high-volume idea generation under minimal constraints.
• Low internal censorship; associative networks “spill” options.
• Expands the search space before evaluation.

How unique is it / what’s unique. A right-tail tendency for sustained output rate independent of category shifting or rarity; psychometrics separates it from flexibility/originality.

Skills it tends to define.

Fast brainstorming & alternative listing
Generating edge cases / test cases
Early feature/options backlogs
Scenario enumeration & contingency design
Prompt engineering / idea remixing

Professions that use it most (and why).

Product/UX concepting and creative direction — volume → selection.
Foresight/research strategy — explore wide hypothesis space.
Game design — many mechanics/levels quickly.

17) Flexibility

Definition (from source). Divergent-production flexibility reflects shifts of category or approach (distinct factors from fluency), enabling movement across conceptual classes.

Gist (3 lines).
• Switch lenses (user, function, constraint) fluidly.
• Avoid perseveration—try different taxonomies.
• Escapes local minima.

How unique is it / what’s unique. Distinctive distribution across categories rather than sheer count; measured independently of fluency/originality.

Skills it tends to define.

Reframing requirements and briefs
Cross-domain analogy swapping
Constraint-driven redesigns
Multi-perspective ideation workshops
Pivoting strategy hypotheses

Professions that use it most (and why).

Innovation consulting / policy design — unlocks stuck problems by changing frames.
Clinical reasoning / A/B strategy — shift hypotheses quickly.

18) Originality

Definition (from source). Divergent-production originality is a factor separable from fluency/flexibility, reflecting statistically infrequent responses (novelty).

Gist (3 lines).
• Generates rare, non-obvious associations.
• “Jumps” beyond conventional patterns.
• Produces distinctive, high-separation concepts.

How unique is it / what’s unique. Emerges from atypical associative search paths; rarity persists even when output volume is controlled.

Skills it tends to define.

Non-obvious feature invention
Category-creating use-cases
Naming/branding that breaks schema
Contrarian experiment design
Generative concept art/vision work

Professions that use it most (and why).

Breakthrough R&D and venture creation — non-incremental moves.
Brand/narrative design — distinctiveness is the product.

19) Selective Combination

Definition (from source). A knowledge-acquisition component: “putting together the relevant information” into a coherent configuration for insight.

Gist (3 lines).
• Picks the right pieces, then composes them into a new whole.
• Core to “aha” synthesis and minimal-viable bundles.
• Turns fragments into architectures.

How unique is it / what’s unique. Strong compositional working model capacity: many parts active yet integrable into a usable structure.

Skills it tends to define.

Feature architecture / product bundling
Multi-source literature synthesis
Systems concepting (interfaces/modules)
Multimodal data fusion
Patentable combination inventions

Professions that use it most (and why).

PM/product architecture — decide which pieces belong together.
Systems engineering / bioinformatics / AI — combine heterogeneous components.

20) Selective Comparison

Definition (from source). A learning component: “relating new information to information already stored in memory” (analogical projection / transfer).

Gist (3 lines).
• Retrieve the right prior pattern for a new case.
• See deep (not surface) similarity.
• Enables fast transfer across domains.

How unique is it / what’s unique. Dense, well-indexed semantic memory exposes structural matches quickly; hallmark of fast cross-domain thinking.

Skills it tends to define.

Analogy-led design & explanation
Zero-to-one feature seeding from other fields
Heuristic reuse with adaptation
Naming metaphors that stick
Transfer-learning choices (human & ML)

Professions that use it most (and why).

Architecture & interface design — metaphor scaffolds usability.
Science/theory building, litigation, AI/ML — principled transfer is the engine.

21) Propulsion: Redirection

Definition (from source). A creative contribution that “attempts to redirect the field” into a different direction from its present trajectory.

Gist (3 lines).
• Detects path-dependence → proposes a turn.
• Builds a compelling alternate vector.
• Catalyzes adoption despite inertia.

How unique is it / what’s unique. Requires field sense plus originality; crowd-defying by design, often meeting initial resistance.

Skills it tends to define.

Contrarian thesis crafting
Agenda-setting roadmaps
Coalition building for pivots
Strategic storytelling for change
Transitional governance design

Professions that use it most (and why).

CTO/CPO & think-tank leads — steer tech/policy courses.
Standards bodies — relocate consensus path.

22) Propulsion: Reinitiation

Definition (from source). “Move the field to a different as-yet unreached starting point and then move from that point.”

Gist (3 lines).
• Declares a dead end; restarts with new primitives.
• Bootstraps fresh scaffolds, tools, and norms.
• Accepts sunk-cost loss for long-run gain.

How unique is it / what’s unique. Clean-slate cognition with high ambiguity tolerance; typically the most crowd-defying propulsion class.

Skills it tends to define.

Greenfield platform founding
From-scratch ontology/taxonomy design
Toolchain bootstrapping
Institutional entrepreneurship
Early-ecosystem shaping

Professions that use it most (and why).

Frontier-tech founders / research-infra builders / open-source initiators — create new starting points others can build upon.

23) Investment Theory: Buying Low, Selling High

Definition (from source). Creativity as a decision: “buy low and sell high in the world of ideas,” i.e., pursue undervalued ideas, then persuade the field and move on to the next unpopular idea.

Gist (3 lines).
• Select unpopular but promising ideas; endure skepticism.
• Marshal evidence and allies; time adoption.
• Exit before commoditization; repeat.

How unique is it / what’s unique. Blends cognition with risk appetite, motivation, and persuasion; explains why not all original thinkers choose contrarian bets.

Skills it tends to define.

Hypothesis staking & thesis maintenance
Evidence marshaling under uncertainty
Evangelizing & coalition formation
Timing of inflection points / exits
Portfolioing multiple contrarian bets

Professions that use it most (and why).

VC/angel & venture studios — idea-market timing is the job.
Platform/product strategy — bet on non-consensus wedges.
Frontier research leads — champion “ahead-of-time” lines.

Group 4 — Analytical modeling & systems reasoning

Purpose
Turn messy realities into coherent, testable models: discover rules, map structures, quantify relations, and run “what-if” simulations.

Why it’s essential
Innovation scales when you can compress complexity into tractable representations—laws, metrics, algorithms, diagrams—then refine them with evidence.

1) Fluid reasoning (Gf) — Induction & relational integration

Definition (source)
Reasoning with novel information, typically measured with inductive tasks (e.g., Raven’s), and tightly linked to working-memory mechanisms; Gf and WM share ~50–80% latent variance.

Gist (3 lines)
• See patterns and relations with minimal prior knowledge.
• Integrate constraints, discard dead-ends, update hypotheses.
• Core driver for learning new domains fast.

How unique?
Large stable individual differences; strongly predicted by executive attention and the ability to disengage from outdated assumptions.

Skills shaped

Breaking novel puzzles down
Rapid concept formation
Hypothesis testing under uncertainty
Debugging logic chains
Adapting strategies mid-task

Professions that lean on it & why

Quant research & data science: abstracting signal from noise in unfamiliar datasets.
Strategy consulting: structuring ambiguous problems into solvable frames.
Security analysis/intelligence: pattern discovery with sparse cues.
Scientific discovery roles: moving beyond known schemas.
Product strategy: reframing messy market signals into decisions.

2) Sequential/deductive reasoning (RG)

Definition (source)
Reasoning that proceeds stepwise through premises to conclusions; a distinct narrow ability under the CHC structure (e.g., series completion, syllogisms).

Gist (3 lines)
• Chain implications without leaks.
• Handle multi-step dependencies.
• Keep invariants straight.

How unique?
Distinct from (but correlated with) induction; supports formal proof, algorithm tracing, and rule-bound decision paths.

Skills shaped

Writing proofs/specs
Policy/compliance interpretation
Pipeline/algorithm tracing
Failure-mode & effects logic
Protocol design

Professions that lean on it & why

Backend/infra engineering: correctness over long chains.
Law & regulation: premise-to-verdict rigor.
Safety engineering: formal hazard logic.
Finance operations/risk: rule-consistent execution.
Compiler/verification roles: exactness of inference.

3) Quantitative reasoning (RQ)

Definition (source)
Understanding quantitative concepts and relations; forming and manipulating mathematical rules beyond rote computation.

Gist (3 lines)
• Think in equations, constraints, magnitudes.
• Build parameterized models.
• Translate scenarios into math.

How unique?
Anchored in parietal number systems that support quantity abstraction and symbolic math learning.

Skills shaped

Modeling KPIs & unit economics
Dimensional analysis & scaling laws
Optimization & sensitivity analysis
Probabilistic forecasting
Experimental design power/threats

Professions that lean on it & why

Economics & policy analysis: causal quant models.
Machine learning science: loss functions & evaluation.
Operations research: constraints/optimization.
Biostatistics/epidemiology: inference under noise.
Climate/energy modeling: systems with parameters.

4) Visual–spatial transformation (Gv; VZ/Spatial Relations)

Definition (source)
The ability to mentally rotate, transform, and integrate visual forms; classic tasks include paper-folding, embedded figures, cube rotations—core narrow abilities under Gv (Visualization/Spatial Relations).

Gist (3 lines)
• See structure in space and time.
• Convert 2D↔3D, track topology.
• Reason with diagrams and flows.

How unique?
Relatively distinct from verbal abilities; strongly predicts success in STEM design and complex system diagramming.

Skills shaped

Architecture of systems (block-diagram thinking)
Circuit/graph reasoning
Spatial debugging (layout, pipelines)
CAD/CAE mental simulation
UI/flow mapping

Professions that lean on it & why

Hardware/ASIC & mechanical design: fit, tolerance, interference.
Network/SRE architecture: topology reasoning.
Robotics/autonomy: geometry of motion/perception.
UX flows & information architecture: spatial metaphors.
Geospatial/urban planning: layered spatial constraints.

5) Working-memory updating & executive attention (Gsm/Gwm)

Definition (source)
The system that maintains and manipulates task-relevant information under distraction; executive attention processes (updating, inhibition, set-shifting) closely tie WM to Gf.

Gist (3 lines)
• Keep the right items “online.”
• Drop stale assumptions fast.
• Juggle constraints without collapse.

How unique?
Beyond capacity, the disengagement component—ability to purge no-longer-relevant info—differentiates high-level reasoning performance.

Skills shaped

Multistep mental simulation
Context switching without drift
Rapid reprioritization
Complex span reading/ops
Real-time troubleshooting

Professions that lean on it & why

Incident command/SRE: maintain situational state under pressure.
Trading & dispatch: live feed integration and inhibition of noise.
Surgical teams/ATC: strict attentional control.
PMs in complex orgs: update plans as evidence changes.
Gameplay/robotics engineers: real-time state management.

6) Number sense & magnitude mapping (parietal/IPS systems)

Definition (source)
A biologically grounded system for representing approximate quantity and mapping symbols to magnitudes; intraparietal sulcus central in numerical cognition.

Gist (3 lines)
• Intuitive feeling for “how big.”
• Fast ratio/scale judgments.
• Smooth link between symbols and quantities.

How unique?
Individual variability in acuity (e.g., ratio sensitivity) supports learning of formal mathematics and efficient quantitative estimation.

Skills shaped

Back-of-envelope sizing
Sanity checks & order-of-magnitude
Pricing & cost curves
A/B uplift intuition
Resource leveling

Professions that lean on it & why

Growth/product analytics: quick plausibility checks.
Supply-chain & logistics: capacity/throughput feel.
FP&A: intuitive forecasting & variance sense.
Energy/grid ops: load/margin intuition.
Sports analytics: rate/ratio reasoning.

7) Systemizing drive (rule extraction & mechanism building)

Definition (source)
A “drive to analyze or build systems”—identifying input-operation-output rules to predict and control events; elevated systemizing is tied to invention and STEM pursuits.

Gist (3 lines)
• Hunt for “if-and-then” rules.
• Tinker, instrument, and refine mechanisms.
• Preference for lawful, deterministic structure.

How unique?
Trait-like motivation toward rule discovery and system construction; often co-occurs with analytical strengths and intense focus.

Skills shaped

API/architecture design
Experimental instrumentation
Rule mining & feature engineering
Versioning and configuration logic
Process control & automation

Professions that lean on it & why

DevTools/platform: building predictable platforms.
Manufacturing/process engineering: control of inputs/outputs.
Algorithmic trading: formalizing signals as rules.
Game engine/simulation: coherent rule-sets.
Knowledge graph / ontologies: lawful structure building.

8) Parieto-Frontal Integration (P-FIT) network efficiency

Definition (source)
A neurocognitive account positing that efficient integration across parietal and frontal regions underlies reasoning and intelligence performance.

Gist (3 lines)
• Efficient long-range fronto-parietal communication.
• Faster evidence accumulation & model revision.
• Better transfer across problem types.

How unique?
Explains why some people generalize across tasks: their networks integrate sensory abstraction (parietal) with hypothesis testing (frontal) more efficiently.

Skills shaped

Cross-domain transfer learning
Rapid re-modeling when feedback arrives
Meta-problem-solving (choosing the right tool)
Coordinating perception ↔ decision loops
Complex, multi-modal analytics

Professions that lean on it & why

CTO/Chief scientist: integrative reasoning across tech stacks.
Research leads: stitching disparate findings into theories.
Autonomy/perception leads: perception-decision integration.
Advanced analytics leaders: orchestrating multi-model insights.
Systems architects: end-to-end coherence.

Group 5 — Experimentation, evidence & causal inference

Purpose
Turn uncertainty into knowledge by posing testable claims, breaking them, and updating models.

Why it’s essential
Innovation compounds when people can operationalize doubt: they form hypotheses, try to refute them, monitor evidence quality, and decide when to replicate versus leap ahead.

24) Hypothesis-testing set (Sternberg metacomponents)

Definition (from source). In successful-intelligence, metacomponents “plan what to do, monitor the plans as they are being carried out, and evaluate them after they are done” (e.g., recognize/define the problem → choose a strategy → monitor → evaluate).

Gist (3 lines).
• Defaults to “what would disconfirm this?” over “how do I prove it.”
• Turns vague questions into operational tests and stopping rules.
• Treats plans as hypotheses that must survive monitoring.

How unique is it / what’s unique. Strong executive framing: rapid problem definition and strategy selection, plus disciplined mid-course evaluation (not just ideation).

Skills it tends to define.

Writing clear hypotheses & success/fail criteria
Designing measurable experiments/AB tests
Pre-mortems & decision logs
Choosing minimal tests with maximal information
Killing or scaling bets based on evidence

Professions that use it most (and why).

Product/Growth science & ops research — continuous test-learn cycles.
Clinical/field trials — protocolized hypothesis→monitoring loops.
Security/IR — hypotheses under time pressure, iterative validation.

25) Falsification-seeking evaluation (monitoring one’s products)

Definition (from source). Metacomponents are “important for monitoring and evaluating one’s products… [to] separate… wheat from the chaff.”

Gist (3 lines).
• Habit of trying to break one’s own ideas before others do.
• Preference for disconfirming tests over confirmatory ones.
• Protects against premature convergence and hype.

How unique is it / what’s unique. Combines sharp self-critique with the willingness to be crowd-defying when evidence warrants (investment/propulsion perspectives).

Skills it tends to define.

Red-team design & adversarial reviews
Counter-hypothesis generation
“Ablation” testing of features/assumptions
Pre-registration & guardrail metrics
Stopping early when effects don’t survive

Professions that use it most (and why).

Safety/reliability engineering — find failure before launch.
Quant & risk — search for refuters, not just confirmers.
Peer review/editorial — stress-test claims for robustness.

26) Error-driven learning sensitivity (mismatch → new learning)

Definition (from source). In extinction research, the absence of an expected outcome produces new learning (CS–noUS), not mere erasure; NMDA-dependent mechanisms and context/hippocampal control shape how prediction-mismatch updates behavior.

Gist (3 lines).
• Learns fastest from violations of expectation.
• Treats errors as informative signals, not setbacks.
• Updates differently by context; tracks when a rule no longer holds.

How unique is it / what’s unique. High gain on prediction error—people here rapidly re-weight rules when outcomes deviate; they also respect context-specificity of learning.

Skills it tends to define.

Designing “error-rich” training loops (fast feedback)
Shadow-mode rollouts to harvest surprises
Post-mortems that separate erasure vs. new-rule learning
Contextual AB/ABA tests (renewal/reinstatement aware)
Data-driven deprecation of stale heuristics

Professions that use it most (and why).

SRE/DevOps & autonomy — systems must learn from anomalies.
Behavioral health/ed-tech — exploit mismatch for durable change.
ML ops — drift detection & online adaptation.

27) Causal–mechanistic reasoning (Systemizing)

Definition (from source). The Systemizing Mechanism analyzes the world into if-and-then rules to predict and control events; neurally, it leans on lateral frontoparietal connections and intraparietal sulcus (IPS) engagement in numerical/mechanical reasoning.

Gist (3 lines).
• Prefers mechanisms over mere correlations.
• Builds controllable input→operation→output models.
• Generalizes to social/organizational “systems” when repeatable.

How unique is it / what’s unique. A trait-like drive to extract lawful structure; often co-occurs with detail focus and tolerance for tinkering until a rule holds.

Skills it tends to define.

Causal diagrams & intervention design
Instrumentation/telemetry for mechanism testing
Feature gating & control-group hygiene
Protocol/process engineering
Bug triage to root causes (not symptoms)

Professions that use it most (and why).

Platform/infra & process engineering — controllability matters.
Policy ops/regulation — if-then compliance mechanisms.
Epidemiology/market design — interventions, not correlations.

28) Evidence weighting & continuous monitoring

Definition (from source). Metacomponents monitor execution and evaluate outcomes; they govern the ongoing assessment of whether a strategy is working.

Gist (3 lines).
• Keeps a live scoreboard for evidence strength/quality.
• Re-balances beliefs as new data arrive (not just at milestones).
• Distinguishes signal from artifacts/contexts.

How unique is it / what’s unique. Habitual meta-awareness of data quality and context; avoids over-updating to noise or under-reacting to real shifts.

Skills it tends to define.

Sequential testing & Bayesian-style updating
Power checks, MDE, and guardrail design
Instrumentation QA & data lineage reviews
Interim analyses without alpha-spending errors
“Stop/go/iterate” governance

Professions that use it most (and why).

Growth analytics & experimentation — many small, monitored bets.
Clinical/biostat — interim monitoring for efficacy/safety.
Industrial quality — SPC and continuous evidence tracking.

29) Replication orientation (Propulsion Type 1)

Definition (from source). Replication “helps solidify the current state of a field”; especially valuable when a finding is surprising—success clarifies where the field really is.

Gist (3 lines).
• Re-runs surprising results to establish ground truth.
• Prefers robustness over novelty.
• Treats failed replications as informative course-corrections.

How unique is it / what’s unique. Atypically low novelty-pressure + high rigor; builds reputations for reliability and clarity about baselines.

Skills it tends to define.

Protocol fidelity & preregistration
Data/version control and environment parity
Sensitivity analyses & robustness checks
Cross-site/multi-cohort coordination
Reporting nulls and boundary conditions

Professions that use it most (and why).

Reliability/QA & test engineering — product truth > novelty.
Pharma/biomarkers — reproducibility underwrites approvals.
Security research/standards — proofs must generalize.

30) Advance incrementation bias (Propulsion Type 4)

Definition (from source). Advance forward incrementation: contributions “ahead of [their] time,” extending the field farther along its current vector than the field is ready to go.

Gist (3 lines).
• Pushes an existing trajectory faster/further than consensus tolerates.
• Requires strong grasp of the base + realistic path to adoption later.
• Often under-valued early, prized post-inflection.

How unique is it / what’s unique. Blends deep trajectory analysis with timing patience; often misread as “too early,” then becomes obvious.

Skills it tends to define.

Long-horizon road-mapping under today’s constraints
Technical debt/feasibility staging toward the future state
Evangelizing with credible intermediate milestones
Patent strategy that anticipates the curve
Ecosystem seeding (data, tooling, standards)

Professions that use it most (and why).

CTO/architecture & research leadership — carry the line forward early.
Venture creation/platform strategy — bet before the market’s ready.
Public-goods infra (standards, datasets) — build precursors to unlock waves.

Group 6 — Perception, imagery & representation

Purpose
Encode the world efficiently: how a brain perceives, imagines, and represents information (verbally, visually, numerically) to build working models.

Why it’s essential
Innovation rides on internal representations. Vivid imagery, robust spatial models, and calibrated magnitude sense let people compress reality into diagrams, equations, and narratives others can use.

31) Verbaliser–Imager preference

Definition (from source). People differ in their preferred representational code: verbalisers habitually use verbal associations; imagers rely on pictures/diagrams, with distinct effects on learning and expression preferences.

Gist (3 lines).
• Default “language” of thought: words vs. pictures.
• Shapes recall strategies and how ideas are explained.
• Predicts which media accelerate comprehension.

How unique is it / what’s unique. It’s a stable style influencing spontaneous imagery/verbal activation and the stability of those representations (imagers’ pictures can be vivid yet more interference-prone; verbalisers’ verbal chains more stable), yielding distinctive communication strengths.

Skills it tends to define.

Technical writing vs. diagram-first explanation
Selecting effective notations (text, schema, storyboard)
Rapid verbal outline vs. sketching systems
Memory scaffolds (mnemonic phrases vs. mind maps)
Audience-tuned docs (dense prose vs. infographics)

Professions that use it most (and why).

Policy/legal and editorial (verbal precision for verbalisers).
UX/architecture/visual analytics (diagrammatic clarity for imagers).
These roles exploit the code that best compresses complexity for stakeholders.

32) Wholist–Analytic orientation

Definition (from source). A complementary style: wholists process information as integrated wholes; analytics segment into parts—affecting preferred modes of expression (text, speech, diagrams, pictures) and task approach.

Gist (3 lines).
• Default granularity: gestalt vs. components.
• Alters sequence: overview→detail vs. parts→whole.
• Guides design of instructions and dashboards.

How unique is it / what’s unique. It’s not ability but habitual granularity, strongly shaping document structure and interface preferences—wholists excel at coherence; analytics at decomposition.

Skills it tends to define.

Information architecture (global nav vs. modular IA)
Decomposition for estimation & scoping
Diagram layering (big-picture maps vs. component specs)
Risk aggregation vs. root-cause drill-downs
Narrative structure (executive summaries vs. appendices)

Professions that use it most (and why).

Product strategy & systems design (wholists keep coherence).
QA/forensics & data engineering (analytics isolate faults fast).

33) Imagery vividness & control (eidetic-like capacity)

Definition (from source). Some individuals show unusually vivid internal imagery—up to eidetic/photographic-like retention—supporting detailed mental inspection and transformation of forms.

Gist (3 lines).
• “See” designs in mind with high surface fidelity.
• Run mental try-outs before building.
• Translate perception → motor plans accurately.

How unique is it / what’s unique. A right-tail vividness/precision of imagery; sources document cases with near-photographic retention fueling representational accuracy far beyond norms.

Skills it tends to define.

Mental prototyping (simulate, then implement)
Rapid redlining without external aids
Visual memory for layouts/assemblies
Precise scene reconstruction & forensics sketching
Visual metaphor generation for communication

Professions that use it most (and why).

Industrial/architectural design & VFX—iterate internally at low cost.
Surgery/robotics & sports technique—mental rehearsal enhances precision.

34) Spatial visualization (Gv; VZ/SR) depth

Definition (from source). Broad Visualization (VZ) and Spatial Relations (SR) factors encompass mental rotation, paper-folding, surface development, perspective taking, and reasoning about hidden structure in 3D arrays.

Gist (3 lines).
• Rotate/transform objects and viewpoints.
• Infer unseen structure from sparse cues.
• Use diagrams as thinking tools.

How unique is it / what’s unique. Factor-analytic work isolates VZ from speeded perceptual factors; top performers reason in unspeeded, complex spatial tasks near general reasoning tests.

Skills it tends to define.

CAD/CAE mental simulation & interference checks
Circuit/graph comprehension and routing
Mechanical reasoning & assembly planning
UI/flow mapping and state-space sketching
Geospatial transforms (map↔aerial↔3D)

Professions that use it most (and why).

Hardware/ASIC/mech design, robotics, SRE topology—geometry governs fit and failure.
Geo/urban planning & AR/VR—complex 3D constraints and perspective shifts.

35) Approximate Number System (ANS) acuity

Definition (from source). A parietal magnitude system supports approximate quantity representations and operations; individual acuity predicts later mathematics achievement beyond g and SES.

Gist (3 lines).
• Intuitive “how big/how many” judgments.
• Ratio-dependent discrimination (Weber’s law).
• Scaffolds symbolic math learning.

How unique is it / what’s unique. People vary in numerical acuity and connectivity of intraparietal areas to frontal systems; sharper ANS → better transfer to formal math.

Skills it tends to define.

Order-of-magnitude checks & sizing
Pricing/elasticity intuition
KPI design with sensible bins/thresholds
Forecast plausibility triage
Fast A/B uplift intuitions

Professions that use it most (and why).

Growth/analytics, FP&A, ops—constant magnitude judgments.
Energy/grid & logistics—capacity, load, and ratio reasoning.

36) Subitizing capacity (object-tracking system)

Definition (from source). Subitizing (exact recognition of 1–3 items) is distinct from approximation; behavioral and imaging evidence show a separate small-number system that tracks objects precisely.

Gist (3 lines).
• Instant, error-free perception of tiny numerosities.
• Supports exact updates when items move in/out.
• Different mechanism from ANS (yet both co-represent 1–3).

How unique is it / what’s unique. Upper limit and speed vary; higher capacity/latency advantages aid rapid scene parsing and object-file updates in dynamic tasks.

Skills it tends to define.

Visual attention in fast UIs/air-traffic-like panels
Real-time object tracking (perception & autonomy)
Quality checks (count/compare at a glance)
Event monitoring & anomaly spotting
Sports/robotics perception (players, drones, parts)

Professions that use it most (and why).

ATC/operations dispatch, video analytics, autonomy—small-set exactness under time pressure.

37) Detail hyperacuity / local-feature bias (weak central coherence profile)

Definition (from source). In weak central coherence, attention favors parts/details over global form; savant cases often show meticulous, high-fidelity visual output and right-hemisphere–leaning skills, sometimes via “release” of posterior visual systems.

Gist (3 lines).
• Extraordinary fidelity to local structure.
• Preference for rigid, rule-rich systems (e.g., calendars).
• Can power precision art, calculation, and spatial skills.

How unique is it / what’s unique. Atypical local-first processing can yield hyper-detail advantages—documented in autistic and acquired savant cases—distinct from typical global-precedence perception.

Skills it tends to define.

Pixel/line-level design QA & typography
CAD tolerance checks & metrology
Data validation, schema linting, and code review
Forensic image/audio analysis
Calendar/sequence algorithm work & rule mining

Professions that use it most (and why).

Semiconductor/layout, type design, forensic labs—microscopic precision matters.
Standards/compliance & dataset curation—detect subtle inconsistencies others miss.

Group 7 — Attention, Control & Cognitive Energy

Purpose of the group

Channel, sustain, and flexibly reallocate mental resources (focus, speed, inhibition, and “gear-shifting”) to match changing task demands.

Why it’s essential

Innovation work is noisy and non-linear. You need stamina to keep a goal online, brakes to suppress the obvious move, throttle to move quickly when the path is clear, and steering to switch strategies when it isn’t. These traits set your throughput and stability under pressure—key for shipping novel ideas at scale.

38) Set-Shifting Agility (Cognitive Flexibility)

Definition (from source). Ability to “maintain and flexibly alter cognitive representations in response to environmental demands”; dopaminergic (D2) striatal signaling supports flexible adaptation; set-shifting is dissociable from reversal learning and is impaired in Parkinson’s disease.

Gist (3 lines).
Switch rules without freezing.
Drop stale frames; adopt new constraints fast.
Neurochemistry (esp. dopamine/noradrenaline) tunes the “shift threshold.”

How unique it is / what’s unique. Markedly separates “adaptive strategists” from “perseverators.” Individuals differ in the cost of switching and the signal needed to pivot.

Skills it strongly defines

Rapid reframing in live problem-solving
Multi-strategy search (A/B/C quickly)
Contextual rule update in analytics and ops
Fault isolation when specs change
Real-time prioritization under new constraints

Professions that use it most & why

Product management / venture building — market signals force fast hypothesis pivots.
Incident response / SRE — shift playbooks as telemetry evolves.
Trading / risk — regime shifts require rule swaps.
UX research — adapt protocols mid-session.

39) Sustained Attention Endurance (Executive Attention)

Definition (from source). Executive attention maintains task goals, manages interference, and includes partially separable mechanisms for maintenance and disengagement when goals must be updated.

Gist (3 lines).
Hold the mission online for long stretches.
Resist lure stimuli and mind-wandering.
Release and re-lock when goals truly change.

How unique it is / what’s unique. Trait-like differences in how long people can keep goal representations active before interference erodes fidelity.

Skills it strongly defines

Long-form reading/comprehension sprints
Log/file triage without misses
Multi-hour bench or code runs
Meeting-to-making “goal carryover”
Accurate QA at end of long cycles

Professions that use it most & why

Data engineering / ETL — hours of careful, repetitive checks.
Bench science — protocol vigilance over time.
Editing / legal review — error detection in long documents.
FP&A — sustained reconciliation under deadlines.

40) Processing Speed (CHC Gs)

Definition (from source). In the CHC model, Gs (Processing Speed) is a broad second-stratum ability—speed/fluency of executing simple or overlearned cognitive operations (e.g., choice RT, clerical speed). Neuroscience links faster elementary operations and efficient white-matter to higher performance.

Gist (3 lines).
How fast your “mental clock” ticks on routine ops.
High Gs ≠ deep insight, but it raises throughput.
Amplifies teams when paired with accuracy.

How unique it is / what’s unique. Distinct from reasoning depth; produces a “many shots on goal” profile—short latencies, low idle time.

Skills it strongly defines

Rapid code/tests compile-fix cycles
High-throughput data labeling/cleaning
Fast UI wireframing iterations
Quick document triage and routing
Snappy dashboard investigations

Professions that use it most & why

Support engineering / ops — queue velocity matters.
Quant/back-office reconciliation — many small ops fast.
Design production — numerous micro-edits under time.
Medical scribe / radiology pre-reads — speed with patterns.

41) Impulsivity–Reflectivity (Kagan’s Cognitive Tempo)

Definition (from source). Under uncertainty, impulsive individuals respond quickly with more errors; reflective individuals delay, sample options, and commit fewer errors. Classically measured by the MFFT; the dimension shows stability across tasks.

Gist (3 lines).
Fast-but-riskier vs. slow-but-safer decision tempo.
Tempo shapes error profile and exploration depth.
Both ends can be assets if matched to task.

How unique it is / what’s unique. Clear bimodality in decision latency vs. accuracy. Not just personality—shows consistent cognitive signature.

Skills it strongly defines

First-strike prototyping vs. thorough scoping
Risk triage: when to ship vs. hold
Sampling depth before commit
Heuristic vs. exhaustive search
Error-tolerant sandboxing vs. precision gating

Professions that use it most & why

Early-stage founders / growth hackers (impulsive-decisive fits high-variance bets).
Safety-critical engineering / aviation / compliance (reflective tempo reduces costly errors).
Clinical diagnosis (balanced tempo: quick flags, careful confirmation).

42) Working-Memory Interference Control (Executive Attention / Gw facet)

Definition (from source). Executive attention constrains WM contents by resisting distraction and proactive interference; maintenance and disengagement are partially separable control mechanisms that support reasoning (Gf).

Gist (3 lines).
Keep the right items “on the mental workbench.”
Block old rules from contaminating the new.
Drop and refresh contents with minimal loss.

How unique it is / what’s unique. Big driver of real-world reasoning yield—people vary in how “sticky” irrelevant traces are.

Skills it strongly defines

Multi-constraint modeling without leakage
API/context switching with state integrity
Clean hypothesis updating (Bayesian feel)
Version control of assumptions in analysis
Complex meeting synthesis without drift

Professions that use it most & why

Machine learning / modeling — juggling features, constraints.
Strategy / policy analysis — counterfactuals without contamination.
Complex care coordination — many threads, high stakes.

43) Exploration–Exploitation Balance (Decision Control)

Definition (from source). Dopamine encodes reward prediction error and interacts with serotonin/noradrenaline in uncertain decisions; set-shifting/WM/creativity are interrelated and modulated by catecholamines—supporting adaptive exploration vs. exploitation.

Gist (3 lines).
When to try the weird thing vs. scale the sure thing.
Neuromodulators tune novelty-seeking and stick-with-it.
Distinct trait “set points” shape innovation style.

How unique it is / what’s unique. Individuals differ in baseline exploration drives and how quickly they “lock in” after small wins.

Skills it strongly defines

Portfolio search vs. double-down timing
Rapid cheap experiments before scale
Kill-criteria discipline after signal
Opportunity sizing under uncertainty
Roadmap pruning without losing option value

Professions that use it most & why

R&D leadership / venture capital — portfolio strategy is explore-exploit.
Growth / experimentation — traffic allocation decisions daily.
Bio/Deep-tech — expensive pivots demand calibrated exploration.

44) Cognitive-Load Management via Style

Definition (from source). Two foundational style dimensions—wholist–analytic and verbal–imagery—and their combinations influence decision making; people often compensate by recruiting the other style when a task mismatches their default, reducing load.

Gist (3 lines).
Know your native representation (words vs. images; parts vs. whole).
Match the task—or deliberately translate—to cut mental load.
Style combos predict decisiveness and information routing.

How unique it is / what’s unique. Distinct, relatively stable preferences in how information is represented and organized; combinatorial profiles (e.g., analytic-verbaliser vs. wholist-imager) behave differently under pressure.

Skills it strongly defines

Designing artifacts that fit your brain (maps vs. specs)
Fast “translation” (diagram⇄bullet list) to relieve overload
Choosing the right note-taking & dashboard forms
Meeting structuring (top-down vs. bottom-up)
Delegation that complements your style

Professions that use it most & why

Architecture / systems design — switching representation formats is core.
Consulting / comms — translate complex content for diverse styles.
Education / enablement — instructional design exploits style strengths.

Group 8 — Social-cognitive & communicative innovation

Purpose
Turn ideas into adoption by reading contexts, reframing audiences, translating across codes, and brokering paradigms—ethically.

Why it’s essential
Breakthroughs don’t move markets unless people buy in. Practical tacit know-how, wise trade-offs, and style-aware translation make novel ideas legible, persuasive, and scalable.

45) Tacit political knowledge (practical intelligence in organizations)

Definition (from source). Tacit knowledge is “what one needs to know in order to work effectively in an environment that one is not explicitly taught and that often is not even verbalized,” studied across dozens of occupations via job-embedded scenarios and “if–then” production rules.

Gist (3 lines).
• Reads unwritten rules, incentives, power and timing.
• Turns goals into feasible sequences that actually pass.
• Knows who/when/how—not only what.

How unique is it / what’s unique. Largely experience-derived and context-specific; separates brilliant ideas that stall from those that ship.

Skills it tends to define

Stakeholder mapping & coalition sequencing
“If–then” playbooks for approvals/escalations
Agenda framing and meeting choreography
Early warning via weak-signal networks
Navigating trade-offs without formal authority

Professions that use it most (and why).

General management / product leadership — aligning diverse actors to a plan.
Policy & corporate affairs — procedural know-how, windows of opportunity.
Sales & enterprise partnerships — multi-party, tacitly governed deals.

46) Audience reframing (Propulsion Type 2: Redefinition)

Definition (from source). Redefinition “attempts to redefine where the field is,” changing the perceived status quo without necessarily moving it forward—often crowd-defying when it challenges shared views.

Gist (3 lines).
• Shifts perception so the same facts land differently.
• Redistributes salience, stakes, and default comparisons.
• Sets up later motion by changing “what counts.”

How unique is it / what’s unique. Distinct persuasion style: not more data, but a new lens that relabels the baseline.

Skills it tends to define

Narrative reframing & category design
Baseline/metric re-definition (what “success” means)
Analogy selection that reroutes intuitions
Objection preemption via re-anchoring
Launch messaging that resets comparisons

Professions that use it most (and why).

Category-creating founders/CMOs — rename the game to win it.
Policy entrepreneurs — reframing problems to unlock coalitions.
Science comms — re-anchoring the public’s priors.

47) Mode translation & code-switching

Definition (from source). Style-aware translation strategies recast content into a mode that fits the learner/reader (diagram ↔ text; whole-view outlines ↔ analytical lists), alongside adaptation and load-reduction strategies.

Gist (3 lines).
• Converts ideas between word, picture, and structure codes.
• Reduces processing load mismatch for target audiences.
• Preserves content while changing form.

How unique is it / what’s unique. A repeatable, trainable bridge between verbaliser–imager and wholist–analytic preferences—key for influencing diverse stakeholders.

Skills it tends to define

Executive-summary ↔ diagram flips on demand
“Storyboard the spec” or “spec the storyboard”
Interface/doc design that matches cognitive load
Mixed-media communication plans (text + schema)
Meeting facilitation with live mode-switching

Professions that use it most (and why).

Consulting / enablement / sales engineering — tailor form to the room.
Product & UX — code-switch between PM, design, and engineering.
Education/learning design — deliberate translation to fit styles.

48) Social judgment / sagacity (Balance for the common good)

Definition (from source). Wisdom applies successful intelligence and creativity, mediated by values, to pursue a common good by balancing intrapersonal, interpersonal, and extrapersonal interests over short and long terms, and by balancing adaptation, shaping, and selection of environments.

Gist (3 lines).
• Sees multi-party interests and time horizons clearly.
• Chooses means/ends that sustain legitimacy.
• Guards against egocentric fallacies and groupthink.

How unique is it / what’s unique. Goes beyond social/emotional intelligence by explicitly balancing interests toward the common good; tacit knowledge is central.

Skills it tends to define

Multi-stakeholder decision charters
Guardrail ethics and “means matter” policies
Dialectical/dialogical facilitation (many viewpoints)
Time-horizon trade-off setting (short vs. long term)
Crisis choices balancing adapt/shape/select

Professions that use it most (and why).

Executive leadership & public service — legitimacy and durable buy-in.
University/governance boards — balancing constituencies over time.
Standards & safety bodies — common-good adjudication.

49) Paradigm-integration brokerage

Definition (from source). Integration “puts together ideas formerly seen as distinct or opposed,” merging paradigms into a single explanatory/practical frame.

Gist (3 lines).
• Finds a third model that reconciles warring camps.
• Creates shared language/metrics across tribes.
• Unlocks scale by ending paradigm trench wars.

How unique is it / what’s unique. Requires deep bilingualism in rival frames plus credibility with both sides—rare and high-leverage.

Skills it tends to define

Boundary object design (shared APIs, schemas, KPIs)
Cross-discipline ontology/lexicon work
“Both-and” roadmaps with win conditions
Consortium/standard formation & governance
Mediation techniques for technical disputes

Professions that use it most (and why).

Chief architects / CTOs — unify stacks and teams.
Standards alliances / open-source leads — integrate ecosystems.
Translational science / platform PMs — merge lab ↔ product frames.

50) Contextual adapt–shape–select strategy

Definition (from source). Practical intelligence deploys the same components to adapt to, shape, or select environments; people differ in their balance and competence across these options.

Gist (3 lines).
• Knows when to fit, when to push, when to move.
• Treats context as a design variable, not a fate.
• Turns misfit into strategy rather than friction.

How unique is it / what’s unique. Distinct situational calculus: not “one style fits all,” but meta-choice among three environmental moves.

Skills it tends to define

Environment scans → adapt/shape/select play
Change-management choreography
Exit/entry strategy and “optionality” design
Localizing rollouts vs. global standardization
Negotiating fit (policy, market, org constraints)

Professions that use it most (and why).

Market expansion / policy ops — decide fit vs. reform vs. relocate.
Org design / transformation — shape structures or select new venues.
Venture building — pivot logic as context shifts.

51) Style–team complementarity sense

Definition (from source). Wholist–analytic and verbaliser–imager styles combine; people often compensate by recruiting the other code when tasks mismatch, and style mixes relate to decisiveness and social/working preferences (e.g., wholists more group-oriented).

Gist (3 lines).
• Spots style gaps that slow teams (too wholist or too analytic).
• Pairs codes (diagram ↔ text) to cut load and indecision.
• Places people where their native mode speeds the group.

How unique is it / what’s unique. Explicit sensitivity to style interactions, not just abilities—predicts friction points and complements.

Skills it tends to define

Team composition by style matrix (WA × VI)
Role design that exploits each code (author, mapper, translator)
Decision process tuning (overview→detail vs. detail→overview)
Meeting/runbook formats matched to audience
Coaching to deploy compensation strategies

Professions that use it most (and why).

Program/portfolio management — orchestrate diverse styles for velocity.
Design-engineering-PM triads — continuous translation and pairing.
Learning & comms — craft artifacts that fit mixed-style populations.

Group 9 — Execution, Scaling & Operationalizing

Purpose. Turn promising ideas into reliable, repeatable, and scalable performance—without burning time, people, or quality.

Why it’s essential. Even world-class discovery and design fail without metacomponents that plan work, monitor reality, and correct course; memory systems that proceduralize steps; speed/throughput biases that keep flow high; self-regulation that delays impulses; analytic styles that surface errors; and the steady accrual of crystallized know-how to institutionalize what works.

60) Planning–Monitoring–Evaluation (Sternberg “Metacomponents”)

Definition (from source). In the triarchic theory, metacomponents are higher-order processes—planning, monitoring, and evaluating—that guide performance and learning, deciding what to do, how to do it, and whether to change course.

Gist (3 lines).
• Plan: set goals, sequence steps, allocate resources.
• Monitor: track progress vs. plan and detect mismatch.
• Evaluate: diagnose why, revise strategies, and redeploy.

How unique / what’s unique. People vary widely in how explicitly and continuously they run this loop; some naturally externalize it (checklists, dashboards), others keep it tacit. It’s a control system talent more than “getting things done.”

Skills it tends to define.

Program/roadmap design and decomposition
Milestone and risk instrumentation
Decision gates & go/no-go discipline
Post-mortems and feedback integration
Governance & change control

Professions that use it most (why).

Product/Program Managers — convert vision into executable, measurable slices.
Operations Leaders — enforce cadence and control limits in production.
Consultants (Ops/Strategy) — architect plans and evaluation frameworks.
Clinical Trial Managers — protocol planning and interim analyses.
Policy Analysts/Regulators — ex-ante criteria, monitoring, and review cycles.

61) Schema Automation (Proceduralization; Habit/Skill Systems)

Definition (from source). Procedural memory supports performance of actions and skills; with practice, goal-directed “actions” become habits routed via cortico-striatal loops (basal ganglia/striatum), chunking sequences into automatic units.

Gist (3 lines).
• Repeated sequences compress into fast, low-effort “macros.”
• Control shifts from deliberative to habit systems for throughput.
• Frees working memory for exceptions and judgment.

How unique / what’s unique. Strong proceduralizers reach stable, high-speed, low-variance execution; weak proceduralizers keep paying “cognitive tax” on routine steps.

Skills it tends to define.

SOP design & standard work creation
Kaizen/lean “single best way” codification
Training that moves from explicit to automatic
Human–machine task chunking (RPA, macros)
High-reliability handoffs & checklists

Professions that use it most (why).

Manufacturing/Process Engineers — turn tacit sequences into repeatable flow.
DevOps/SRE — automate runbooks; reduce toil and mean-time-to-recovery.
Surgeons/Anesthetists — proceduralize critical steps to cut error.
Airline/ATC Ops — habit-based checklists for safety under load.
Warehouse/Logistics Leads — standardize picks, routes, and scans.

62) Throughput-Optimization Bias (Gs × Systemizing)

Definition (from sources). CHC processing speed (Gs) captures speed/fluency of simple, over-learned operations; systemizing is the drive to analyze/build rule-based systems (“if-and-then” structures). Together they bias people toward maximizing flow and eliminating bottlenecks.

Gist (3 lines).
• Notice queueing, variance, and waste by default.
• Prefer formal rules, schemas, and deterministic pipelines.
• Pursue cycle-time and WIP reduction almost reflexively.

How unique / what’s unique. It’s not mere “speed”; it’s a taste for rule clarity + a brain that runs fast on familiar primitives—ideal for scaling stable systems.

Skills it tends to define.

Bottleneck discovery & Little’s Law intuition
Workflow formalization and constraint management
Metric design (throughput, WIP, lead time)
Deterministic API/contract thinking
Data pipeline and ETL reliability focus

Professions that use it most (why).

Operations Research / Industrial Engineering — formalize flow and constraints.
Platform/Infra Engineers — prefer reliable, rule-clean systems at scale.
Revenue Ops — pipeline health, handoff SLAs, and cycle-time cuts.
Supply-Chain Planners — queueing, buffers, and takt alignment.
Quality Engineers — reduce variance with rules and checks.

63) Tolerance for Delayed Gratification (Self-Regulatory Resource)

Definition (from source). Creative/innovative performance draws on personality–motivational resources like persistence and delayed gratification—the capacity to defer immediate rewards for long-term, uncertain payoffs.

Gist (3 lines).
• Choose compounding learning over immediate wins.
• Stick with slow feedback loops (science, platforms).
• Accept boredom and latency without derailing.

How unique / what’s unique. Many can plan; fewer will sustain dull stretches and ambiguous payoffs without quality decay.

Skills it tends to define.

Long-horizon project stamina (multi-quarter/year)
Versioning & incrementalism over “big bang”
Capital budgeting discipline
Deep practice/spaced retrieval adherence
Pre-mortem/patient iteration culture

Professions that use it most (why).

Fundamental Researchers — endure long cycles between hypothesis and result.
Platform/Product Infrastructure — invest now for future developer leverage.
Pharma/Biotech PMs — tolerate long, regulated pipelines.
Venture Builders — absorb uncertainty to reach product–market fit.
Education Tech/Policy — play multi-year outcome games.

64) Error-Taxonomy Instinct (Analytic Style)

Definition (from sources). On the wholist–analytic dimension, analytics habitually parse information into parts, check consistency, and prefer sequential, detail-focused strategies—an advantage for error detection and classification.

Gist (3 lines).
• Spot incongruities and boundary cases.
• Build taxonomies and escalation criteria.
• Default to “first diagnose, then fix.”

How unique / what’s unique. Genuine “splitters” see kinds of wrong and route them differently—core to quality systems.

Skills it tends to define.

Defect classification & Pareto logic
Root-cause analysis and containment (8D/5-Why)
Control charts & triggers for intervention
Playbook branching and severity matrices
Auditability & evidence trails

Professions that use it most (why).

QA/Validation Leads — live on taxonomies and severity routing.
Security Engineers/Analysts — classify threats and triage precisely.
Clinical QA/Regulatory Affairs — enumerate deviations and CAPAs.
Forensic Accountants/Auditors — categorize anomalies systematically.
Customer Support Ops — error codes to resolution pathways.

65) Knowledge Crystallization (Gc Growth & Institutionalization)

Definition (from sources). CHC crystallized intelligence (Gc): knowledge and verbal concepts acquired through experience (vocabulary, general information), distinct from fluid reasoning; it grows with domain exposure and becomes the substrate for faster, better execution.

Gist (3 lines).
• Transform hard-won lessons into shared language and heuristics.
• Raise team “base rate” accuracy and speed via stored know-how.
• Enable onboarding and scale through articulation.

How unique / what’s unique. Some people have a knack for codifying tacit wins into reusable concepts, checklists, and exemplars that lift the median.

Skills it tends to define.

SOPs, playbooks, and decision trees that actually get used
Naming patterns/anti-patterns; glossary creation
Post-mortem synthesis into rules of thumb
Training design that builds Gc (worked examples, contrasts)
Knowledge graphing/ontology light-weighting

Professions that use it most (why).

Enablement/Instructional Designers — convert tacit expertise to teachable chunks.
Knowledge Managers/Technical Writers — maintain the org’s Gc backbone.
Sales/CS Ops — playbooks and objection libraries that compound wins.
Safety Officers — capture lessons-learned into standard practice.
Data Governance Leads — define terms, lineage, and usage norms.

Group 10 — Decision architecture, values & risk

Purpose
Decide what to pursue, why, and when—by calibrating risk, balancing stakeholders and time horizons, and choosing whether to adapt to, shape, or exit environments.

Why it’s essential
In innovation, value is often destroyed by the wrong bets and the wrong ends. Wise decision patterns—grounded in explicit values, calibrated risk, and field-aware judgment—separate durable breakthroughs from flashy cul-de-sacs.

66) Wise reasoning (Balance Theory of Wisdom)

Definition (from source). Wisdom is applying successful intelligence and creativity, mediated by values, toward a common good, balancing intrapersonal, interpersonal, and extrapersonal interests over short and long terms, and balancing adaptation, shaping, and selection of environments.

Gist (3 lines).
• Makes choices that hold up across stakeholders and time.
• Uses adapt–shape–select deliberately (context is a design variable).
• Aims at common good, not just personal win.

How unique is it / what’s unique. Distinct from social/emotional intelligence: explicitly balances competing interests and time horizons in service of a common good.

Skills it tends to define.

Stakeholder maps that include self/others/society
Decision charters with short–long horizon trade-offs
Choosing adapt vs. shape vs. select moves
Guardrail policies (“means matter”)
Dialectical reviews that surface value conflicts

Professions that use it most (and why).

Executive leadership / founders — legitimacy and durable buy-in hinge on balancing interests.
Public policy / regulators — common-good adjudication across horizons.
Standards & safety boards — encode values into protocols.

67) Risk calibration for creative leadership (Investment & Propulsion lenses)

Definition (from source). The investment theory frames creativity as a decision to “buy low and sell high in the world of ideas,” i.e., pursue unpopular ideas, persuade the field, then move on; propulsion types also differ in how crowd-defying they are.

Gist (3 lines).
• Tunes how contrarian your bets are and how long you hold them.
• Distinguishes sensible non-consensus from reckless novelty.
• Couples idea quality with persuasion and timing.

How unique is it / what’s unique. Not “risk-seeking” per se—risk-calibrated contrarianism tied to field dynamics and adoption curves.

Skills it tends to define.

Non-consensus thesis formation & evidence ladders
Sequencing milestones to de-risk adoption
Go-to-market for unpopular-now ideas
Kill/scale rules keyed to field readiness
Portfolio mix of crowd-led vs. crowd-defying bets

Professions that use it most (and why).

Venture capital / venture studio — price non-consensus correctly.
CPO/CTO strategy — stage contrarian platform moves.
Frontier R&D leads — time visibility vs. proof for uptake.

68) Integrative complexity (cognitive complexity)

Definition (from source). Classic style literature distinguishes cognitive complexity / integrative complexity and related constructs (e.g., conceptual integration), i.e., differentiating multiple perspectives and integrating them into a coherent whole.

Gist (3 lines).
• Holds conflicting models without collapse.
• Differentiates before integrating—then reconciles constraints.
• Produces decisions that survive many lenses.

How unique is it / what’s unique. High differentiate-then-integrate capacity: avoids premature closure and simplistic averaging; distinct from mere verbosity.

Skills it tends to define.

Multi-criteria decision analysis that really weighs trade-offs
“Both-and” synthesis (reconcile performance vs. safety, growth vs. trust)
Writing decision memos with rival models & convergence
Scenario trees that preserve conditional structure
Conflict turning into design constraints, not stalemates

Professions that use it most (and why).

Chief architects / systems PMs — unify competing requirements.
Policy designers / negotiators — integrate cross-party constraints.
Clinical / safety leadership — reconcile efficacy vs. risk.

69) Paradigm acceptance–rejection set-point (Propulsion spectrum)

Definition (from source). Propulsion theory specifies contribution types from replication and forward/advance incrementation to redirection, reconstruction, reinitiation, and integration, with varying degrees of crowd-defiance.

Gist (3 lines).
• Your default is measurable: preserve, extend, redirect, restart, or integrate.
• Predicts where you’ll push a field and how hard.
• Team mix across types reduces strategy myopia.

How unique is it / what’s unique. A field-movement trait: not just idea quality but how you try to move the field—and whether that aligns with timing.

Skills it tends to define.

Roadmaps tuned to your propulsion bias
Evidence and coalition suited to type (e.g., integration → standards)
Anticipating resistance patterns by type
Hiring complements (replicators + redirectors)
Governance matching motion (e.g., RFCs for integration; pilots for redirection)

Professions that use it most (and why).

Research leadership — choose motion type per domain maturity.
Standards/consortia — integrate paradigms credibly.
Editorial/PM leaders — pace the field, not just the team.

70) Ethical foresight & guardrails (wisdom applied)

Definition (from source). Within wisdom, one balances interests in service of a common good, explicitly infusing values and choosing among adapt/shape/select responses.

Gist (3 lines).
• Anticipates downstream externalities before they bite.
• Builds “means matter” constraints into strategy.
• Sustains legitimacy so adoption sticks.

How unique is it / what’s unique. Goes beyond compliance: value-aware prediction of harms/benefits and pre-committed guardrails that survive pressure.

Skills it tends to define.

Red-teaming for societal harms (not just product risks)
Policy-by-design (privacy, safety, fairness)
Long-horizon consequence mapping (public, ecosystem)
Oversight mechanisms with teeth (stoppers, audits)
Communicating trade-offs transparently

Professions that use it most (and why).

Chief ethics / safety officers — encode foresight into process.
Public sector & standards leaders — translate values into enforceables.
Health/biotech PMs — high-stakes externalities need pre-commitment.

71) Cultural reframing facility (adapt–shape–select across cultures)

Definition (from source). Wisdom/practical intelligence involve adapting to, shaping, or selecting environments.

Gist (3 lines).
• Reads cultural priors; picks the right move (fit, reform, or relocate).
• Localizes frames without diluting core values.
• Builds bridges across meaning systems.

How unique is it / what’s unique. Less “communication skill,” more context calculus: knows which cultural move changes feasibility.

Skills it tends to define.

Market/policy localization without mission drift
Narrative/metric translation for different cultures
Coalition sequencing by cultural readiness
Selecting where to pilot vs. standardize
Hiring/partnering that supplies cultural “keys”

Professions that use it most (and why).

Global PMs / market expansion — adapt/shape/select by locale.
Diplomacy / public affairs — reframe issues to local values.
Translational science & health — culturally legible programs.

72) Crowd-defying endurance (investment conviction)

Definition (from source). In the investment theory, creators “buy low” (pursue unpopular ideas), persuade, then move on; many propulsion types are crowd-defying during their adoption slog.

Gist (3 lines).
• Stays with a non-consensus thesis through skepticism.
• Converts evidence into allies without burning trust.
• Knows when to exit after uptake to avoid lock-in.

How unique is it / what’s unique. Combines motivational stamina with field sense; not stubbornness—evidence-conditioned persistence.

Skills it tends to define.

Persuasion under uncertainty (progressive proof)
Community/standard building for new categories
Evangelism that recruits skeptics, not just fans
Exit timing (sell high; move on)
Portfolioing multiple long-slog wedges

Professions that use it most (and why).

Category-creating founders/CMOs — build belief over time.
Open-source / standards leaders — shepherd adoption s-curves.
Scientific PIs in frontier areas — evidence-first persuasion over years.

A guest post by

Jakub Bareš

Chief Strategist at Metamatics Organization metamatics.org and Head of Research at Intelligence Strategy Research Institute intelligencestrategy.org I am former CTO with experience from 9 startups, 7 accelerators and 25+ ML/NLP/GNN projects