Concepts
The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types
Habits as Context-Response Associations
Habits form when repeated behavior in stable contexts produces rewards, creating automatic context-response associations that trigger behavior without conscious deliberation.
Neural Plasticity Enables Lifelong Automatic Learning
Adult human brains retain the capacity to reorganize neural pathways throughout life in response to changes in input and behavior, automatically detecting and extracting statistical regularities from repeated input without conscious instruction.
Perceptual Plasticity Through Training
Experience and cultural training shape perceptual processing itself at preconscious levels, enabling the system to make distinctions it previously could not make and causing people from different backgrounds to literally perceive different features of objectively identical stimuli.
Environmental Installation of Early Schemas
Early cognitive schemas are installed by environmental input during critical developmental periods rather than being self-generated or innately specified in detail.
Mark every hesitation and vague word while writing…
When attempting to write an explanation of something you believe you understand, mark every sentence where you hesitate, use vague language, or skip a step as diagnostic evidence of incomplete understanding.
Writing stalls on understood topics are knowledge gaps…
When writing stalls on a supposedly understood topic, treat the stall point as a specific learning target rather than a writing problem.
Map the whole idea first, then decompose — knowledge gaps…
Before attempting decomposition of any complex idea, map it as a whole with your current understanding externalized, then decompose systematically until you encounter steps you cannot explain clearly—those uncertainty points are your actual knowledge gaps.
Link partial answers to the question — never replace…
When a question receives a partial answer, preserve the original question as a persistent atom and link the answer to it rather than replacing the question, creating a visible record of how understanding evolves from open inquiry to accumulated evidence.
For each surprise, write
For each captured surprise, write one sentence answering 'What did I apparently believe that turned out to be wrong?' to convert observations into explicit model gaps.
Capture small mundane surprises — they reveal systematic…
Capture small, mundane surprises rather than filtering for 'important' ones, because small surprises reveal systematic blind spots that large surprises obscure.
One source at full depth per week beats ten at surface…
Process one information source at full depth (notes, connections, could-explain-to-others) per week rather than ten sources at surface level to build signal detection capacity in your critical domains.
Test understanding by generating three examples…
Before claiming to understand a complex topic, generate at least three concrete examples from different domains that instantiate the concept, and if you cannot produce varied examples, treat this as evidence that you have acquired vocabulary without understanding.
Write 'This connects to [X] because [Y]' after consuming…
After consuming any piece of information, write one connecting sentence that relates it to existing knowledge using the structure 'This connects to [X] because [Y]'; if you cannot write this sentence within two minutes, classify the content as non-compounding noise regardless of its intrinsic quality.
Document what you have learned to ignore — not just what…
During expertise development, explicitly document both what you now prioritize and what you have learned to ignore, as conscious articulation of ignored features is the operational test of signal efficiency.
Domain facts need aggressive updating; structural…
Distinguish domain-specific facts (treatment protocols, software frameworks, market conditions) requiring aggressive temporal updating from structural principles (logic, mathematics, core psychological mechanisms) where age indicates Lindy-tested robustness, applying opposite update strategies to each type.
When recall fails, mentally reinstate the encoding context…
When recall of studied material fails, mentally reinstate the original encoding context—room, time of day, task being done, emotional state—before concluding the information wasn't learned or needs re-studying.
Structure learning notes as: claim, evidence, connection…
Write learning in the structure: claim (one sentence, your words), evidence (why believe it), connection (how it relates), question (what's unresolved) to force generation rather than transcription.
Write your explanation before asking AI — generation…
When using AI for learning, write your own explanation first, then use AI interrogation to find gaps, then revise—never let AI write the initial explanation because reading AI output does not produce the generation effect.
Classify failures into three types before analysis…
Categorize each failure as preventable (process deviation), complex (novel factor interaction), or intelligent (frontier experiment) before analysis, because different failure types require different questions.
Test: can you reconstruct the reasoning without the AI?
Test AI integration by verifying whether interactions increase your independent understanding—if you cannot reconstruct the reasoning without the AI, the tool is replacing cognition rather than extending it.
Write belief updates as 'Based on [evidence], I am…
When you update a belief, write an explicit update statement in the format 'Based on [specific evidence], I am updating my model from [old version] to [new version]' to reframe revision as calibration rather than defeat.
Map prerequisite chains backward before learning — ask…
Before attempting to learn a target skill, map its prerequisite chain backward by repeatedly asking 'what must I be able to do first?' until reaching skills you can perform reliably, then start at the lowest-rated prerequisite rather than the target.
If you can only say 'they go together' but not HOW one…
For each candidate enabling relationship, articulate the specific mechanism through which one condition creates another; if you can only state correlation ('they go together') rather than mechanism, treat it as association not enabling.
Ground every abstraction with 3+ examples from different…
Connect each abstract concept in your knowledge system to at least three concrete examples from different domains, because single examples invite surface-feature overgeneralization while multiple examples force attention to shared structural patterns.