Concepts
The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types
Advance observation practice when the trigger becomes…
When practicing observation at any difficulty level, stay at that level until the trigger becomes boring rather than advancing on a fixed schedule, because boredom signals that automatic judgment has been replaced by automatic observation at that complexity level.
Keep observations and conclusions in separate visible…
When conducting week-long observation audits of high-stakes domains, structure daily entries with physically separated sections for 'what I observed' and 'what my mind wanted to conclude,' keeping both sections visible simultaneously to train recognition of the observation-evaluation gap.
Three occurrences in 30 days = pattern candidate…
When a behavior, reaction, or outcome recurs three or more times across a 30-day period, classify it as a pattern candidate requiring structural analysis rather than treating it as coincidence.
Name patterns in 2-4 words that compress…
Name behavioral patterns using 2-4 word descriptors that compress the full trigger-response sequence into a recognizable label, enabling real-time pattern recognition under cognitive load.
Document every pattern with three fields
For each named pattern in your Pattern Dictionary, document three required fields: the pattern name, its observable trigger conditions, and the default behavioral response it produces.
Every meta-pattern claim must ground in at least three…
When identifying meta-patterns, require each second-order claim to ground in at least three documented first-order pattern instances to distinguish genuine meta-patterns from intellectual speculation.
When 3+ patterns share formation dynamics, change…
Allocate pattern-change effort to second-order interventions (changing how patterns form) over first-order fixes (changing individual patterns) when three or more first-order patterns share formation or dissolution characteristics.
Read all notes without editing first, then extract themes…
When reviewing notes for patterns, read through all entries without editing or organizing first, then extract recurring themes on a separate page, to prevent premature categorization from filtering out emergent structures.
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.
After 15-20 judgments, check if your errors are bias…
After accumulating 15-20 judgments in the same domain, analyze whether errors cluster directionally (bias requiring correction factor) or scatter randomly (noise requiring aggregation).
Keep a two-week bias journal to build your personal error…
Conduct a two-week bias journal recording significant judgments with confidence levels, then categorize errors by direction and type to build your personal bias profile.
Write a specific pre-correction procedure for each known…
For each identified bias in your profile, write a specific pre-correction question or procedure to execute before acting on judgments in that domain.
After four weeks, check if your beliefs under-react…
After four weeks of belief tracking, examine whether beliefs barely moved despite evidence (conservatism) or swung dramatically on single data points (base rate neglect) to identify domain-specific updating patterns.
Run a five-question context scan before interpreting…
Before interpreting any piece of information—a message, a metric, a statement, a data point—run a five-question context scan: What environment am I in? What role am I occupying? What just happened that might color my perception? What are the goals (mine and others')? What assumptions am I importing from a different context?
If you deliberated for 60+ seconds, the reasoning is worth…
Set the threshold for decision context documentation at any choice where you deliberated between options for more than sixty seconds, because if you considered alternatives consciously, the reasoning is worth preserving against memory reconstruction.
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.
If writing your reasoning produced zero surprises…
When a reasoning chain contains no surprises or pauses during construction—no moments where the next link was weaker than expected—you have transcribed conclusions rather than constructed reasoning and should restart with genuine step-by-step building.
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.
Inventory what you actually do — not what you should…
Before attempting to design better schemas, inventory your current operating schemas by writing what you actually do (not what you should do) across professional, relational, and self-concept domains.
Five-step schema audit
Perform schema inspection through a five-step audit: (1) list actual operating rules in a domain, (2) source each rule's origin, (3) identify one success and one failure case per rule, (4) rate confidence in each rule, (5) compare confidence to evidence quality.
Write your schemas first, then ask AI to extract…
When using AI to assist schema inspection, first externalize your thinking in writing, then request assumption extraction, pattern detection across entries, or adversarial questioning—because AI can only inspect articulated schemas, not internal ones.
Analyze validation records as a set — recurring failure…
After accumulating multiple validation records, analyze them as a set to identify recurring patterns in how your schemas fail—same blind spot, same overconfidence direction, same boundary condition—that no single test reveals.
Match review frequency to volatility
Schedule schema reviews at cadences matched to environmental volatility: weekly/biweekly for high-change contexts, monthly/quarterly for stable contexts, plus triggered reviews when surprises occur.
Surprise = automatic schema review — do not wait…
Treat surprising outcomes as automatic triggers for schema review rather than waiting for scheduled validation cycles, as surprise signals that at least one schema in your stack has drifted from reality.