Concepts
The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types
Track every urgent-feeling demand for a week — your false…
Run a daily urgency log for one week, recording every urgent-feeling demand with timestamp, then scoring each on actual time-sensitivity and impact-if-delayed-two-hours to build calibration data on false urgency rates.
After an information fast, sort inputs
After each information fast, document three specific categories: inputs you genuinely missed, inputs you craved but didn't need, and inputs you forgot existed, then eliminate or downgrade items in the latter two categories.
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.
Ask for behavioral feedback, not character evaluations
Frame feedback requests as specific behavioral questions ('What do I consistently do that I probably don't realize?') rather than character evaluations to keep feedback at task level instead of identity level.
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.
Document expectation vs. reality in cross-cultural…
When you experience confusion, friction, or judgment in a cross-cultural interaction, document three elements before reacting: (1) what you expected, (2) what actually happened, (3) what cultural assumption might explain the gap—treating the collision as diagnostic data about invisible defaults.
Write your position before group discussion — compare…
Before committing to a private written position for any group decision, externalize your reasoning and conclusion before the group discussion begins, then compare it to your post-discussion position to detect social influence effects.
After labeling an emotion, trace the cause then ask what…
After labeling each emotion, write one sentence identifying what is generating it using causal language ('because'), then check for emotional layers by asking 'What is underneath this?' to surface masking dynamics.
Use structured labeling instead of narrative venting…
When externalizing emotions, avoid narrative venting ('he did this and then that happened') and instead use structured labeling ('I feel X because Y') to convert fusion into defusion.
Let AI find patterns across journal entries — label…
Use AI to analyze patterns across multiple emotional externalization entries (recurring emotions, triggers, trends) rather than to label emotions for you, because the regulatory benefit comes from the act of labeling, not from being labeled.
Capture feedback within 60 minutes in five structured…
Capture feedback within 60 minutes of receiving it using structured fields (date, source, verbatim content, emotional reaction, specific behavior) before memory reconstruction distorts the signal.
Cross-reference domains in weekly review
During weekly reviews, cross-reference externalized domains to detect contradictions—compare stated priorities against time allocation, goals against commitments, assumptions against failure analyses—because isolated review of each domain misses the conflicts that degrade decision quality.
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.
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.
When gut and analysis disagree, investigate for 30 minutes…
When formal and intuitive schemas disagree on a decision, investigate the disagreement for thirty minutes rather than defaulting to either—write what your gut is reacting to and test whether it reveals a pattern your formal criteria missed or a bias you haven't examined.
Irritation at alternative categorizations signals you've…
When someone proposes a different categorization and your first reaction is irritation that they are 'wrong,' treat this as a signal that you have mistaken a constructed category for an objective feature of reality.
Classification friction reveals missing categories — map…
When items consistently resist classification in your system (you hesitate, force-fit, or leave uncategorized), map what those resistant items have in common to diagnose missing categories that represent dimensions you care about but haven't encoded.
Audit categories against your values — missing values…
For each top-level category in your knowledge system, write one sentence explaining what value that category protects or promotes, then identify missing categories that would operationalize values you hold but aren't currently encoding.
Defensiveness about testing a schema IS the signal…
When a schema triggers defensiveness at the suggestion of testing it, treat that emotional response as a diagnostic signal of high psychological investment requiring especially rigorous validation.
Convert 'I am X' identity claims into 'In situation Y, I…
Rewrite personal identity schemas as behavioral predictions with specified conditions and thresholds to convert unfalsifiable identity claims into testable hypotheses.
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.
Include external ratings alongside self-assessment…
When validating schemas about personal capability or performance, include external observer ratings alongside self-assessment to detect systematic overconfidence blind spots that introspection cannot reveal.