Concepts
The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types
Egocentric Anchoring in Perspective-Taking
Humans exhibit egocentric anchoring when modeling others' perspectives, beginning from their own viewpoint and adjusting insufficiently to account for differing knowledge, beliefs, or perceptual access.
Automatic Pattern Perception
The human brain automatically generates and perceives patterns, relationships, and regularities in sensory input prior to and often independent of conscious verification, subject to systematic biases such as confirmation bias that preferentially encode pattern-consistent information while filtering contradictory evidence.
Feeling of 'thorough consideration' is a warning signal…
When you feel you have 'thoroughly considered' a decision, treat that feeling as a warning signal requiring additional externalized inventory, because WYSIATI (What You See Is All There Is) creates confidence from narrative coherence rather than completeness.
Document 2-3 pre-mortem failure modes with every…
For significant decisions, add a fifth field documenting pre-mortem risks (2-3 specific ways the decision could fail) to preserve concerns that hindsight bias will erase.
Read logs for five minutes before proposing theories…
Before acting on snap judgments during debugging or incident response, read system logs and dashboards for five minutes without proposing theories to prevent hypothesis anchoring from corrupting observation.
Search logs for evidence that falsifies your hypothesis…
When debugging with strong initial hypotheses about root cause, deliberately search logs for evidence that would falsify the hypothesis rather than confirm it, to counteract confirmation bias in data collection.
Before committing to a bug hypothesis, write
Before committing to a hypothesis about a bug's cause, write one sentence completing 'What would I expect to see if I were wrong?' then specifically search for that evidence before continuing the investigation.
High confidence is a trigger for more scrutiny, not less…
When confidence in a technical conclusion exceeds 8/10, treat that high confidence as a trigger to increase scrutiny and deliberately search for disconfirming evidence rather than reducing verification effort.
If the same judgment appears in three contexts…
When you encounter the same judgment arising across three or more different contexts, treat it as a structural cognitive habit requiring explicit examination rather than a series of independent assessments, because cross-context repetition indicates the judgment is executing from pattern-matching rather than situation-specific analysis.
Mark habitual judgments after they fire — you cannot…
For habitual judgments that fire too quickly to intercept before they execute, implement a marking protocol where you log each occurrence with 'HJ' notation immediately after noticing it, because marking converts the judgment from invisible background process to observable object without requiring the neurologically impossible task of preventing automatic activation.
Apply the substitution test to check if a judgment…
When noticing a habitual judgment about a person or group, apply the substitution test by asking whether you would make the same evaluation if a different person or group were in the identical situation—if the answer is uncertain or negative, you have detected a judgment running on identity rather than behavior.
Apply the camera test to your data before letting AI…
Before using AI for pattern analysis on observational data, ensure your input consists of descriptive observations rather than evaluative conclusions by applying the camera test to each input statement, because AI analyzing your conclusions produces confirmation of your biases rather than structural insights.
Log suspected patterns with dates before concluding…
Record each occurrence of a suspected pattern with date and context in a dedicated log before drawing conclusions, because memory-based frequency assessment systematically overestimates recurrence through the frequency illusion.
When expert intuition locks on, ask
When expert pattern recognition locks onto a familiar solution, force consideration of alternatives by asking 'I see X—but what if it is not X? What would I expect if it were Y?' before committing.
When angry, seek disconfirming evidence — anger inflates…
When angry, deliberately seek disconfirming evidence and independent risk assessments, as anger systematically inflates certainty, deflates risk perception, and increases risk-seeking behavior.
Under stress, ask AI what you are not seeing…
When using AI during high stress, prompt with 'I am stressed and may be experiencing tunnel vision—what am I likely not seeing?' rather than 'prove my interpretation is right.'
Check whether you are estimating frequency or vividness
When making frequency or probability estimates, pause and ask: 'Am I estimating actual frequency, or how easily I can picture this?' then look up the base rate before deciding.
Before big decisions, separate the recent trigger…
Before any consequential decision, populate two mandatory fields: the recent event driving current feeling, and the base rate/historical trend across the full relevant time window.
When a vivid case drives certainty, ask for the base rate…
When a vivid individual case makes you feel certain about probability, explicitly ask 'what is the actual frequency of this event in the relevant population?' before forming any judgment.
Frame pre-mortems as explanations of failure…
Frame pre-mortem prompts as 'It is [future date]. This has failed completely. Write why.' rather than 'What could go wrong?' to shift cognition from speculation to explanation.
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).
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.
Apply your documented error factor to timeline estimates…
After identifying that you are systematically overconfident on timelines by X%, multiply your initial timeline estimates by (1 + X/100) before stating them publicly.
Record five elements at the moment of every significant…
Record decision context at the moment of commitment using five elements: (1) decision statement, (2) forces/constraints/emotions active at choice point, (3) expected consequences with timeline, (4) confidence level 1-10, (5) review trigger date—before hindsight bias can rewrite your reasoning.