The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types and 2 molecules
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.
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.
When a judgment forms during an interaction, immediately convert it into a genuine question by replacing the evaluative conclusion with an inquiry about constraints, context, or reasoning ('Why would anyone write it this way?' becomes 'What constraints or context led to this approach?'), because questions activate exploratory cognition while conclusions activate confirmatory cognition.
In code reviews or technical evaluations, frame feedback requests as requests for reasoning rather than requests for justification—ask 'What was your reasoning?' instead of 'Why did you do it this way?'—because the first activates analytical explanation while the second activates defensive explanation.
When building new observation skills, construct a five-level difficulty hierarchy ranging from trivial annoyances to identity-level triggers, then spend minimum one week at each level before progressing, because attempting high-stakes observation without low-stakes mastery produces reversion to automatic judgment under pressure.
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trigger becomes boring rather than advancing on a fixed schedule, because boredom signals that automatic judgment has been replaced by automatic observation at that complexity level.
When using AI to practice observation skills, provide it with your written accounts of charged situations and explicitly request separation of observational statements from evaluative statements, using the AI's output as immediate feedback on which judgments you embedded without noticing.
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.
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.
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.
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 the same symptom triad precedes system failures across three independent incidents, document it as a named detection pattern and build an automated alert triggered by that specific combination.
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.
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.
Do not expect pattern recognition alone to eliminate the pattern—track the ratio of pattern-following to pattern-breaking instances over weeks rather than demanding immediate control, because automaticity requires repeated override practice to weaken.
Validate cross-domain pattern candidates by verifying that the relational structure (not surface similarity) matches across domains—two patterns share structure when the causal relationships between elements are preserved even when the elements themselves differ completely.
Before optimizing around a perceived positive pattern, verify through deliberate removal tests whether the pattern persists when suspected causal factors are absent.
When a keystone habit produces cascading benefits, protect its trigger conditions and structural enablers rather than optimizing the habit's internal execution.
Before building optimization systems around a personal correlation, test whether the correlation survives when you control for potential confounding variables through deliberate experimental variation.
When a pattern appears to reverse across subgroups in your data, disaggregate by relevant context variables (sleep, stress, social setting) before drawing conclusions from the aggregate pattern.
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.
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.
Pre-load support structures during the phase before a known cyclical trough rather than attempting to maintain behavior through willpower during the trough itself.
When multiple relationships produce the same tension pattern despite different people, map your own contribution to the dynamic before attributing the pattern to others' behavior.