The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types and 2 molecules
Natural frequency representations of statistical information produce dramatically better Bayesian reasoning than conditional probability representations because frequency tracking matches evolutionarily older cognitive mechanisms.
When a belief revises three or more times in a short period without converging, treat this as a diagnostic signal that you are reacting to surface events rather than updating a deeper model.
When new evidence arrives, classify it by diagnostic value before updating—ask whether you'd see this evidence regardless of belief truth versus only if belief were true/false.
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.
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.