Reusable cognitive primitives
Bayesian updating
A belief's strength should be revised by combining how likely it was beforehand (the prior) with how strongly new evidence bears on it.
- Thomas Bayes — Formulated · 1763
- Pierre-Simon Laplace — Formalized · 1774
Bayes stated the theorem (published posthumously in 1763); Laplace independently developed and generalised it into a systematic method of inference.
Sources: MacTutor History of Mathematics Archive, Encyclopaedia Britannica
Where it appears
Dependencies & synergies
Derived from the graph’s real structure — 3 concepts across 5 disciplines carry this pattern. Every figure is a count, not a score.
How many concepts carrying this pattern are seen through each discipline.
Other thinking patterns that recur on the same concepts — the more shared concepts, the more often they co-occur.
What kinds of relationships the carrying concepts form — the relational signature of the pattern.
Concepts where this pattern does the most cross-disciplinary work — each reaches disciplines beyond its own.
- Risk assessmentreachesEconomicsEthicsFinanceMedicineMicrobiologyPublic PolicyStatistics
- Infectious dosereachesMedicinePharmacyToxicology
- PrevalencereachesMedicineMicrobiology
Examples across disciplines
Reading a positive test through the pre-test probability, not sensitivity alone.
The same lab result implies different things at high versus low prevalence.
A classifier's posterior combines a prior with the likelihood of the data.
Weighing a new piece of evidence against the prior probability of the claim.
How this pattern travels
Interpreted from where the pattern recurs in the atlas — structural transfer and coverage, not a claim it is universally the "best" model.
Recurs across 5 disciplines over 3 carrier concepts — concentrated (67% of carriers in one field).
structural · Structural recurrence in the atlas — a pattern is a reasoning lens, not a law.
2 of its 3 carrier concepts are themselves cross-disciplinary connectors.
structural · Structural recurrence in the atlas — a pattern is a reasoning lens, not a law.
Explicit "where it breaks" notes exist for 0 of 3 annotated assignments.
curated · Curated boundary annotations — absence is a representation gap, not evidence the model has no limits.
Currently dark in the atlas: no assignment cites a source · no boundary annotations.
atlas representation · Describes the current Thinking OS representation, not the model itself.
The statistical fingerprint
How the 3 concepts that exercise this pattern distribute — from the graph, not a ranking.
Disciplinary fingerprint
Carrier concepts under each illuminating lens.
How settled its carriers are
Epistemic status of the concepts that exercise this pattern.
Technical detail
Formally, Bayes' theorem gives P(H|E) = P(E|H)·P(H) / P(E): the posterior probability of a hypothesis equals its prior times the likelihood of the evidence, normalised. In odds form, posterior odds = prior odds × likelihood ratio. The practical lesson is that a test result must be weighed against the base rate — in a low-prevalence setting even a highly specific test yields mostly false positives.
✕ A positive test means the condition is present. Its meaning depends on the prior — in a low-prevalence setting most positives can be false.
You have seen this model in one place. Where else could it apply — and where would the analogy break?
Infectious dose →A result doesn't speak alone — weigh it against how common the thing was to begin with. The same positive test means different things in a high- versus low-prevalence setting.
- Recurs across 5 disciplines.
- 7 concepts exercise this thinking pattern.
Derived from the graph — a pattern is a reasoning lens, not a law.
Before trusting this result, what was the prior chance — and how much does this evidence actually shift it, not just 'is it positive'?
Keep this question handy when you meet something new — it helps you notice the pattern, not just name it.