u/tractorboynyc

Predictive processing tells us the brain minimizes prediction error weighted by precision. The brain assigns high precision to error signals it can verify (a dropped ball, an oversalted dish) and low precision to error signals it can't (a meditation session, a ritual outcome). High precision means the model updates; low precision means it doesn't.

Cultural evolution has a structurally similar story at the population scale. The Price equation decomposes trait change into selection (pushing toward fitness) and transmission (eroding it with copying error). El Mouden et al. 2014 applied this to cultural traits explicitly. What hasn't been worked out as cleanly is what governs the selection term — what determines whether the population-level selection coefficient is large or small for a given cultural trait.

The proposal I've been developing: observability does the same work at the population scale that precision weighting does at the cognitive scale. High observability — content with a stable referent in the world, perceptual access to that referent, error detectability, correction opportunity, and institutional correction authority — keeps the cultural-Price selection coefficient large. Low observability collapses it, and the trait drifts under transmission error.

Some empirical fingerprints that look consistent with this:

41 cultural-knowledge domains scored on observability vs. accuracy: Spearman r = 0.527, blind-rater r = 0.893 (raters with no exposure to the accuracy data reproduced the same gradient).

Aboriginal Australian, Native Californian, and West African fire-management practices independently converged on near-identical parameters (timing, intensity, mosaic pattern). Fisher's combined test p = 0.007. Three traditions with no contact, same answer.

Andean potato farmers' Pleiades-visibility method for predicting El Niño rainfall: original Orlove et al. 2000 reported r = 0.577 across 8 years. A 25-year prospective replication on data the original authors never saw: r = 0.788.

Curious what people here make of the cross-scale claim. The math of precision weighting and the math of the Price equation aren't identical, but the structural role of the "weight on the error signal" feels parallel. Is there literature I should be reading on this that isn't El Mouden 2014 or the iterated-learning Bayesian-filter work (Beppu & Griffiths 2009, Krafft et al. 2016, Hardy et al. 2023)?

Two cases that have been on my mind lately as I've been delving into this research program.

Pinotti et al. 2025 in Nature paired ancient-genome sequencing of Picuris Pueblo individuals with Picuris oral-traditional accounts of lineage continuity back to the Chacoan era. The two converged. Lineage-group identity, migration narratives, and place-name continuity preserved across roughly a thousand years. Specific named individuals from the deeper end — their biographies, what they did, who they were — largely didn't.

Burley's 1998 paper on Tongan archaeology documents a cleaner version of the same pattern. The early Tu'i Tonga sacerdotal genealogy preserves position-ordinals and names back to ~AD 950, supported by institutional priestly recitation. The names are there. But Burley's exact phrase: "Little is known of these individuals beyond their names."

Two regions with no methodological connection. Same within-tradition pattern. Institutional infrastructure preserves the names but not the people.

The standard cumulative-culture story (Henrich, Boyd & Richerson) tells us high-fidelity transmission preserves cultural content but doesn't specify what enables high fidelity for some content and not others. The cultural-attraction story (Sperber) tells us drift goes toward cognitively easy forms but doesn't explain why some content resists that drift for millennia. Kelly's mnemonic-architecture story actually predicts that institutional infrastructure should preserve named-individual content. The Pinotti and Burley anchors suggest it doesn't, at least at this granularity.

Curious whether anyone here has worked on the granularity question explicitly. Within-tradition contrasts (named ancestors vs. lineage segments, ritual formulas vs. ritual explanations, named founders vs. named offices) seem like the cleanest empirical test — same source, same institution, same time depth, different granularity. Are there other case studies in the literature beyond Pinotti and Burley?

Eleven cultures on five continents, seven different drug classes, no shared ancestry or pre-colonial contact, all independently arrived at ceremony durations that match the pharmacological window of the substance.

• Yanomami epená (insufflated DMT, ~1 hr) → ~1 hr session
• Mazatec velada (psilocybin, ~5 hr) → 5–8 hr ceremony
• Native American Church (mescaline, ~10 hr) → sundown-to-sunrise, ~10 hr
• Bwiti iboga (ibogaine + noribogaine, 24–36 hr metabolic window) → 24 hr initiation
• Koryak fly agaric (muscimol, 4–8 hr) → 10–12 hr session, the gap explained by urine recycling — muscimol passes renally unmetabolized and Siberian shamans drink the urine of the person who ate the mushroom to extend the window. They didn't know the word muscimol. They knew how long the experience lasted and how to make it last longer.

Log-log slope is 1.010. 9 of 11 ceremony-to-drug ratios fall within ±0.5 of unity. The odds of this lining up by chance across unrelated traditions are about 1 in 100,000.

The one real outlier is Santo Daime - 10 hour hinários on a 4 hour brew. The other Brazilian ayahuasca church, UDV, runs 4 hour ceremonies on the same brew. Same drug, two communities, the extender is the one with documented added liturgical structure. Even the outlier is interpretable.

Clinical convergence is the part I find hardest to dismiss. Griffiths at Hopkins designed his psilocybin protocol around pharmacokinetic data - 6 hour sessions. Mazatec veladas are 6 hours. Riba's Barcelona ayahuasca sessions were 4 hours. UDV ceremonies are 4 hours. Different inputs, same output. Mass spectrometry and ethics boards on one side. Centuries of accumulated observation on the other. Both systems are solving the same optimization problem.

The maestros weren't doing pharmacokinetics. They were doing something a tradition can actually do... running the ceremony, watching when people came back, and converging across generations on the window that worked. The drug's clock is observable. Anything observable in a tradition that's been run for centuries gets calibrated.