Build
A reader's tool for the psychology of individual differences. Pick a trait, see the three plain-language buckets (direct genes / family setup / environment + chance) instead of the V(A_d)/V(A_LD)/V(A_i) decomposition. Plus the four motivated-reasoning traps the field gets caught in, the asymmetric environmental-effects finding, three "heritability ≠ destiny" misreadings, and a seven-bullet take-away. Translates the formalization and data pipeline into something a non-specialist can actually use.
TLDR
The model formalization produced one equation per person and seven variance components. The data pipeline produced eight tested predictions and seven downloadable CSVs. Both are correct, both are useful for someone who already speaks the vocabulary, and neither does what the topic statement asked: produce something useful for someone who wants to understand how and why people differ without being captured by motivated reasoning from any direction.
This build is that translation layer. It collapses the seven-component variance decomposition into three plain-language buckets — direct genes, family setup, environment + chance — picks the ten traits a reader most likely cares about, and for each one shows the bucket breakdown, the key environmental levers (when relevant), and the two specific ways the most common political readings of that trait go wrong. Plus four secondary views: the four motivated-reasoning traps the field gets caught in (with what each side cites correctly and ignores), the asymmetric environmental-effects finding (severe insults cost 10–30 IQ points; enrichment above normal yields a few at most — the single most useful action-oriented insight), three “heritability ≠ destiny” misreadings with worked examples, and a seven-bullet take-away that holds up across mainstream behavior genetics in 2026.
If you want to engage with the math, the model stage has the parametric dashboard and the data stage has the prediction-by-prediction empirical tests. This page is for the reader who wants to come away knowing what to actually believe.
Pick a trait
Cognitive ability — adults
Why people differ in cognitive ability as adults is mostly genetic at the population level — but a sizeable chunk of what twin studies count as 'genetic' is actually the family setup parents create, not direct biological causation.
Why people differ — three buckets
The slice that's actually direct biological causation. What within-family designs (sibling-fixed-effect, MZ-discordant, parent-offspring trio GWAS) recover after stripping out parental environment and assortative-mating-induced linkage.
Most of this bucket is genetic nurture — parents who pass on cognitive-ability variants also create environments correlated with those variants (vocabulary, books, expectations, peer-group selection). Classical twin models cannot easily separate this from direct biological causation. Within-family GWAS for cognition recovers ~0.50, substantially below twin h² of 0.79; the gap is dominated by genetic-nurture leakage. About ~5% is residual shared family environment that persists into adulthood. Assortative mating (m=0.44 for IQ) does inflate population-level V(A) via LD but biases Falconer's twin formula downward, partially canceling rather than adding to the gap.
Most of this small bucket is unmeasured developmental noise. Identified large levers (severe deprivation, lead, fetal alcohol syndrome) account for almost no population variance in modern Western samples because their prevalence is now low.
Severe negative levers (when present)
- Prenatal alcohol (full FAS)−30 IQ ptsStreissguth 2004
- Severe deprivation (Romanian orphanages)−15 IQ ptsNelson 2007 BEIP
- Lead, blood 1→10 µg/dL−6.2 IQ ptsLanphear 2005
Positive levers
- Schooling, per year+1 to +5 IQ ptsRitchie & Tucker-Drob 2018
- Within-Western-normal parenting~0 to +1 IQ ptsPlomin & Daniels 1987
What environmentalist readings get wrong here
'Heritability is just methodological artifact' is not what the evidence shows. SNP-based heritability bypasses twin-design assumptions and recovers most of twin h²; adoption studies converge on similar numbers. The signal is real. But citing 0.79 as if it means 'genes determine 79% of cognitive ability' confuses a population-variance ratio with an individual partition. Both moves drop information.
What hereditarian readings get wrong here
Citing 0.79 to argue 'environment doesn't matter much for cognition' ignores that ~37% of the 'genetic' bucket disappears when you switch to within-family designs. The direct-biological component is closer to ~50%, and the gap to twin h² is dominated by genetic nurture and equal-environments-assumption violations rather than direct biological causation.
Take away
About half of why adults differ in cognitive ability is direct genetic effect; another ~35% is the family setup that genetically-similar parents create around their kids; ~15% is everything else. The interesting policy levers are at the tails (preventing severe insults like lead, malnutrition, fetal alcohol, and severe deprivation), not at the middle (parenting style within Western normal).
How to use this
The default view is trait lookup. Pick a trait — adult cognitive ability, schizophrenia, height, political orientation — and see the three-bucket breakdown plus the trait-specific traps and take-away. Most readers should start there, then move through the four secondary views in order.
A few framing notes:
The three buckets are not orthogonal categories of cause. They are three plain-language groupings of the seven model-formalization variance terms (A_d, A_LD, A_i, C, E_m, E_s, I). Direct genes is the within-family direct-effect slice — the part that is unambiguously direct biological causation. Family setup combines AM-induced LD, genetic nurture, and residual shared environment — all the things that get counted as “genetic” in twin studies but are not direct biological causation. Environment + chance combines measured non-shared environment and stochastic developmental noise. The split is pedagogical; the model shows the underlying seven-term decomposition.
The four-traps view is opinionated in a way the other views are not. The label “trap” assumes that motivated reasoning is what produces these positions, which is not entirely fair — most people cite the evidence they have seen and have not personally vetted what they have not seen. The integrated reading at the bottom of each trap card is the closest the artifact comes to a normative claim about how the field should be read, and it is not algorithmically derivable from the data alone. If you disagree with one of the integrated readings, the topology stage has the underlying graph.
The asymmetry finding is the most action-relevant single insight in the topic. If you only take one thing away from this work, take that one — the population-level cognitive levers run almost entirely through preventing severe insults, not through optimizing within normal. Most parental anxiety and policy expenditure on enrichment is misallocated relative to where the empirical effect sizes are.
The seven take-aways are calibrated to be the things a behavior-geneticist in 2026 would actually defend in a public talk. Finer-grained claims (specific magnitudes per trait, mechanism per finding, what polygenic scores measure causally) sit downstream of these and are more contested.
What this is not
It is not a prediction tool. There is no model that takes your demographics, your parents’ phenotypes, or your DNA and outputs a predicted trait value. The science does not currently support that for psychological traits, and the data stage shows why — polygenic scores trained on European-ancestry data lose 30–80% of their accuracy across other ancestries, and within-family direct effects are often less than half of population-level prediction.
It is not policy advice. The asymmetry finding has clear implications for cognitive intervention (lead remediation has higher effect-per-dollar than enrichment programs), but turning empirical asymmetries into policy involves trade-offs the science does not adjudicate.
It is not a complete picture. Three open questions named in the model stage (the Plomin/Turkheimer dispute about what polygenic scores measure, the mechanism behind the Gender Equality Paradox, the magnitude of assortative-mating correction across the full psychiatric cross-disorder rg matrix) are not answered here because the field has not answered them. The honest reading is “we don’t know yet”; the artifact does not pretend otherwise.
Connection to the rest of the pipeline
The trait-lookup numbers are computed directly from public/data/human-psych-variation/heritability_estimates.csv (the Stage-4 input), with the H2 partition (V(A_LD) = m·h²) and the genetic-nurture identity (V(A_i) = (β_i/β_d)² · V(A_d)) applied as in the model formalization §3.3 pass 5. The asymmetry view’s exposure list comes from environmental_effects.csv (the H7 input). The four-traps view materializes the topology stage’s Variant D distortion-to-target matrix (D1–D4) into reader-facing cards.
A future stretch would promote some of this to /dashboards/human-psych-variation as a public dashboard that lets the visitor enter their own per-trait estimates and see the buckets recompute. That is one of the planned dashboard slots in the site PRD but is out of scope for the first build.