Reasoning About Cultural and Genetic Transmission: Developmental and Cross‐Cultural Evidence From Peru, Fiji, and the United States on How People Make Inferences About Trait Transmission
Bibliographic record
Abstract
Using samples from three diverse populations, we test evolutionary hypotheses regarding how people reason about the inheritance of various traits. First, we provide a framework for differentiat-ing the outputs of mechanisms that evolved for reasoning about variation within and between (a) biological taxa and (b) culturally evolved ethnic categories from (c) a broader set of beliefs and categories that are the outputs of structured learning mechanisms. Second, we describe the results of a modified "switched-at-birth" vignette study that we administered among children and adults in Puno (Peru), Yasawa (Fiji), and adults in the United States. This protocol permits us to study perceptions of prenatal and social transmission pathways for various traits and to differentiate the latter into vertical (i.e., parental) versus horizontal (i.e., peer) cultural influence. These lines of evidence suggest that people use all three mechanisms to reason about the distribution of traits in the population. Participants at all three sites develop expectations that morphological traits are under prenatal influence, and that belief traits are more culturally influenced. On the other hand, each population holds culturally specific beliefs about the degree of social influence on non-morphological traits and about the degree of vertical transmission-with only participants in the United States expecting parents to have much social influence over their children. We reinterpret people's differentiation of trait transmission pathways in light of humans' evolutionary history as a cultural species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".