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Investigating environmental effects on phonology using diachronic models.

ABSTRACT: Previous work has proposed various mechanisms by which the environment may affect the emergence of linguistic features. For example, dry air may cause careful control of pitch to be more effortful, and so affect the emergence of linguistic distinctions that rely on pitch such as lexical tone or vowel inventories. Criticisms of these proposals point out that there are both historical and geographic confounds that need to be controlled for. We take a causal inference approach to this problem to design the most detailed test of the theory to date. We analyse languages from the Bantu language family, using a prior geographic-phylogenetic tree of relationships to establish where and when languages were spoken. This is combined with estimates of humidity for those times and places, taken from historical climate models. We then estimate the strength of causal relationships in a causal path model, controlling for various influences of inheritance and borrowing. We find no evidence to support the previous claims that humidity affects the emergence of lexical tone. This study shows how using causal inference approaches lets us test complex causal claims about the cultural evolution of language.

SUBMITTER: Hartmann F

PROVIDER: S-EPMC10955398 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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Investigating environmental effects on phonology using diachronic models.

Hartmann Frederik F Roberts Seán G SG Valdes Paul P Grollemund Rebecca R

Evolutionary human sciences 20240103

Previous work has proposed various mechanisms by which the environment may affect the emergence of linguistic features. For example, dry air may cause careful control of pitch to be more effortful, and so affect the emergence of linguistic distinctions that rely on pitch such as lexical tone or vowel inventories. Criticisms of these proposals point out that there are both historical and geographic confounds that need to be controlled for. We take a causal inference approach to this problem to de ...[more]

PMID: 38516369

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Project description:BACKGROUND:A recent meta-analysis of nine cohort studies in youths reported that baseline ever e-cigarette use strongly predicted cigarette smoking initiation in the next 6-18 months, with an adjusted odds ratio of 3.62 (95% confidence interval 2.42-5.41). A recent review of e-cigarettes agreed there was substantial evidence for this "gateway effect". However, the number of confounders considered in the studies was limited, so we investigated whether the effect might have resulted from inadequate adjustment, using Waves 1 and 2 of the Population Assessment of Tobacco and Health study. METHODS:Our main analyses considered Wave 1 never cigarette smokers who, at Wave 2, had information available on smoking initiation. We constructed a propensity score for ever e-cigarette use from Wave 1 variables, using this to predict ever cigarette smoking. Sensitivity analyses accounted for use of other tobacco products, linked current e-cigarette use to subsequent current smoking, or used propensity scores for ever smoking or ever tobacco product use as predictors. We also considered predictors using data from both waves to attempt to control for residual confounding from misclassified responses. RESULTS:Adjustment for propensity dramatically reduced the unadjusted odds ratio (OR) of 5.70 (4.33-7.50) to 2.48 (1.85-3.31), 2.47 (1.79-3.42) or 1.85 (1.35-2.53), whether adjustment was made as quintiles, as a continuous variable or for the individual variables. Additional adjustment for other tobacco products reduced this last OR to 1.59 (1.14-2.20). Sensitivity analyses confirmed adjustment removed most of the gateway effect. Control for residual confounding also reduced the association. CONCLUSIONS:We found that confounding is a major factor, explaining most of the observed gateway effect. However, our analyses are limited by small numbers of new smokers considered and the possibility of over-adjustment if taking up e-cigarettes affects some predictor variables. Further analyses are intended using Wave 3 data which should avoid these problems.

Dataset Information

Investigating environmental effects on phonology using diachronic models.

Publications

Investigating environmental effects on phonology using diachronic models.

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