Are Languages Really Independent from Genes? If Not, WhatWould a Genetic Bias Affecting Language Diversity Look Like?

It is generally accepted that the relationship between human genes and language is very complex and multifaceted. This has its roots in the “regular” complexity governing the interplay among genes and between genes and environment for most phenotypes, but with the added layer of supraontogenetic and supra-individual processes defining culture. At the coarsest level, focusing on the species, it is clear that human-specific—but not necessarily faculty-specific—genetic factors subtend our capacity for language and a currently very productive research program is aiming at uncovering them. At the other end of the spectrum, it is uncontroversial that individual-level variations in different aspects related to speech and language have an important genetic component and their discovery and detailed characterization have already started to revolutionize the way we think about human nature. However, at the intermediate, glossogenetic/population level, the relationship becomes controversial, partly due to deeply ingrained beliefs about language acquisition and universality and partly because of confusions with a different type of genelanguages correlation due to shared history. Nevertheless, conceptual, mathematical and computational models—and, recently, experimental evidence from artificial languages and songbirds—have repeatedly shown that genetic biases affecting the acquisition or processing of aspects of language and speech can be amplified by population-level intergenerational cultural processes and made manifest either as fixed “universal” properties of language or as structured linguistic diversity. Here, I review several such models as well as the recently proposed case of a causal relationship between the distribution of tone languages and two genes related to brain growth and development, ASPM and Microcephalin, and I discuss the relevance of such genetic biasing for language evolution, change, and diversity

Ultraviolet light affects the color vocabulary: evidence from 834 languages

It has been suggested that people living in regions with a high incidence of ultraviolet light, particularly in the B band (UV-B), suffer a phototoxic effect during their lifetime. This effect, known as lens brunescence, negatively impacts the perception of visible light in the “blue” part of the spectrum, which, in turn, reduces the probability that the lexicon of languages spoken in such regions contains a word specifically denoting “blue.” This hypothesis has been recently tested using a database of 142 unique populations/languages using advanced statistical methods, finding strong support. Here, this database is extended to 834 unique populations/languages in many more language families (155 vs. 32) and with a much better geographical spread, ensuring a much better representativity of the present-day linguistic diversity. Applying similar statistical methods, supplemented with novel piecewise and latent variable Structural Equation Models and phylogenetic methods made possible by the much denser sampling of large language families, found strong support for the original hypothesis, namely that there is a negative linear effect of UV-B incidence on the probability that a language has a specific word for “blue.” Such extensions are essential steps in the scientific process and, in this particular case, help increase our confidence in the proposal that the environment (here, UV-B incidence) affects language (here, the color lexicon) through its individual-level physiological effects (lifetime exposure and lens brunescence) amplified by the repeated use and transmission of language across generations

Ultraviolet light affects the color vocabulary: evidence from 834 languages

[eng] It has been suggested that people living in regions with a high incidence of ultraviolet light, particularly in the B band (UV-B), suffer a phototoxic effect during their lifetime. This effect, known as lens brunescence, negatively impacts the perception of visible light in the 'blue' part of the spectrum, which, in turn, reduces the probability that the lexicon of languages spoken in such regions contains a word specifically denoting 'blue.' This hypothesis has been recently tested using a database of 142 unique populations/languages using advanced statistical methods, finding strong support. Here, this database is extended to 834 unique populations/languages in many more language families (155 vs. 32) and with a much better geographical spread, ensuring a much better representativity of the present-day linguistic diversity. Applying similar statistical methods, supplemented with novel piecewise and latent variable Structural Equation Models and phylogenetic methods made possible by the much denser sampling of large language families, found strong support for the original hypothesis, namely that there is a negative linear effect of UV-B incidence on the probability that a language has a specific word for 'blue.' Such extensions are essential steps in the scientific process and, in this particular case, help increase our confidence in the proposal that the environment (here, UV-B incidence) affects language (here, the color lexicon) through its individual-level physiological effects (lifetime exposure and lens brunescence) amplified by the repeated use and transmission of language across generations

Non-spurious correlations between genetic and linguistic diversities in the context of human evolution

This thesis concerns human diversity, arguing that it represents not just some form of noise, which must be filtered out in order to reach a deeper explanatory level, but the engine of human and language evolution, metaphorically put, the best gift Nature has made to us. This diversity must be understood in the context of (and must shape) human evolution, of which the Recent Out-of-Africa with Replacement model (ROA) is currently regarded, especially outside palaeoanthropology, as a true theory. It is argued, using data from palaeoanthropology, human population genetics, ancient DNA studies and primatology, that this model must be, at least, amended, and most probably, rejected, and its alternatives must be based on the concept of reticulation. The relationships between the genetic and linguistic diversities is complex, including interindividual genetic and behavioural differences (behaviour genetics) and inter-population differences due to common demographic, geographic and historic factors (spurious correlations), used to study (pre)historical processes. It is proposed that there also exist nonspurious correlations between genetic and linguistic diversities, due to genetic variants which can bias the process of language change, so that the probabilities of alternative linguistic states are altered. The particular hypothesis (formulated with Prof. D. R. Ladd) of a causal relationship between two human genes and one linguistic typological feature is supported by the statistical analysis of a vast database of 983 genetic variants and 26 linguistic features in 49 Old World populations, controlling for geography and known linguistic history. The general theory of non-spurious correlations between genetic and linguistic diversities is developed and its consequences and predictions analyzed. It will very probably profoundly impact our understanding of human diversity and will offer a firm footing for theories of language evolution and change. More specifically, through such a mechanism, gradual, accretionary models of language evolution are a natural consequence of post-ROA human evolutionary models. The unravellings of causal effects of inter-population genetic differences on linguistic states, mediated by complex processes of cultural evolution (biased iterated learning), will represent a major advance in our understanding of the relationship between cultural and genetic diversities, and will allow a better appreciation of this most fundamental and supremely valuable characteristic of humanity - its intrinsic diversity

Biology matters: Variation in vocal tract anatomy and language

There are about 7,000 or so languages currently used, and they vary in myriad ways at all their levels. We argue here that part of this cross-linguistic diversity might be explained by factors that are external to language itself, but which differ between groups of speakers and to which language adapts. In particular, we present evidence that there is widespread variation between individuals and groups in what concerns the anatomy of the vocal tract, variation that results in biases (that generate constraints and affordances) which may affect phonetics and phonology. We propose that factors such as the frequency of the biased speakers, their status and position in the communicative network of a speech community form a pool of standing variation which interacts in complex ways with the community’s language and may result in the community-wide amplification of such biases. While more work is necessary, we suggest that these processes play a role in explaining the observed linguistic diversity

Beyond Adherence Thresholds: A Simulation Study of the Optimal Classification of Longitudinal Adherence Trajectories From Medication Refill Histories

International audienceBackground: The description of adherence based on medication refill histories relies on the estimation of continuous medication availability (CMA) during an observation period. Thresholds to distinguish adherence from non-adherence typically refer to an aggregated value across the entire observation period, disregarding differences in adherence over time. Sliding windows to divide the observation period into smaller portions, estimating adherence for these increments, and classify individuals with similar trajectories into clusters can retain this temporal information. Optimal methods to estimate adherence trajectories to identify underlying patterns have not yet been established. This simulation study aimed to provide guidance for future studies by analyzing the effect of different longitudinal adherence estimates, sliding window parameters, and sample characteristics on the performance of a longitudinal clustering algorithm

Environment and culture shape both the colour lexicon and the genetics of colour perception

Many languages express 'blue' and 'green' under an umbrella term 'grue'. To explain this variation, it has been suggested that changes in eye physiology, due to UV-light incidence, can lead to abnormalities in blue-green color perception which causes the color lexicon to adapt. Here, we apply advanced statistics on a set of 142 populations to model how different factors shape the presence of a specific term for blue. In addition, we examined if the ontogenetic effect of UV-light on color perception generates a negative selection pressure against inherited abnormal red-green perception. We found the presence of a specific term for blue was influenced by UV incidence as well as several additional factors, including cultural complexity. Moreover, there was evidence that UV incidence was negatively related to abnormal red-green color perception. These results demonstrate that variation in languages can only be understood in the context of their cultural, biological, and physical environments

Beyond Adherence Thresholds: A Simulation Study of the Optimal Classification of Longitudinal Adherence Trajectories From Medication Refill Histories

Background: The description of adherence based on medication refill histories relies on the estimation of continuous medication availability (CMA) during an observation period. Thresholds to distinguish adherence from non-adherence typically refer to an aggregated value across the entire observation period, disregarding differences in adherence over time. Sliding windows to divide the observation period into smaller portions, estimating adherence for these increments, and classify individuals with similar trajectories into clusters can retain this temporal information. Optimal methods to estimate adherence trajectories to identify underlying patterns have not yet been established. This simulation study aimed to provide guidance for future studies by analyzing the effect of different longitudinal adherence estimates, sliding window parameters, and sample characteristics on the performance of a longitudinal clustering algorithm.Methods: We generated samples of 250–25,000 individuals with one of six longitudinal refill patterns over a 2-year period. We used two longitudinal CMA estimates (LCMA1 and LCMA2) and their dichotomized variants (with a threshold of 80%) to create adherence trajectories. LCMA1 assumes full adherence until the supply ends while LCMA2 assumes constant adherence between refills. We assessed scenarios with different LCMA estimates and sliding window parameters for 350 independent samples. Individual trajectories were clustered with kml, an implementation of k-means for longitudinal data in R. We compared performance between the four LCMA estimates using the adjusted Rand Index (cARI).Results: Cluster analysis with LCMA2 outperformed other estimates in overall performance, correct identification of groups, and classification accuracy, irrespective of sliding window parameters. Pairwise comparison between LCMA estimates showed a relative cARI-advantage of 0.12–0.22 (p < 0.001) for LCMA2. Sample size did not affect overall performance.Conclusion: The choice of LCMA estimate and sliding window parameters has a major impact on the performance of a clustering algorithm to identify distinct longitudinal adherence trajectories. We recommend (a) to assume constant adherence between refills, (b) to avoid dichotomization based on a threshold, and (c) to explore optimal sliding windows parameters in simulation studies or selecting shorter non-overlapping windows for the identification of different adherence patterns from medication refill data

Interindividual variation refuses to go away: a Bayesian computer model of language change in communicative networks

Treating the speech communities as homogeneous entities is not an accurate representation of reality, as it misses some of the complexities of linguistic interactions. Interindividual variation and multiple types of biases are ubiquitous in speech communities, regardless of their size. This variation is often neglected due to the assumption that 'majority rules,' and that the emerging language of the community will override any such biases by forcing the individuals to overcome their own biases, or risk having their use of language being treated as 'idiosyncratic' or outright 'pathological.' In this paper, we use computer simulations of Bayesian linguistic agents embedded in communicative networks to investigate how biased individuals, representing a minority of the population, interact with the unbiased majority, how a shared language emerges, and the dynamics of these biases across time. We tested different network sizes (from very small to very large) and types (random, scale free, and small world), along with different strengths and types of bias(modeled through the Bayesian prior distribution of the agents and the mechanism used for generating utterances: either sampling from the posterior distribution ['sampler'] or picking the value with the maximum probability ['MAP']). The results show that, while the biased agents, even when being in the minority, do adapt their language by going against their a priori preferences, they are far from being swamped by the majority, and instead the emergent shared language of the whole community is influenced by their bias