The role of genetic biases in shaping the correlations between languages and genes

Abstract

It has recently been proposed (Dediu, D., Ladd, D.R., 2007. Linguistic tone is related to the population frequency of the adaptive haplogroups of two brain size genes, ASPM and Microcephalin. Proc Natl Acad Sci USA 104, 10944-10949) that genetically coded linguistic biases can influence the trajectory of language change. However, the nature of such biases and the conditions under which they can become manifest have remained vague. The present paper explores computationally two plausible types of linguistic acquisition biases in a population of agents implementing realistic genetic, linguistic and demographic processes. One type of bias represents an innate asymmetric initial state (Initial Expectation bias)while the other an innate asymmetric facility of acquisition (Rate of Learning bias). It was found that only the second type of bias produces detectable effects on language through cultural transmission across generations and that such effects are produced even by weak biases present at low frequencies in the population. This suggests that learning preference asymmetries, very small at the individual level and not very frequent at the population level, can bias the trajectory of language change through the process of cultural transmission.

A Transcription Factor Map as Revealed by a Genome-Wide Gene Expression Analysis of Whole-Blood mRNA Transcriptome in Multiple Sclerosis

“…ASPM is also a highly accelerated region that holds key features that make us human [112], including perhaps language [180]–[182]. Deficits in ASPM's functions has been consistently associated with neurodevelopmental disorders, primary in microencephaly [183]–[188].…”

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

“…I also have used two types of computational agents, Bayesian (sampler and maximizers) and non-Bayesian (implementing two types of biases: initial expectation representing an asymmetric initial state and rate of learning representing an asymmetric easiness of acquisition). The simulations showed that Bayesian agents in situations 2 and 3 still produce language influenced by their biases but in a much more complex manner (Dediu 2009) and that non-Bayesian agents implementing a rate of learning bias also show bias amplification even in scenario 3 (Dediu 2008).…”

“…The generalizability of such simple models has been questioned both on grounds of the social and communicative structures considered (Dediu 2008(Dediu , 2009) as well as in the assumptions behind this Bayesian model of language acquisition and usage (Dediu 2009;Ferdinand and Zuidema 2009). To study the effects of more complex (and realistic) social and communicative settings, I have recently conducted a series of studies (Dediu 2008(Dediu , 2009) where computational agents learn and transmit language either in (1) the simple, single-agent transmission chain discussed above; (2) a more complex setting involving chains of pairs of agents (allowing vertical, horizontal, and oblique transmission); and (3) a two-dimensional world with complex demography, featuring several populations that migrate and interact.…”

“…To study the effects of more complex (and realistic) social and communicative settings, I have recently conducted a series of studies (Dediu 2008(Dediu , 2009) where computational agents learn and transmit language either in (1) the simple, single-agent transmission chain discussed above; (2) a more complex setting involving chains of pairs of agents (allowing vertical, horizontal, and oblique transmission); and (3) a two-dimensional world with complex demography, featuring several populations that migrate and interact. I also have used two types of computational agents, Bayesian (sampler and maximizers) and non-Bayesian (implementing two types of biases: initial expectation representing an asymmetric initial state and rate of learning representing an asymmetric easiness of acquisition).…”

Linguistic Diversity and Traffic Accidents: Lessons from Statistical Studies of Cultural Traits

“…By demonstrating that the hypothesised link was stronger than competing hypotheses, a convincing claim was made for the further experimental investigation of this hypothesis. In order to develop the basis of the general theory, a follow-up experimental study found support for part of the hypothesis in that there are individual differences in the perception of pitch [21], and a computer simulation demonstrated that such differences could influence linguistic structure in the long-term [22].…”