Words as alleles: connecting language
evolution with Bayesian learners to
models of genetic drift
Florencia Reali* and Thomas L. Griffiths
Department of Psychology, 3210 Tolman Hall, MC 1650, University of California at Berkeley,
Berkeley, CA 94720-1650, USA
Scientists studying how languages change over time often make an analogy between biological and
cultural evolution, with words or grammars behaving like traits subject to natural selection. Recent
work has exploited this analogy by using models of biological evolution to explain the properties of
languages and other cultural artefacts. However, the mechanisms of biological and cultural evolution are
very different: biological traits are passed between generations by genes, while languages and concepts
are transmitted through learning. Here we show that these different mechanisms can have the same results,
demonstrating that the transmission of frequency distributions over variants of linguistic forms by Bayesian
learners is equivalent to the Wright–Fisher model of genetic drift. This simple learning mechanism thus
provides a justification for the use of models of genetic drift in studying language evolution. In addition
to providing an explicit connection between biological and cultural evolution, this allows us to define a
‘neutral’ model that indicates how languages can change in the absence of selection at the level of linguistic
variants. We demonstrate that this neutral model can account for three phenomena: the s-shaped curve of
language change, the distribution of word frequencies, and the relationship between word frequencies and
Keywords: language evolution; genetic drift; Bayesian inference; neutral models
Natural languages, like species, evolve over time. The
mechanisms of language evolution are quite different
from those underlying biological evolution, with learning
being the primary mechanism by which languages are
transmitted between people. However, accounts of
language evolution often appeal to forces that have ana-
logues in biological evolution, such as selection or
directed mutation. Recent computational work has
emphasized the role of selective forces by focusing on
the consequences of a language for the ‘fitness’ of
its speakers in terms of communication success (Cavalli-
Sforza & Feldman 1983; Hurford 1989; Oliphant 1994;
Komarova & Nowak 2001). Other studies have empha-
sized the effects of differential learnability of competing
linguistic variants, with selection or directed mutation
operating at the level of sounds, words or grammatical
structures (Batali 1998;
Christiansen & Chater 2008). These functional expla-
nations provide an intuitive and appealing account of
language evolution. However, it is possible that the
changes we see in languages over time could be explained
without appealingto such
processes analogous to genetic drift.
Evaluating the role of selective forces in language evol-
ution requires developing neutral models for language
evolution, characterizing how languages can be expected
to change simply as a consequence of being passed from
one learner to another in the absence of selection or
directed mutation. Neutral models have come to play a
significant role in the modern theory of biological evol-
ution, where they account for variation seen at the
molecular level and provide a tool for testing for the
presence of selection (Kimura 1983). The work men-
tioned in the previous paragraph illustrates that there
are at least two levels at which evolutionary forces can
operate in language evolution: at the level of entire
languages (through the fitness of speakers or directed
mutation when languages are passed from one speaker
to another), and at the level of individual linguistic
variants (with particular sounds, words or grammatical
structures being favoured over others by learners). In
this paper, we define a model that is neutral at the level
of linguistic variants, indicating how languages can
change in the absence of selection for particular variants.
Defining a model of language evolution that is neutral
at the level of linguistic variants requires an account
of learning that is explicit about the inductive biases of
learners—those factors that make some variants easier
to learn than others—so that it is clear that these biases
do not favour particular variants. We model learning as
statistical inference, with learners using Bayes’ rule to
combine the clues provided by a set of utterances with
inductive biases expressed through a prior distribution
over languages. We define a neutral model by using a
prior that assigns equal probability to different variants
of a linguistic form. While it is neutral at the level of
variants, this approach allows for the possibility that lear-
ners have more general expectations about the structure
of a language—such as the amount of probabilistic
variation in the language, and the tendency for new
* Author for correspondence (email@example.com).
Electronic supplementary material is available at http://dx.doi.org/10.
1098/rspb.2009.1513 or via http://rspb.royalsocietypublishing.org.
Proc. R. Soc. B (2010) 277, 429–436
Published online 7 October 2009
Received 21 August 2009
Accepted 21 September 2009
This journal is q 2009 The Royal Society
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436F. Reali & T. L. Griffiths
Words as alleles
Proc. R. Soc. B (2010)