Dear all,

I'm pleased to announce the availability of a recent PhD thesis on language
evolution:

Self-organization and Language Evolution: System, Population and Individual

A pdf file of the thesis is available from:
http://www.ee.cityu.edu.hk/~jyke/phdthesis.htm

Your comments and advice are welcome.

Best regards,
Jinyun Ke


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Abstract of the thesis
This thesis proposes a framework adopting the self-organization theory for the
study of language evolution. Self-organization explains collective behaviors
and evolution with the observation that the patterns at the global level in a
complex system are often properties spontaneously emergent from the numerous
local interactions among the individual components, and they cannot be
understood by only examining the individual components.

Language can be viewed as such emergent properties instead of products from
some innate blueprint in humans. We highlight the importance of recognizing
language at two distinctive but inter-dependent levels of existence, i.e. in
the idiolect and in the communal language, and a self-organizing process
existing at each of the two levels. It is necessary to clarify what phenomena
are properties of the idiolects, and what properties are the collective
behaviors at the population level.

In linguistics, however, very often an abstract language system is taken as
the object of analysis. This level of analysis disregards the distinction
between idiolect and communal language, and neglects the heterogeneous nature
of language at both levels. As a consequence, explanations for observed
patterns based on this abstract level of analysis are often inadequate.
However, this is a necessary step for linguists to identify interesting
phenomena in the first place. At this abstract level of analysis, the
self-organization framework can also be applied. It is assumed that the
abstract language system self-organizes. A study on homophony in languages is
taken as an example to illustrate the analysis at this level. It is shown that
the existence of homophony reflects several self-organization characteristics
in a dynamic process of language evolution, such as the predictable degree of
homophony, the disyllabification in Chinese dialects, the differentiation of
homophone pairs in grammatical class.

We are further interested in how the self-organization is implemented. To
answer this question, we need to look into the idiolects in this
self-organizing process, to know how the idiolects are formed and affect each
other. Language change provides an informative window in addressing these
issues. Language change is the result of the collective behaviors of
idiolects, even as it affects the idiolects. The heterogeneity among idiolects
is exposed to the greatest extent in on-going changes.

An on-going sound change in Cantonese is taken as a case study to scrutinize
the heterogeneity in the self-organizing processes. The fieldwork data reveal
a large degree of variation both in the population (VT-I) and in the set of
words (VT-II). Another type of variation (VT-III) is highlighted, that is, a
word may also show variation within one single speaker. But this VT-III within
speakers only exists in a proportion, but not all, of the words subject to the
change. Also we find that if a speaker has some words consistently in the
unchanged state and some words in the changed state, then this speaker must
have some other words in the variation state. Most speakers show the existence
of VT-III, but they vary in degree. The observed individual differences in the
degree of VT-III suggest that the large heterogeneity may be not only
accounted for by the variability of linguistic input, but also by individuals'
different learning styles.
We hypothesize two types of lexical learning styles, i.e. probabilistic and
categorical learning. These differences in learning styles suggest that when
we examine the agent's internal properties in the self-organization framework,
it is not only necessary to examine the commonalities among agents, but also
the differences among them.

In addition to empirical studies, this thesis employs computational modeling
as a major tool for investigation, as modeling provides effective ways to test
hypotheses beyond empirical studies, and suggests new questions. After a brief
review of the modeling studies in the field, some models developed in this
thesis for language origin and language change are reported.

The first model is to simulate the emergence of a consistent vocabulary from a
set of random mappings between meanings and forms. It emphasizes the
importance of implementing the actual process of interaction among agents, and
the cumulative effect on agents' linguistic behaviors. The model suggests that
the Saussurean sign with identical speaking and listening mappings may not be
a biological predisposition from natural selection, but rather a result from
the process of language learning and use. The process exhibits a
phase transition from a long period of small oscillation to an abrupt
convergence. Such phase transition is often observed in self-organizing
systems.

The second model simulates language change as innovation diffusion, and
examines the effects of various factors, including some concerning properties
of agents and some affecting agents' interactions. By comparing the outcome
under different conditions, the model illustrates the importance of
incorporating realistic assumptions, such as finite population size,
age-dependent propensity to change, different learning environment in a social
network, etc. The model compares the dynamics of language change in different
types of network structures and shows that in non-regular networks, the rate
of innovation diffusion increases little as population size increases. The
model also tests the effect of the two types of hypothesized learning styles,
and shows that in a population with the presence of probabilistic learners, an
innovation with a small advantage will easily spread into the population and
lead to a change. This may explain why language changes are so frequent.

This thesis demonstrates that both empirical and modeling studies on language
evolution can greatly benefit from adopting a self-organization framework. The
convergence and interplay of the two lines of exploration, i.e. biological
bases in agents and the long term effect of interactions among them, should
bring us a deeper understanding of how language has evolved and is evolving.