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Unpublished PhD dissertation. University of Edinburgh, Scotland
Infinite Languages, Finite Minds: Connectionism, Learning and Linguistic Structure
Morten H. Christiansen
Abstract
This thesis presents a connectionist theory of how infinite languages may fit within nite
minds. Arguments are presented against the distinction between linguistic competence
and observable language performance. It is suggested that certain kinds of finite state
automata--i.e., recurrent neural networks|are likely to have sufficient computational
power, and the necessary generalization capability, to serve as models for the processing
and acquisition of linguistic structure. These arguments are further corroborated by
a number of computer simulations, demonstrating that recurrent connectionist models
are able to learn complex recursive regularities and have powerful generalization
abilities. Importantly, the performance evinced by the networks are comparable with
observed human behavior on similar aspects of language. Moreover, an evolutionary account
is provided, advocating a learning and processing based explanation of the origin
and subsequent phylogenetic development of language. This view construes language
as a nonobligate symbiant, arguing that language has evolved to fit human learning
and processing mechanisms, rather than vice versa. As such, this perspective promises
to explain linguistic universals in functional terms, and motivates an account of language
acquisition which incorporates innate, but not language-specific constraints on
the learning process. The purported poverty of the stimulus is re-appraised in this light,
and it is concluded that linguistic structure may be learnable by bottom-up statistical
learning models, such as, connectionist neural networks.
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