ABSTRACT
Unification-based approaches have come to play an important role in both
theoretical and applied modeling of cognitive processes, most notably natural
language. Attempts to model such processes using neural networks have met
with some success, but have faced serious hurdles caused by the limitations of
standard connectionist coding schemes. As a contribution to this effort, this
paper presents recent work in Infinite RAAM (IRAAM), a new connectionist
unification model. Based on a fusion of recurrent neural networks with fractal
geometry, IRAAM allows us to understand the behavior of these networks as
dynamical systems. Using a logical programming language as our modeling
domain, we show how this dynamical-systems approach solves many of the
problems faced by earlier connectionist models, supporting unification over
arbitrarily large sets of recursive expressions. We conclude that IRAAM can
provide a principled connectionist substrate for unification in a variety of
cognitive modeling domains
Language and Connectionism: Three Approaches
Language, to a cognitive scientist, can be held to include natural language and the
“language of thought” (Fodor 1975), as well as symbolic programming languages
developed to simulate these, like LISP and Prolog. Attempts to build connectionist
models of such systems have generally followed one of three approaches.
