ABSTRACT
It is difficult to clearly define the symbolic and subsymbolic paradigms; each is usually described by its tendencies rather than any one definitive property. Symbolic processing is generally characterized by hard-coded, explicit rules operating on discrete, static tokens, whereas subsymbolic processing is associated with learned, fuzzy constraints affecting continuous, distributed representations. In addition, programming languages such as LISP and mechanisms such as Turing machines are typically associated with the symbolic paradigm, whereas connectionism is frequently associated with the subsymbolic paradigm. Debates contrasting the two paradigms sometimes center on these mechanisms, for example comparing the capabilities of Turing machines with those of connectionist networks (see Adams, Aizawa, & Fuller chap. 3 in this volume). However, connectionist networks can be proven to be computationally equivalent to the abstract notion of Turing machines (Franklin & Garzon, 1990). Therefore the computational mechanism is not the crucial issue in separating the symbolic and subsymbolic paradigms. What then is the crucial issue?
