Semiotics, Meaning, and Discursive Neural Networks
Knowledge representation is too complicated to be well-served by the traditional semantic approach (e.g., Katz, 1972; Winston, 1977). The problems encountered by Rumelhart and McClelland (1986), as pointed out by Fodor and Pylyshyn (1988), are endemic to the model employed. Eco (1976, 1984) and others have urged that we see all systematically-generated information as parts of a "science of signs", semiotics. The three-faceted semiotic structure embraces information from the genetic level to language — and production. Semiotics fits modern neurolinguistic approaches (e.g., Dingwall, 1980) and neurophysiological theorizing (Arnold, 1984; Black et al, 1988; Posner & Keele, 1968). It also enables us to employ "holographic models" (Pribram, 1971) — and to extend our models to higher level processes (Leven, 1987a, 1987b; Levine, 1986). Ultimately, both internal and interpersonal interactions are discursive.