A Connectionist Architecture with Inherent Systematicity

For connectionist networks to be adequate for higher level cognitive activities such as natural language interpretation, they have to generalize in a way that is appropriate given the regularities of the domain. Fodor and Pylyshyn (1988) identified an important pattern of regularities in such domains, which they called systematicity. Several attempts have been made to show that connectionist networks can generalize in accordance with these regularities, but not to the satisfaction of the critics. To address this challenge, this paper starts by establishing the implications of systematicity for connectionist solutions to the variable binding problem. Based on the work of Hadley (1994a), we argue that the network must generalize information it learns in one variable binding to other variable bindings. We then show that temporal synchrony variable binding (Shastri and Ajjanagadde, 1993) inherently generalizes in this way. Thereby we show that temporal synchrony variable binding is a connectionist architecture that accounts for systematicity. This is an important step in showing that connectionism can be an adequate architecture for higher level cognition.