ABSTRACT
An investigation of the capacity of distributed systems to represent patterns of activation in parallel is presented. Connectionist models of lexical ambiguity have captured this capacity by activating the arithmetic mean of the vectors representing the relevant meanings to form a lexical blend. However, a more extreme test of this system occurs in a distributed model of lexical access in speech perception, which may require a lexical blend to represent transiently the meanings of hundreds of words. I show that there is a strict limit on the number of distributed patterns that can be represented effectively by a lexical blend. This limit is dependent to some extent on the structure and content of the distributed space, which in the case of lexical access corresponds to structure and content of the mental lexicon. This limitation implies that distributed models cannot be simple re-implementations of parallel localist models and offers a valuable opportunity to distinguish experimentally between localist and distributed models of cognitive processes.
