Modelling Lexical Decision Using Corpus Derived Semantic Representations in a Connectionist Network

Connectionist models of the mapping from orthography or phonology to random binary semantic vectors allow the simulation of lexical decision with reaction times that show patterns of semantic and associative priming similar to those found experimentally with human subjects. The co-occurrence statistics of words in large corpora allow the generation of vectors whose distribution correlates with the perceived semantic relatedness of the words. Here we discuss the use of these more realistic corpus derived semantic representations in connectionist models of lexical decision. We find lexical decision priming that correlates with distances in the semantic vector space, but the reaction times are very noisy. Averages over many words and/or many networks are required for the relationships to become clear. The question of associative priming remains open.