Orthographic neighbourhood effects in parallel distributed processing models

Recent research in visual word recognition suggests that the speed with which a word is identified is influenced by the reader's knowledge of other, orthographically similar words (Andrews, 1997). In serial-search and activation-based models of word recognition, mental representations of these "orthographic neighbours" of a word are explicitly assumed to play a role in the lexical selection process. Thus, it has been possible to determine the specific predictions that these models make about the effects of orthographic neighbours and to test a number of those predictions empirically. In contrast, the role of orthographic neighbours in parallel distributed processing models (e.g., Plaut, McClelland, Seidenberg, & Patterson, 1996; Seidenberg & McClelland, 1989) is less clear. In this paper, several statistical analyses of error scores from these types of models revealed that low frequency words with large neighbourhoods had lower orthographic, phonological, and cross-entropy error scores than low frequency words with small neighbourhoods; and that low frequency words with higher frequency neighbours had lower error scores than low frequency words without higher frequency neighbours. According to these models then, processing should be more rapid for low frequency words with large neighbourhoods and for low frequency words with higher frequency neighbours.