Orthographic neighbourhood effects in parallel distributed processing models.

Abstract 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. A word's orthographic neighbourhood is classically defined as the set of words that can be created by changing one letter of the word while preserving letter positions (Coltheart, Davelaar, Jonasson, & Besner, 1977). For example, the words PINE, POLE, and TILE are all orthographic neighbours of the word PRE. In recent years, there have been a number of studies examining the effects of a word's orthographic neighbourhood on identification latencies (see Andrews, 1997, for a review), and a considerable, although sometimes contradictory, database on this topic has now emerged. Many models of the word recognition process do assume that the lexical representations of the orthographic neighbours of a presented word will be activated and will play an important role in the lexical selection process. In what follows, we first examine the predictions of serial-search models (Forster, 1976; Paap, Newsome, McDonald, & Schvaneveldt, 1982) and activation-based models (Grainger J McClelland & Rumelhart, 1981) with regard to orthographic neighbourhood effects. We then consider the role of orthographic neighbours in parallel distributed processing models (i.e., Plaut, McClelland, Seidenberg, & Patterson, 1996; Seidenberg & McClelland, 1989), which constitute the main focus of the present investigation. ORTHOGRAPHIC NEIGHBOURHOOD EFFECTS IN SERIAL-- SEARCH MODELS In serial-search models which incorporate a frequency-- ordered search through a candidate set of lexical entries (e.g., Forster, 1976; Paap, Newsome, McDonald, & Schvaneveldt, 1982), the size of a word's orthographic neighbourhood will influence the speed with which a correct match is found. More specifically, because a target word's orthographic neighbours will typically be members of an activated candidate set (due to their similarity to the target), increases in the number of neighbours will typically lead to increases in the size of the candidate set, which will in turn produce increases in the time required for lexical selection. According to serial search models then, words with large neighbourhoods should typically be processed more slowly than words with small neighbourhoods (such an effect can be referred to as "an inhibitory neighbourhood size effect"). Because the search through the candidate set is frequency-- ordered in these models, however, it is actually not the absolute neighbourhood size of a word that is critical, but the number of higher frequency neighbours in the word's orthographic neighbourhood. That is, only higher frequency neighbours would delay lexical selection, because only those candidates would have to be evaluated prior to the word itself during the frequency-ordered search for the target's lexical representation. …