What Can Homophone Effects Tell Us About the Nature of Orthographic Representation in Visual Word Recognition

What Can Homophone Effects Tell Us About the Nature of Orthographic Representation in Visual Word Recognition? Jodi D. Edwards (jdedward@ucalgary.ca) Department of Linguistics, University of Calgary 2500 University Drive NW, Calgary, AB, Canada T2N 1N4 Penny M. Pexman (pexman@ucalgary.ca) Department of Psychology, University of Calgary 2500 University Drive NW, Calgary, AB, Canada T2N 1N4 Abstract In a lexical decision task (LDT), Pexman, Lupker, and Jared (2001) reported longer response times for homophones (e.g., MAID-MADE) than for non-homophones (e.g., MESS) and attributed these effects to orthographic competition created by feedback activation from phonology. The focus of the present research was the grain-size of the orthographic units activated by feedback from phonology. We created 9 categories of homophones based on the sublexical, orthographic overlap between members of homophone pairs. We also manipulated the type of foils presented in LDT (consonant strings, pseudowords, pseudohomophones) to create conditions involving less vs. more extensive processing. Homophone effect sizes varied by category; effects were largest when spellings of both onsets and bodies differed within the homophone pairs (e.g., KERNEL-COLONEL) and when members of the homophone pairs differed by vowel graphemes (e.g., BRAKE-BREAK). These results suggest that several specific grain-sizes of orthographic representation are activated by feedback phonology. Feedback activation is assumed to operate between all sets of units in the word recognition system. The focus of the present research, however, was feedback activation from phonology to orthography. Taft and van Graan (1998; see also Taft, 1991) argued for bi-directional activation between orthography and phonology by what they termed “orthography-phonology-orthography rebound”. The model of word recognition they described was similar to models proposed by Grainger and Ferrand (1994), Plaut et al. (1996), and Van Orden and Goldinger (1994). A version of this model is illustrated in Figure 1. This is a connectionist model with sets of processing units representing orthographic, phonological, and semantic information. Importantly, the orthographic and phonological components of the model (but not the semantic component) are “composed of a hierarchy of units ranging from graphemes (e.g., C, A, and T) and phonemes (e.g., /k/, /ae/, and /t/) up to whole words. Activation passes up this hierarchy as well as between O and P units at the same level. ” (p. 206). Introduction In a number of recent articles in the word recognition literature, the notion of feedback activation has been invoked to explain particular findings (e.g., Hino & Lupker, 1996; Pecher, in press; Pexman & Lupker, 1999; Pexman, Lupker, & Jared, 2001; Stone, Vanhoy, & Van Orden, 1997; Taft & van Graan, 1998; Ziegler, Montant, & Jacobs, 1997). In a fully interactive model of word recognition (e.g., the PDP model of Plaut, McClelland, Seidenberg, & Patterson, 1996) activation between sets of units can be bi-directional. For instance, in a lexical decision task, when a target word is presented, there is initial activation of an orthographic representation for the target, and then very quickly there is also activation of semantic and phonological representations for that word. Those semantic and phonological representations then re-activate, via feedback connections, the orthographic representation. This bi-directional flow of activation can help the system settle on a representation for the target word. The purpose of the present research was to address an unresolved issue regarding feedback activation: What is the nature (grain-size) of the orthographic units that are activated by feedback from phonology? S CAT kaet AT aet O P C A T k t Figure 1: As depicted in Taft & van Graan (1998), a model of word recognition with sets of units representing orthography (O), phonology (P), and semantics (S). Taft and van Graan (1998) argued that, when processing printed words, there is automatic activation of the phonological component of the model. Certainly, there has been controversy about the role that phonology plays in