the properties of distributed network models, and support this account by demonstrating that an implemented simulation closely approximates the empirical findings despite the absence of expectancy-based processes and postlexical semantic matching. The results suggest that distributed network models can provide a viable single-mechanism account of lexical processing. Introduction It is well-established that people are faster and more accurate to read a word (e.g., BUTTER) when it is preceded by a related word (e.g., BREAD) compared with when it is preceded by an unrelated word (e.g., DOCTOR; The research was supported by an NIMH FIRST award (MH55628) to the first author and by NIMH Training Grant 5T32MH19102 and NICHD Grant 80258. The computational simulation was run using customized software written within the Xerion simulator (version 3.1) developed by Drew van Camp, Tony Plate, and Geoff Hinton at the Univers

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this article may be sent to David Plaut, Mellon Institute 115-- CNBC, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh PA 15213--2683; email: plaut@cmu.edu

this article were implemented using software developed by Michael Harm, whom we also thank

INTRODUCTION Many researchers assume that the most appropriate way to express the systematic aspects of language is in terms of a set of rules. For instance, there is a systematic relationship between the written and spoken forms of most English words (e.g., GAVE f /geIV/), and this relationship can be expressed in terms of a fairly concise set of grapheme-phoneme correspondence (GPC) rules (e.g., G f /g/, A_E f /eI/, V f /v/). In addition to being able to generate accurate pronunciations of so-called regular words, such rules also provide a straightforward account of how skilled readers apply their knowledge to novel items---for Direct all correspondence to: David C. Plaut, Mellon Institute 115-CNBC, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213-2683; E-mail: plaut@cmu.edu 543 example, in pronouncing word-like nonwords (e.g., MAVE f /meIV/). Most linguistic domains, however, are only partially systematic. Thus, there are many English words whose pronunciations

eded word naming studies in which subjects had been asked to pronounce a printed word "as quickly and accurately as possible" (or some instructions to that effect) . The speeded word naming task has been used to examine a wide variety of theoretical issues, including: processes that map orthography to phonology (Glushko, 1979; Seidenberg, Waters, Barnes, & Tanenhaus, 1984), organization of the lexicon (Forster & Chambers, 1973; Frederiksen & Kroll, 1976), semantic, phonological, and orthographic priming (Forster & Davis, 1991; Tabossi & Laghi, 1992; Taraban & McClelland, 1987), sentence and discourse processes (Hess, Foss, & Carroll, 1995; Trueswell, Tanenhaus, & Kello, 1993), reading impairments (Patterson & Behrmann, 1997; Stanovich, Siegel, & Gottardo, 1997), and reading acquisition (Lemoine, Levy, & Hutchinson, 1993; Manis, 1985). In each of these areas of research, a primary source of data has come from latencies in naming tasks. Therefore, understanding the processes responsible

this paper. Correspondence concerning this article should be addressed to Michael W. Harm, Center for the Neural Basis of Cognition, 115 Mellon Institute, 4400 Fifth Avenue, Pittsburgh, Pennsylvania, 15213. Electronic mail may be sent to mharm@cnbc.cmu.edu

A long-standing debate regarding the representation of semantic knowledge is whether such knowledge is represented in a single, amodal system or whether it is organised into multiple subsystems based on modality of input or type of information. The current paper presents a distributed connectionist model of semantics that constitutes a middle ground between these unitary- versus multiple-semantics accounts. In the model, semantic representations develop under the pressure of learning to mediate between multiple input and output modalities in performing various tasks. The system has a topographic bias on learning that favours short connections, leading to a graded degree of modality-specific functional specialisation within semantics. The model is applied to the specific empirical phenomena of optic aphasia—a neuropsychological disorder in which patients exhibit a selective deficit in naming visually presented objects that is not attributable to more generalised impairments in object recognition (visual agnosia) or naming (anomia). As a result of the topographic bias in the model, as well as the relative degrees of systematicity among tasks, damage to connections from vision to regions of semantics near phonology impairs visual object naming far more than visual gesturing or tactile naming, as observed in optic aphasia. Moreover, as in optic aphasia, the system is better at generating the name of an action associated with an object than at generating the name of the object itself, because action naming receives interactive support from the activation of action representations. The ability of the model to account for the pattern of performance observed in optic aphasia across the full range of severity of impairment provides support for the claim that semantic representations exhibit graded functional specialisation rather than being entirely amodal or modality-specific.

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