Many theorists assume that the cognitive system is composed of a collection of encapsulated processing components or modules, each dedicated to performing a particular cognitive function. On this view, selective impairments of cognitive tasks following brain damage, as evidenced by double dissociations, are naturally interpreted in terms of the loss of particular processing components. By contrast, the current investigation examines in detail a double dissociation between concrete and abstract word reading after damage to a connectionist network that pronounces words via meaning and yet has no separable components (Plaut & Shallice, 1993). The functional specialization in the network that gives rise to the double dissociation is not transparently related to the network's structure, as modular theories assume. Furthermore, a consideration of the distribution of effects across quantitatively equivalent individual lesions in the network raises specific concerns about the interpretation of...

this article, in conjunction with those developed previously (Plaut et al., 1996; Seidenberg & McClelland, 1989), illustrate how connectionist computational principles---distributed representation, structure-sensitive learning, and interactivity---can provide insight into central empirical phenomena in normal and impaired lexical processing. Moreover, they make it clear that distinctions in the function of the lexical system---as manifest in the behaviour of experimental subjects--- need not re#ect corresponding distinctions in the structure of the system. Thus, networks exhibit word-frequency effects and word/nonword discrimination without word representations, and spelling --sound consistency effects without separate mechanisms for regular and exception items. In this way, gaining insight into the structure and function of the cognitive system by observing its normal and impaired behaviour ---the central goal of cognitive psychology and neuropsycho logy---may depend critically on developing theories and explicit simulations in the context of a speci#c computational framework that relates structure to function

The development of reading skill and bases of developmental dyslexia were explored using connectionist models. Four issues were examined: the acquisition of phonological knowledge prior to reading, how this knowledge facilitates learning to read, phonological and non phonological bases of dyslexia, and effects of literacy on phonological representation. Compared with simple feedforward networks, representing phonological knowledge in an attractor network yielded improved learning and generalization. Phonological and surface forms of developmental dyslexia, which are usually attributed to impairments in distinct lexical and nonlexical processing “routes,” were derived from different types of damage to the network. The results provide a computationally explicit account of many aspects of reading acquisition using connectionist principles.

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

xi 1 Introduction 1 1.1 Intuitions and Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Previous Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.1 The Classical Dual Route Model . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.2 Seidenberg and McClelland 1989 . . . . . . . . . . . . . . . . . . . . . . 10 1.2.3 Plaut and Shallice 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2.4 Plaut et al. 1996: Naming . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2.5 Bullinaria 1996 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.2.6 Plaut 1997: Lexical Decision . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.2.7 Harm and Seidenberg 1998: Naming . . . . . . . . . . . . . . . . . . . . 16 1.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2 A New Computational Model 18 2.1 Principles . . . . . . . . . . . . . . . . . . . . . . . . ...

and McClelland (1989) model are addressed. The model's performance differs from that of people in ways that are predictable from an understanding of the limitations of the implementation. The principal limitations are the size of the training corpus and the phonological representation. The issue of pseudohomophone effects is discussed, and Besner et al.'s new data are shown to be consistent with the Seidenberg and McClelland account of lexical decision. Besner, Twilley, McCann, and Seergobin's (1990) broad critique of the Seidenberg and McClelland (1989) model merits closer inspection; we think the model stands up quite well. The model correctly simulates a broad range of behavioral phenomena; its performance departs from that of people in ways that are predictable from an understanding of limitations of the implementation. These limitations do not call into question any of the basic assumptions of the model. Size of the Training Corpus Besner et al. (1990) noted that the model does not perform

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 describes simulation experiments demonstrating that a unitary processing system in the form of a connectionist network is capable of learning to read exception words and pronounceable nonwords aloud. We trained such a network on the 3000 word corpus used by Seidenberg and McClelland (1989). After training, the network was able to read over 99 % of the training corpus correctly. When tested on the lists of pronounceable nonwords used in several experiments, its accuracy was closely comparable to that displayed by human subjects. The work addresses the ongoing debate about the nature of the mechanisms that are used in reading words aloud. One view, defended most recently by Coltheart, Curtis, Atkins, and Haller (1993), states that adequate performance on both pronounceable nonwords and exception words depends on the use of two separate mechanisms, one that applies rules of grapheme phoneme correspondence and another that retrieves pronunciations specific to particular familiar words. An alternative view, expressed by Seidenberg and McClelland, is that a single system may be capable of learning to read both kinds of letter strings. The work relates more generally to the ongoing debate about the nature of the processing systems underlying human language use. The question is, should these systems be viewed as systems that learn and apply an explicit system of rules, augmented where necessary with an explicit enumeration of exceptions;

In alphabetic writing systems like English or French, many words are composed of more letters than phonemes (e.g. BEACH is composed of five letters and three phonemes, i.e. /biJ/).

In reading research, morphological processing and monomorphemic word identification have generally been treated separately. We describe a computational model that brings both kinds of reading together within a single framework. This model assumes that word knowledge—the orthography, phonology, and meaning of words—accumulates with experiences with individual words and that this knowledge is reflected in two functionally different aspects of word processing—familiarity and availability. We report simulations that demonstrate that the model accounts both for classical effects of frequency and consistency in simple word reading and for morphological effects in the reading of complex words. The morphology simulations naturally capture a distinction between inflectional and derivational morphology without defining this distinction a priori. We discuss the implications of our model for general issues in reading, including individual differences in reading ability.