A parallel distributed processing model of visual word recognition and pronunciation is described. The model consists of sets of orthographic and phonologlc ~ units and an interlevel of hidden units. Weights on connections between units were modified during a training phase using the back-propa-gation learning algorithm. The model simulates many aspects of human performance, including (a) differences bet~n~.'n words in terms of processing difficulty, (b) pronunciation of novel items, (c) differences between readers in terms of word recognition skill, (d) transitions from beginning to skilled reading, and (e) differences in performance on lexieal decision and naming tasks. The model's behavior early in the learning phase corresponds to that of children acquiring word recognition skills. Training with a smaller number of hidden units produces output characteristic of many dys-lexic readers. Naming is simulated without pronunciation rules, and lexical decisions are simulated without accessing word-level representations. The performance of the model is largely determined by three factors: the nature of the input, a significant fragment of written English; the learning rule, which encodes the implicit structure of the orthography in the weights on connections; and the architecture of the system, which influences the scope of what can be learned. The recognition and pronunciation of words is one of the cen-

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In reading, lexical form-form relations may be more reliable than form-meaning relations. Accordingly, phonological forms (activated by graphic forms) become actual constituents, rather than addenda, of word identification. These considerations suggest that access to phonological forms can precede meaning access in single-word reading in many circumstances. The time course of form and meaning activation during Chinese word reading was tested in 2 primed-naming experiments varying prime type and prime-target stimulus onset asynehrony (SOA). The results showed a sequence of facilitation over SOA: (a) graphic, (b) phonological, (e) semantic. Words with precise meanings produced more rapid semantic priming than words with vague meanings. Graphic prime facilitation at a 43-ms SOA gave way to inhibition at longer SOAs. The onset of graphic inhibition coincided with the onset of phonological facilitation, suggesting a single identification moment. The authors describe an interactive constituency model that accounts for the pattern of data. Writing systems vary in how they represent the phonology of the language they encode. How are these differences reflected in visual word identification? Three classes of hypotheses have been proposed. According to the universal direct access hypothesis, word reading is accomplished by a visual route in all writing systems and in all orthographies. Phonological processing, to the extent that it occurs, is a product of individual reader and word characteristics (e.g., word frequency; Baluch & Besner, 1991; Besner, 1987;

This thesis describes a formal model of a subtype of humour, and the implementation of that model in a program that generates jokes of that subtype. Although there is a great deal of literature on humour in general, very little formal work has been done on puns, and none has been implemented. All current linguistic theories of humour are over-general and not falsifiable. Our model, which is specific, formal, implemented and evaluated, makes a significant contribution to the field. Punning riddles are our chosen subtype of verbal humour, for several reasons. They are very common, they exhibit certain regular structures and mechanisms, and they have been studied previously by linguists. Our model is based on our extensive analysis of large numbers of punning riddles, taken from children's jokebooks. The implementation of the model, JA PE (Joke Analysis and Production Engine), generates punning riddles, from a humour independent lexicon. Pun generation requires much less world knowle...

A central concern of psycholinguistic research is explaining the relative ease or difficulty involved in processing words. In this thesis, we explore the connection between lexical processing effort and measurable properties of the linguistic environment. Distributional information (information about a word's contexts of use) is easily extracted from large language corpora in the form of co-occurrence statistics. We claim that such simple distributional statistics can form the basis of a parsimonious model of lexical processing effort.

1.1. Conceptual systems 621 1.2. Semantic memory 621

Abstract. This paper presents an attempt to point out some problematic issues about the understanding of context. Although frequently used in cognitive sciences or other disciplines, context stays a very ill-defined concept. Our goal is to identify the main components of the context on the basis of the analysis of a corpus of 150 definitions coming mainly from the web in different domains of cognitive sciences and close disciplines. We analyzed this corpus of definitions through two methods, namely LSA [1], [2] and STONE [3], [4], and we conclude that finally the content of all the definitions can be analyzed in terms of few parameters like constraint, influence, behavior, nature, structure and system.

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Distributional information has recently been implicated as playing an important role in several aspects of language ability. Learning the meaning of a word is thought to be dependent, at least in part, on exposure to the word in its linguistic contexts of use. In two experiments, we manipulated subjects ’ contextual experience with marginally familiar and nonce words. Results showed that similarity judgements involving these words were affected by the distributional properties of the contexts in which they were read. The accrual of contextual experience was simulated in a semantic space model, by successively adding larger amounts of experience in the form of item-in-context exemplars sampled from the British National Corpus. The experiments and the simulation