Most words in English are ambiguous between different interpretations; words can mean different things in different contexts. We investigate the implications of different types of semantic ambiguity for connectionist models of word recognition. We present a model in which there is competition to activate distributed semantic representations. The model performs well on the task of retrieving the different meanings of ambiguous words, and is able to simulate data reported by Rodd, Gaskell, and Marslen‐Wilson [J. Mem. Lang. 46 (2002) 245] on (...) how semantic ambiguity affects lexical decision performance. In particular, the network shows a disadvantage forwords with multiple unrelated meanings (e.g., bark) that coexists with a benefit for words with multiple related word senses (e.g., twist). The ambiguity disadvantage arises because of interference between the different meanings, while the sense benefit arises because of differences in the structure of the attractor basins formed during learning. Words with few senses develop deep, narrow attractor basins, while words with many senses develop shallow, broad basins. We conclude that the mental representations of word meanings can be modelled as stable states within a high‐dimensional semantic space, and that variations in the meanings of words shape the landscape of this space. (shrink)

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A number of recent studies have examined the effects of phonological variation on the perception of speech. These studies show that both the lexical representations of words and the mechanisms of lexical access are organized so that natural, systematic variation is tolerated by the perceptual system, while a general intolerance of random deviation is maintained. Lexical abstraction distinguishes between phonetic features that form the invariant core of a word and those that are susceptible to variation. Phonological inference relies on the (...) context of surface changes to retrieve the underlying phonological form. In this article we present a model of these processes in speech perception, based on connectionist learning techniques. A simple recurrent network was trained on the mapping from the variant surface form of speech to the underlying form. Once trained, the network exhibited features of both abstraction and inference in its processing of normal speech, and predicted that similar behavior will be found in the perception of nonsense words. This prediction was confirmed in subsequent research (Gaskell & Marslen-Wilson, 1994). (shrink)

We address the notion of integration of new memory representations and the potential dependence of this phenomenon on sleep, in light of recent findings on the lexicalization of spoken words. A distinction is introduced between measures tapping directly into the strength of the newly acquired knowledge and indirect measures assessing the influence of this knowledge on spoken word identification.

I respond to Norris et al.'s criticism of Gaskell and Marslen- Wilson (1997). When the latter's network is tested in circumstances comparable to the Merge simulations in the target article, it produces the desired pattern of results. In another area of potential feedback in spoken word processing, aspects of lexical content influence word recognition and our network provides a simple explanation of why such effects emerge. It is unclear how such effects would be accommodated by Merge.

Learning a mapping involves finding regularities in a training set and generalization to novel patterns. Clark & Thornton's type distinction has been discussed in terms of generalization, but has limited value in this respect. However, in terms of detection of regularities in the training set, the distinction is more valid, as it provides a measure of complexity and correlates with the size of search space.