Early lexical development in a self-organizing neural network
Neural Networks - 2004 Special issue: New developments in self-organizing systems
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Previous psycholinguistic research has debated the nature of the mental representation of verbs and the access of relevant verb information in sentence processing. In this study, we used behavioral and electrophysiological methods to examine the representation of verbs in and out of sentence contexts. In five experiments, word naming and event-related potential (ERP) components were used to measure the speed and the amplitude, respectively, associated with different verb-object combinations that result in different degrees of fit between the verb and its object. Both naming speed and ERP amplitudes (N400) are proven to be sensitive indices of the degree of fit, varying as a function of how well the object fits the verb in terms of selectional restrictions. The results suggest that the semantic features of the verb's arguments are an integral part of the mental representation of verbs, and such information of the verb is accessed and used on-line during sentence processing. Implications of these results are discussed in light of recent computational semantic models that view the lexicon through high-order lexical co-occurrences in language use.