We present the Bayesian Information-Theoretical (BIT) model of lexical processing: A mathematical model illustrating a novel approach to the modelling of language processes. The model shows how a neurophysiological theory of lexical processing relying on Hebbian association and neural assemblies can directly account for a variety of eects previously observed in behavioral experiments. We develop two information-theoretical measures of the distribution of usages of a word or morpheme. These measures are calculated through unsupervised means from corpora. We show that our measures succesfully predict responses in three visual lexical decision datasets investigating the processing of in ectional morphology in Serbian and English languages, and the eects of polysemy and homonymy in English. We discuss how our model provides a neurophysiological grounding for the facilitatory and inhibitory eects of dierent types of lexical neighborhoods. In addition, our results show how, under a model based on neural assemblies, distributed patterns of activation naturally result in the arisal of discrete symbol-like structures. Therefore, the BIT model oers a point of reconciliation in the debate between distributed connectionist and discrete localist models. Finally, we argue that the modelling framework exemplied by the BIT model, is a powerful tool for integrating the different levels of the description of the human language processing system
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