A discriminative information-theoretical analysis of the regularity gradient in inflectional morphology

Over the last decades, several independent lines of research in morphology have questioned the hypothesis of a direct correspondence between sublexical units and their mental correlates. Word and paradigm models of morphology shifted the fundamental part-whole relation in an inflection system onto the relation between individual inflected word forms and inflectional paradigms. In turn, the use of artificial neural networks of densely interconnected parallel processing nodes for morphology learning marked a radical departure from a morpheme-based view of the mental lexicon. Lately, in computational models of Discriminative Learning, a network architecture has been combined with an uncertainty reducing mechanism that dispenses with the need for a one-to-one association between formal contrasts and meanings, leading to the dissolution of a discrete notion of the morpheme.The paper capitalises on these converging lines of development to offer a unifying information-theoretical, simulation-based analysis of the costs incurred in processing (ir)regularly inflected forms belonging to the verb systems of English, German, French, Spanish and Italian. Using Temporal Self-Organising Maps as a computational model of lexical storage and access, we show that a discriminative, recurrent neural network, based on Rescorla-Wagner's equations, can replicate speakers' exquisite sensitivity to widespread effects of word frequency, paradigm entropy and morphological (ir)regularity in lexical processing. The evidence suggests an explanatory hypothesis linking Word and paradigm morphology with principles of information theory and human perception of morphological structure. According to this hypothesis, the ways more or less regularly inflected words are structured in the mental lexicon are more related to a reduction in processing uncertainty and maximisation of predictive efficiency than to economy of storage.