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A Connectionist Model of Auxiliary Doubling in Questions

This paper demonstrates the successful modelling of the attested developmental error of auxiliary doubling in question forms in first language acquisition of English, such as do you don’ t like bananas, generally considered as spell-out of trace. Through distributional analysis of the 18 clause types presented word-by-word (though not coded for word category) as input, a connectionist SRN network learns to predict the correct word category at each instant for each clause type, with a low percentage error (mean error < 2%). The network does, however, exhibit a marked tendency for auxiliary doubling in questions, found to be a consequence of the presence of particular input clause types. It is argued that the different frequencies of different types of aux-doubling error found in the literature can be only be explained in these terms; no rule-based model can account for them. Since the network has no innate linguistic competence, these findings therefore undermine the arguments that aux-doubling necessarily reveals the workings of (a) any innate linguistic component and (b) processes of rule-formation, and challenge the assumption that other spellout-of-trace errors are evidence for a Chomskian Universal Grammar.