Cryptotype, Overgeneralization and Competition: A Connectionist Model of the Learning of English Reversive Prefixes

27 Scopus citations

Abstract

This study examined the role of covert semantic classes or 'cryptotypes' in determining children's overgeneralizations of reversive prefixes such as un- in (Black star);unsqueeze or (Black star);unpress. A training corpus of 160 English verbs was presented incrementally to a backpropagation network. In three simulations, we showed that the network developed structured representations for the semantic cryptotype associated with the use of the reversive prefix un-. Overgeneralizations produced by the network, such as (Black star);unbury or (Black star);unpress, match up well with actual overgeneralizations observed in human children, showing that structured cryptotypic semantic representations underlie this overgeneralization behaviour. Simulation 2 points towards a role of lexical competition in morphological acquisition and overgeneralizations. Simulation 3 provides insight into the relationship between plasticity in network learning and the ability to recover from overgeneralizations. Together, these analyses paint a dynamic picture in which competing morphological devices work together to provide the best possible match to underlying covert semantic structures.

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All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Artificial Intelligence

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