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.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Artificial Intelligence