Proceedings of the Annual Meeting of the Cognitive Science Society

Most work in learnability theory assumes that both the environment (the data to be learned) and the learning mechanism are static. In the case of children, however, this is an unrealistic assumption. First-language learning occurs, for example, at precisely that point in time when children undergo significant developmental changes. In this paper I describe the results of simulations in which network models are unable to learn a complex grammar when both the network and the input remain unchanging. However, when either the input is presented incrementally, or—more realistically—the network begins with limited memory that gradually increases, the network is able to learn the grammar. Seen in this light, the early limitations in a learner may play both a positive and critical role, and make it possible to master a body of knowledge which could not be learned in the mature system.