Maturation and Learnability in Parametric Systems

Recent work in parametric language learning has showed that even very small systems of linguistically plausible parameters pose very serious problems for error-driven and conservative learning algorithms. It has been argued that such problems may be solved by considering that different parameters may become available for reset ring at different times, as an effect of biological maturation. This article presents a general framework for studying the effects of the Maturation Hypothesis on the problem of language learning, parametrically conceived, and offers a general method for finding all maturational solutions (where some exist) for any parametric hypothesis space and any learning algorithm that differs from Gibson and Wexier's TLA only in the number of parameters that can be reset at each step. Implications for research in natural language acquisition are discussed in the concluding section.