Comparing different Optimality-theoretic learning algorithms: the case of metrical phonology

TL;DR: The authors fed short overt Latin stress patterns to 100 virtual language learners whose grammars consist of a universal set of 12 Optimality-Theoretic constraints, for 50 learners the learning algorithm was Error-Driven Constraint Demotion (EDCD), for the remaining 50 it was the Gradual Learning Algorithm (GLA).

Abstract: We fed short overt Latin stress patterns to 100 virtual language learners whose grammars consist of a universal set of 12 Optimality-Theoretic constraints. For 50 learners the learning algorithm was Error-Driven Constraint Demotion (EDCD), for the remaining 50 it was the Gradual Learning Algorithm (GLA). The EDCD learners did not succeed: they ended up in a grammar that could not reproduce the correct stress pattern. The GLA learners did succeed: they came up with an analysis close to one of the analyses proposed in the literature, namely that by Jacobs (2000). These results add to previous findings that the GLA seems to be a more realistic ingredient than EDCD for models of actual language acquisition.