Date of Original Version
Abstract or Description
Optimality Theory (OT) has had a lot of attention from the linguistics research community but also still largely lacks cognitive grounding. We used the ACT-R cognitive architecture to gain greater insight into the cognitive grounding issues that OT needs to address, most notably the GEN process and the learning of the constraint ranking. A generic ACT-R 5.0 model was developed guided by OT principles. The generic model was instantiated in two specific models, one for syllabification and one for past tense formation. Realistic perception data was used to train the models, both were successful in learning the correct constraint ranking for their domain. This result partly bridges the gap between Optimality Theory and ACT-R, providing OT with a better cognitive grounding and ACT-R with better linguistic capabilities.