Social and Behavioral Sciences Commons^™

Computational Modeling Of Learning Biases In Stress Typology, Robert D. Staubs

Doctoral Dissertations

This dissertation demonstrates a strong connection between the frequency of stress patterns and their relative learnability under a wide class of learning algorithms. These frequency results follow from hypotheses about the learner's available representations and the distribution of input data. Such hypotheses are combined with a model of learning to derive distinctions between classes of stress patterns, addressing frequency biases not modeled by traditional generative theory. I present a series of results for error-driven learners of constraint-based grammars. These results are shown both for single learners and learners in an iterated learning model. First, I show that with general n …