Dissertation - Michael Becker

Speakers chose voicing alternations when presented with novel nouns more often withpoly-syllables than with mono-syllables, and with non-coronals more often than coronals,reflecting the trends in the lexicon.However, they did not choose more alternatingresponses when the rightmost vowel of the novel noun was high or back, ignoring the trendfor more alternations in those conditions in the lexicon. The proposal made here was thatlexical trends are learned in terms of typologically-responsible constraints, which are partof UG. The prediction this makes is that there is a necessary correlation between the spaceof regular phonological processes as observed in the world languages on one hand, andthe space of irregular trends that speakers can extract from their lexicon on the other hand,since both kinds of phenomena stem from a single posited set of Universal Constraints.A statistical analysis of the Turkish lexicon was offered, and contrasted with the resultsfrom the experiment, showing that speakers ignored a correlation between vowel qualityand the voicing of a neighboring vowel. The experimental results were contrasted with theresults of the MGL simulation (Albright & Hayes 2002, 2003, 2006), which over-learnedthe Turkish data, projecting the vowel quality effects that humans ignored.The conclusion was that a general-purpose statistical learner could not reproduce thebehavior that humans display, and that a successful theory of lexical learning must combinethe ability to learn lexical trends with UG-based biases. The proposed learner identifiedconflicting lexical behaviors in the lexicon and resolved the conflict by cloning constraints.Once constraints are cloned, each clone keeps a list of the words it governs, assuringthat existing words behave consistently. At the same time, the clones can be used in ageneralized way, referring only to the proportion of words that are governed by each clone,to project the lexical trend onto novel words.The resulting learner simulated the process of learning a lexicon without relying ongeneral-purpose pattern matching. Rather, it used a set of Universal Constraints that wereaugmented by the ability to clone constraints. In the Turkish case, the simulated learner72