| Description |
Nine-month-olds already display knowledge of the native phonotactics, namely react differently to licit versus illicit sound combinations. Children must thus rely on a remarkably efficient phonotactic learning procedure. What could it look like? Assume that the learner is provided with the typology of OT grammars corresponding to the rankings of a given constraint set. Data come in a stream and consist of licit phonological forms. The learner maintains a current constraint ranking, which is initialized with markedness above faithfulness constraints, yielding a restrictive initial phonotactics. Whenever the current ranking fails at accepting the current piece of data as licit, a slight re-ranking is performed, in the direction of a less restrictive phonotactics. The current piece of data is then discarded and the learner waits for the next piece of data to evaluate the performance of the updated ranking. This learner is called an error-driven ranking algorithm (EDRA) because the learning dynamics is driven by the errors made on the incoming stream of data. As EDRAs do not keep track of previously seen data, they are suited to model the precocious acquisition of phonotactics, prior to the development of the native language lexicon. This talk provides an overview of the computational theory of the EDRA model of the acquisition of phonotactics, focusing on convergence, efficiency, consistency, and restrictiveness.
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