A model of rapid phonotactic generalization

The phonotactics of a language describes the ways in which the sounds of the language combine to form possible mor-phemes and words. Humans can learn classes, generalizing across sounds (e.g., “words can end in a voiced stop”). More-over, they rapidly acquire these general-izations, even before they acquire sound-specific patterns. We present a probabilis-tic model intended to capture this early-abstraction phenomenon. The model rep-resents both abstract and concrete gen-eralizations in its hypothesis space from the outset of learning. This—combined with a parsimony bias in favor of compact descriptions of the input data—leads the model to favor rapid abstraction in a way similar to human learners.