This thesis is concerned with the nature and representation of the knowledge acquired in this task.
I present a family of fragment-based "toy" models, that can account for participants' classification performance with a wide range of materials. These toy models can explain transfer performance (where the surface form of the stimuli is changed between training and test), by positing processes which abstract across surface forms during the classification task. I report studies using the guessing game paradigm, showing that grammaticality judgement and the ability to predict successive elements of a test item are reliably associated across a wide range of conditions, consistent with a unitary fragment learning account. I also report simulations with connectionist models, showing that models which are sensitive to arbitrary (nonadjacent) structure do not capture crucial aspects of human performance.
I argue that fragment learning provides a more parsimonious account of artificial grammar learning than accounts based on abstract rules (Reber, 1967), exemplar learning (Brooks & Vokey, 1991), or combinations of these kinds of knowledge.
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