Abstract
Five experiments evaluated the contributions of rule, exemplar, fragment, and episodic knowledge in artificial grammar learning using memorization versus hypothesis-testing training tasks. Strings of letters were generated from a biconditional grammar that allows different sources of responding to be unconfounded. There was no evidence that memorization led to passive abstraction of rules or encoding of whole training exemplars. Memorizers instead used explicit fragment knowledge to identify the grammatical status of test items, although this led to chance performance. Successful hypothesis-testers classified at near-perfect levels by processing training and test stimuli according to their rule structure. The results support the episodic-processing account of implicit and explicit learning.
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