Proceedings of the Annual Meeting of the Cognitive Science Society

In a guessing game, Ss reconstruct a sequence by guessing each successive element of the sequence from a finite set of alternatives, receiving feedback after each guess. A n upper bound on Ss knowledge of the sequence is given by H, the estimated entropy of the numbers of guesses. The method provides a measure of learning independent of material type and distractors, and the resulting data set is very rich. Here, the method is apK plied to artificial grammar learning; Ss were exposed to strings from a finite state grammar and subsequently distinguished between strings that followed or violated the grammar reliably better than Ss who had not seen the learning strings (but who themselves performed at above chance levels). Ss knowledge of the strings, H, reflected both grammaticality and exposure to learning strings, and was correlated with overall judgement performance. For non-grammatical strings, the strings that Ss knew most about were those they found most difficult to c\assify correctly. These results support the hypothesis that fragment knowledge plays an important part in artificial grammar learning, and we suggest that the guessing game paradigm is a useful tool for studies of learning and memory in general.