The Guessing Game : A Paradigm for Arti cial Grammar

In a guessing game, Ss reconstruct a sequence by guessing each successive element of the sequence from a nite set of alternatives, receiving feedback after each guess. An 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 applied to artiicial grammar learning; Ss were exposed to strings from a nite 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, reeected 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 diicult to classify correctly. These results support the hypothesis that fragment knowledge plays an important part in artii-cial grammar learning, and we suggest that the guessing game paradigm is a useful tool for studies of learning and memory in general.