A neurophysiological study on probabilistic grammatical learning and sentence processing

Syntactic anomalies reliably elicit P600 effects in natural language processing. A survey of previous work converged on a conclusion that the mean amplitude of the P600 seems to be associated with the goodness of fit of a target word with expectation generated based on already unfolded materials. Based on this characteristic of the P600 effects, the current study aimed to look for evidence indicating the influence of input statistics in shaping grammatical knowledge/representations, and as a result leading to probabilistically-based competition/expectation generation processes of online sentence processing. An artificial grammar learning (AGL) task with 4 different conditions varying in probabilities were used to test this hypothesis. Results from this task indicated graded mean amplitude of the P600 effects across conditions, and the pattern of gradience is consistent with the variation of the input statistics. The use of the artificial language to simulate natural language learning process was further justified with statistically undistinguishable P600 effects elicited in a natural language sentence processing (NLSP) task. Together, the results indicate that the same neural mechanisms are recruited for both syntactic processing of natural language stimuli and sentence strings in an artificial language.