Developing rich and quickly accessed knowledge of an artificial grammar

Bill Sallas, Department of Psychology, Office of Applied Cognition, Louisiana State University, Baton Rouge, Louisiana 70803, USA.
Robert C. Mathews
Sean M. Lane
Ron Sun


In contrast to prior research, our results demonstrate that it is possible to acquire rich, highly accurate, and quickly accessed knowledge of an artificial grammar. Across two experiments, we trained participants by using a string-edit task and highlighting relatively low-level (letters), medium-level (chunks), or high-level (structural; i.e., grammar diagram) information to increase the efficiency of grammar acquisition. In both experiments, participants who had structural information available during training generated more highly accurate strings during a cued generation test than did those in other conditions, with equivalent speed. Experiment 2 revealed that structural information enhanced acquisition only when relevant features were highlighted during the task using animation. We suggest that two critical components for producing enhanced performance from provided model-based knowledge involve (1) using the model to acquire experience-based knowledge, rather than using a representation of the model to generate responses, and (2) receiving that knowledge precisely when it is needed during training.