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.

People are often taught using a combination of instruction and practice. In prior research, we have distinguished between model-based knowledge (i.e., acquired from explicit instruction) and experience-based knowledge (i.e., acquired from practice), and have argued that the issue of how these types of knowledge (and associated learning processes) interact has been largely neglected. Two experiments explore this issue using a dynamic control task. Results demonstrate the utility of providing model-based knowledge before practice with the task, but more importantly, suggest how this information improves learning. Results also show that learning in this manner can lead to "costs" such as slowed retrieval, and that this knowledge may not always transfer to new task situations as well as experientially acquired knowledge. Our findings also question the assumption that participants always acquire a highly specific "lookup" table representation while learning this task. We provide an alternate view and discuss the implications for theories of learning.

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