Flexibility of knowledge as a function of practice and explicit instruction

Two experiments used a dynamic control task (Berry & Broadbent, 1984) to examine the flexibility of experientially acquired knowledge. The results suggest that experientially acquired knowledge of this task is represented by a lookup table, not a set of tuned strategies. With practice, transfer to a new task was achieved through an extrapolation procedure. Experiment 2 demonstrated far superior task and transfer performance in participants trained with a combination of experiential practice and model-based knowledge. Transfer to new states was only possible when participants were provided with model-based knowledge through direct instruction. Also, providing model-based knowledge during practice resulted in a more flexible representation compared to providing it before or after practice. Pedagogical implications are discussed

Animation in artificial grammar learning: can animation facilitate learning?

Domangue, Mathews, Sun, Roussel, and Guidry (2004) trained participants to generate valid exemplars from an artificial grammar using either memory-based or model-based processing. Their results showed that learning by memory-based processing resulted in fast but inaccurate performance, while model-based learning resulted in slow but accurate performance. Attempts to integrate both types of training did not result in fast and accurate string generation. Fast and accurate performance was achieved by Sun and Mathews (2004) using a computer animated display to train participants. The current study used a 2x2x2 factorial design to determine why participants who view an animated display of a diagram of the grammar perform well at test. The results suggest that the diagram informs participants of which letters, or chunks of letters can appear in each position, as well as where they cannot appear. Animating the diagram focuses attention on the relevant portion of the complex display and leads to the best performance

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Abstract…...…………………………………………………………………………....v Introduction………..…………………………………………………………………...

Developing rich and quickly accessed knowledge of an artificial grammar

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

Facilitative interactions of model- and experience-based processes: implications for type and flexibility of representation

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