This research aims to clarify, by constructing and testing a computer simulation, the use of multiple representations in problem solving, focusing on the role of visual representations. We model the behavior of an economics expert as he teaches some economics principles while drawing a graph on a blackboard. Concurrent verbal protocols are used to guide construction of a production system. The model employs representation-specific data structures and rules. The graph on the blackboard is represented by a bit map; the pictorial working memory (WM ) and long term memory (LTM) representations are node-link structures of a pictorial nature; the auditory WM and LTM representations are node-link structures of a verbal-semantic nature. Pieces from the different representations are linked together on a sequential and temporary basis to form a reasoning and inferencing chain, using cues from LTM and from the external graph. The expert used two representations so as to exploit the unique advantages of each. The graphical representation served as a place holder during reasoning, as well as a summary. The verbal-semantic representation served to give semantic meaning and causal background. Both could initiate reasoning chains. We compare the expert's behavior with novices' trying to learn the same principles.

We describe an extension of CaMeRa, a Computational model of M.ultiple Kepresentations in problem solving (Tabachneck, Leonardo. & Simon, 1994, 1995). CaMeRa provides a genera] architecture for LTM , ST M and their interactions, and illustrates how experts integrate pictorial and verbcd reasoning processes while solving problems. A linked production system and parallel network are used to further resolve the communication between pictorial and verbal knowledge by simulating how a diagram is understood by an expert. Low-level scanning processes and an attention window, based on both psychological and biological evidence, are incorporated into CaMeRa, and productions are developed that allow these processes to interface with the high-level visual rules and representations already in the model. These processes can explain interruptibility during problem solving, and show how understanding is reached when reading a novel diagram.