Mapping Descriptive Models of Graph Comprehension into Requirements for a Computational Architecture: Need for Supporting Imagery Operations

Psychologists have developed many models of graph comprehension, most of them descriptive, some computational. We map the descriptive models into requirements for a cognitive architecture that can be used to build predictive computational models. General symbolic architectures such as Act-R and Soar satisfy the requirements except for those to support mental imagery operations required for many graph comprehension tasks. We show how Soar augmented with DRS, our earlier proposal for diagrammatic representation, satisfies many of the requirements, and can be used for modeling the comprehension and use of a graph requiring imagery operations. We identify the need for better computational models of the perception operations and empirical data on their timing and error rates before predictive computational models can become