Understanding human high-level spatial memory: An ACT-R model to integrate multi-level spatial cues and strategies

The ability to process and use spatial knowledge is a basic cognitive ability. Two human navigation strategy types (map-based and route-based) relying on two different knowledge representations have been frequently observed. These studies suggest that the first strategy uses a sequential representation and the second uses a hierarchical cluster-based representation. These studies also suggest that humans also routinely use hybrid strategies, and that the ratio between cognitive load and relative utility mediated by situational factors influences, and when modeled, could successfully predict strategy choice. We created an ACT-R model to test these hypotheses by simulating navigation strategies, strategy choices, and strategy switches. This model deepens the empirical findings by defining more clearly the memory mechanisms involved in generating the basic representation types, and by positing a theory of interaction between these types based on ACT-R's associative declarative memory. We believe that such a work provides a concrete example on principles of these biological theories can be implemented and used in cognitive architectures.