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Computational Cognitive Models of Spatial Memory in Navigation Space: A Review
Spatial memory refers to the part of the memory system that encodes, stores, recognizes and recalls spatial
information about the environment and the agent's orientation within it. Such information is required to
be able to navigate to goal locations, and is vitally important for any embodied agent, or model thereof, for
reaching goals in a spatially extended environment.
In this paper, a number of computationally implemented cognitive models of spatial memory are reviewed
and compared. Three categories of models are considered: symbolic models, neural network models, and
models that are part of a systems-level cognitive architecture. Representative models from each category
are described and compared in a number of dimensions along which simulation models can dier (level of
modeling, types of representation, structural accuracy, generality and abstraction, environment complexity),
including their possible mapping to the underlying neural substrate.
Neural mappings are rarely explicated in the context of behaviourally validated models, but they could be
useful to cognitive modeling research by providing a new approach for investigating a model's plausibility.
Finally, suggested experimental neuroscience methods are described for verifying the biological plausibility
of computational cognitive models of spatial memory, and open questions for the eld of spatial memory
modeling are outlined.
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