Computational cognitive models of spatial memory in navigation space: A review

Tamas Madl, Ke Chen, Daniela Montaldi, Robert Trappl

    Research output: Contribution to journalArticlepeer-review

    177 Downloads (Pure)


    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 differ (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 behaviorally 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 field of spatial memory modeling are outlined.
    Original languageEnglish
    Pages (from-to)18-43
    Number of pages25
    JournalNeural Networks
    Publication statusPublished - May 2015


    • Spatial memory models, Computational cognitive modeling


    Dive into the research topics of 'Computational cognitive models of spatial memory in navigation space: A review'. Together they form a unique fingerprint.

    Cite this