It is often suggested that the place cells of the hippocampus (and more recently, the grid cells of the entorhinal cortex) furnish a cognitive map. However, this can only be part of the story: (1) the “hippocampal chart” provided by place cells and grid cells differs radically when a rat is placed in different environments and so a higher level organization is needed to link these charts into an overall cognitive map of the rat’s world; and (2) even without a hippocampus a rat can exploit much of the spatial structure of its world. The World Graph (WG) model addressed the former problem, whereas the Taxon Affordance Model (TAM) was developed to address the latter, with the two models being integrated to form the TAM-WG model. Here we provide a new version strengthened in three ways: (1) we relate the TAM model to explicit ideas of executability and desirability; (2) we show how temporal difference learning elegantly supplies “spatial difference learning” to resolve the debate between the local hypothesis and the non-local hypothesis for node selection in the original WG model; and (3) we analyze an explicit example of how the “locometric map” provided by grid cells and place cells can complement the high-level cognitive map given by the WG, demonstrating the importance of navigation algorithms that integrate across multiple levels of spatial organization.
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