Representing spatial relations with fractional binding

AbstractWe propose a cognitively plausible method for representing and querying spatial relationships in a neural architecture. This technique employs a fractional binding operator that captures continuous spatial information in spatial semantic pointers (SSPs). We propose a model that takes an image with several objects, parses the image into an SSP memory representation, and answers queries about the objects. We demonstrate that our model allows us to not only store and extract objects and their spatial information, but also perform queries based on location and in relation to other objects. We show that we can query images with 2, 3, and 4 objects with relative spatial locations. We also show that the model qualitatively reproduces Kosslyn's famous map experiment.


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