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Exploring the Structure of Spatial Representations
ABSTRACT
It has been suggested that the map-like representations that support human spatial memory
are fragmented into sub-maps with local reference frames, rather than being unitary
and global. However, the principles underlying the structure of these "cognitive maps" are
not well understood.We propose that the structure of the representations of navigation
space arises from clustering within individual psychological spaces, i.e. from a process
that groups together objects that are close in these spaces. Building on the ideas of representational
geometry and similarity-based representations in cognitive science, we formulate
methods for learning dissimilarity functions (metrics) characterizing participants'
psychological spaces. We show that these learned metrics, together with a probabilistic
model of clustering based on the Bayesian cognition paradigm, allow prediction of participants'
cognitive map structures in advance. Apart from insights into spatial representation
learning in human cognition, these methods could facilitate novel computational tools
capable of using human-like spatial concepts. We also compare several features influencing
spatial memory structure, including spatial distance, visual similarity and functional
similarity, and report strong correlations between these dimensions and the grouping
probability in participants' spatial representations, providing further support for clustering
in spatial memory.
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