Similarity as a Window on the Dimensions of Object Representation

Abstract

Hebart et al. recently analysed 1.5 million human similarity judgments and found that natural objects are described by a small set of interpretable dimensions. Such large-scale analyses offer new opportunities to characterise how people represent their knowledge, but also challenges, including scaling to even larger data sets and integrating accounts of semantic representation.

Keywords: big data; embedding spaces; knowledge representation; multidimensional scaling; similarity.