Proceedings of the Annual Meeting of the Cognitive Science Society

Responding to similarity, difference, and relative magnitude isubiquitous in the animal kingdom. However, humans seemunique in the ability to represent relative magnitude andsimilarity as abstract relations that take arguments (e.g.,greater-than (x,y)). While many models use structuredrelational representations of magnitude and similarity, littleprogress has been made on how these representations arise.Models that use these representations assume access tocomputations of similarity and magnitude a priori. We detail amechanism for producing invariant responses to “same”,“different”, “more”, and “less” which can be exploited tocompute similarity and magnitude as an evaluation operator.Using DORA (Doumas, Hummel, & Sandhofer, 2008), theseinvariant responses can serve to learn structured relationalrepresentations of relative magnitude and similarity from pixelimages of simple shapes.