A distributed basis for analogical mapping

Abstract

We are concerned with the practical fea- sibility of the neural basis of analogical map- ping. All existing connectionist models of ana- logical mapping rely to some degree on local- ist representation (each concept or relation is represented by a dedicated unit/neuron). These localist solutions are implausible because they need too many units for human-level compe- tence or require the dynamic re-wiring of net- works on a sub-second time-scale.Analogical mapping can be formalised as finding an approximate isomorphism between graphs representing the source and target con- ceptual structures. Connectionist models of analogical mapping implement continuous heuristic processes for finding graph isomor- phisms. We present a novel connectionist mechanism for finding graph isomorphisms that relies on distributed, high-dimensional representations of structure and mappings. Consequently, it does not suffer from the prob- lems of the number of units scaling combinato- rially with the number of concepts or requiring dynamic network re-wiring.