  A central challenge for cognitive science is to explain how abstract concepts
are acquired from limited experience. This has often been framed in terms of a
dichotomy between connectionist and symbolic cognitive models. Here, we
highlight a recently emerging line of work that suggests a novel reconciliation
of these approaches, by exploiting an inductive bias that we term the
relational bottleneck. In that approach, neural networks are constrained via
their architecture to focus on relations between perceptual inputs, rather than
the attributes of individual inputs. We review a family of models that employ
this approach to induce abstractions in a data-efficient manner, emphasizing
their potential as candidate models for the acquisition of abstract concepts in
the human mind and brain.
