  Is analogical reasoning a task that must be learned to solve from scratch by
applying deep learning models to massive numbers of reasoning problems? Or are
analogies solved by computing similarities between structured representations
of analogs? We address this question by comparing human performance on visual
analogies created using images of familiar three-dimensional objects (cars and
their subregions) with the performance of alternative computational models.
Human reasoners achieved above-chance accuracy for all problem types, but made
more errors in several conditions (e.g., when relevant subregions were
occluded). We compared human performance to that of two recent deep learning
models (Siamese Network and Relation Network) directly trained to solve these
analogy problems, as well as to that of a compositional model that assesses
relational similarity between part-based representations. The compositional
model based on part representations, but not the deep learning models,
generated qualitative performance similar to that of human reasoners.
