Two Computational Approaches to Visual Analogy: Task-Specific Models Versus Domain-General Mapping.

Abstract

Advances in artificial intelligence have raised a basic question about human intelligence: Is human reasoning best emulated by applying task-specific knowledge acquired from a wealth of prior experience, or is it based on the domain-general manipulation and comparison of mental representations? We address this question for the case of visual analogical reasoning. Using realistic images of familiar three-dimensional objects (cars and their parts), we systematically manipulated viewpoints, part relations, and entity properties in visual analogy problems. We compared human performance to that of two recent deep learning models (Siamese Network and Relation Network) that were directly trained to solve these problems and to apply their task-specific knowledge to analogical reasoning. We also developed a new model using part-based comparison (PCM) by applying a domain-general mapping procedure to learned representations of cars and their component parts. Across four-term analogies (Experiment 1) and open-ended analogies (Experiment 2), the domain-general PCM model, but not the task-specific deep learning models, generated performance similar in key aspects to that of human reasoners. These findings provide evidence that human-like analogical reasoning is unlikely to be achieved by applying deep learning with big data to a specific type of analogy problem. Rather, humans do (and machines might) achieve analogical reasoning by learning representations that encode structural information useful for multiple tasks, coupled with efficient computation of relational similarity.