Visual-Imagery-Based Analogical Construction in Geometric Matrix Reasoning Task

Analogical reasoning fundamentally involves exploiting redundancy in a given task, but there are various strategies for an intelligent agent to identify and exploit such redundancy, often resulting in very different levels of reasoning ability. We explore such variations of analogy in geometric reasoning task, namely the Raven's Progressive Matrices. We show how different analogical constructions used by the same basic imagery-based computational model -- varying only in how they "slice" a matrix problem into parts and search within/across these parts -- achieve very different test scores, substantially overlapping the range of human performance. Our findings suggest that the ability to build effective high-level analogical constructions is as important as competencies in low-level reasoning, which raises interesting questions about the extent to which building the "right" analogies contributes to individual differences in human reasoning and how intelligent agents might learn to build among different constructions in the first place.