Proceedings of the Annual Meeting of the Cognitive Science Society

Similarity and generalization are often assumed to reflect distance in an underlying representational space. In semantic networks, however, distance can be defined by both shortest paths or by multiple indirect pathways. Previous research has shown that non-shortest, higher-order paths influence similarity judgements. Here, we extend these findings by re-analyzing a publicly available dataset to compare shortest-path models with models integrating over discounted higher-order paths. The latter better accounted for similarity judgements; error analyses indicate that this advantage arose from integrating multiple indirect paths rather than relying on shortest connections. These random-walk models implement the same core computation as the successor representation (SR), which has been proposed to support human generalization. Consistently, an SR-based model outperformed alternatives in accounting for inductive generalization judgements from the same dataset. These findings suggest that similarity and generalization are both shaped by higher-order connectivity between concepts.