Modeling the effect of chained study in transitive inference

A hallmark of human memory is the ability to integrate discrete experiences into cognitive maps. A fundamental form of this integration is transitive inference (TI), in which overlapping premises (A < B, B < C) are integrated into a unified representation of a relational hierarchy (A < B < C). Few existing theories provide a mechanistic account of this construction of relational knowledge and how it is shaped by different training conditions. This study builds on recent findings that TI is facilitated by chaining of overlapping premises, with a new behavioral experiment confirming an advantage over non-overlapping sequences matched for premise frequency and spacing. A subsequent simulation study shows that the chaining effect is captured by a particle filter which performs approximate Bayesian inference about the latent hierarchy. These results provide a better understanding of how chaining shapes the construction of relational knowledge in the face of uncertainty and forgetting