Sequence-to-sequence models with attention mechanistically map to the architecture of human memory search.

ABSTRACT: Past work has long recognized the important role of context in guiding how humans search their memory. While context-based memory models can explain many memory phenomena, it remains unclear why humans develop such architectures over possible alternatives in the first place. In this work, we demonstrate that foundational architectures in neural machine translation - specifically, recurrent neural network (RNN)-based sequence-to-sequence models with attention - exhibit mechanisms that directly correspond to those specified in the Context Maintenance and Retrieval (CMR) model of human memory. Since neural machine translation models have evolved to optimize task performance, their convergence with human memory models provides a deeper understanding of the functional role of context in human m

SUBMITTER: Salvatore N 

PROVIDER: S-EPMC12521410 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature