Proceedings of the Annual Meeting of the Cognitive Science Society

In this work, we propose a neural network model for free recall that draws direct parallels between neural machine translation (NMT) and cognitive models of memory search, specifically the Context Maintenance and Retrieval (CMR) model. We hypothesize that NMT advancements such as attention mechanisms (Luong et al., 2015) closely resemble how humans reactivate prior contexts (“mental time travel”; Tulving, 1985). To demonstrate these parallels, we train a seq2seq model with attention as a cognitive model of memory search and evaluate behavior against human free recall data. We find that the model can capture typical free recall patterns previously observed (Kahana et al., 2022); and after optimization, the model demonstrates the same optimal behavior as previously derived by the CMR model (Zhang, Griffiths, & Norman, 2023). Performing an ablation study, we demonstrate that behavioral differences between models with and without attention align with impaired behavior observed in hippocampal amnesia patients.