Retrieval induced forgetting in a non-monotonic hippocampal model

Abstract Retrieval induced forgetting (RIF) occurs when the retrieval of one item negatively impacts the recall probability of related items stored in memory (Anderson et al., 1994). Recently, Ritvo et al. (2023) demonstrated RIF emerges in a neural network model equipped with non-monotonic plasticity. Their finding supports the non-monotonic plasticity hypothesis (NMPH; Ritvo et al., 2019): the theory that connection changes in the brain follow a “U” shaped function of post-synaptic stimulation. Here, we apply a unique implementation of non-monotonic plasticity to a neural network model of an idealized hippocampus (HPC) and evaluate it with an adaptation of a classic RIF task. The model evidences the behavioral and representational characteristics of RIF, replicating Ritvo et al. (2023). As a monotonic baseline model failed these tests, we provide evidence of non-monotonic plasticity’s sufficiency for RIF. In addition to demonstrating the NMPH is robust to multiple implementations and evaluative paradigms, we conduct additional analysis to provide a mechanistic explanation for how non-monotonic plasticity brings about RIF. Lastly, we evaluate the model with an expansion of RIF: reverse RIF. The model fails this final test, raising questions for future research on the necessary parameters of non-monotonic plasticity and whether it must pair with complementary processes in the brain.