Competition Between Memory Updating and Differentiation Emerges from Intrinsic Network Dynamics

Abstract

When an event occurs that is similar to a previous experience, the original episodic memory can be modified with new information (updating) or a new memory can be encoded (differentiation). Prediction errors, the deviation between expected and actual stimuli, are believed to mediate the competition between updating and differentiation, but the underlying mechanisms remain unclear. Here, we present a new analysis of experimental studies (Boeltzig, Liedtke, & Schubotz, 2025; Liedtke et al., 2025) that examine recognition memory and cued recall of similar conversations. The original version was recognized more confidently than the modified version, and the recognition confidence for modified versions showed a U-shaped dependence on the prediction error. Furthermore, the larger the prediction error, the more frequently participants retrieved two versions during cued recall. To account for these results, we propose a computational model based on a modified Hopfield network, which encodes the original and modified versions sequentially and weights the encoding of new patterns by the prediction error. The model shows that (1) similar new memories interfere with previous ones (updating) while dissimilar ones are stored separately (differentiation), (2) interference from similar representations leads to reduced memory accuracy and lower-confidence recognition, and (3) the encoding weight must be modulated by the prediction error to account for the experimental data. Our modeling results show that prediction-error-driven competition between updating and differentiation can emerge from intrinsic network dynamics alone.