Beyond Frequency and Recency: An Entropy-Based Extension for Modeling Memory Retrieval in Dynamic Environments

This study extends the ACT-R base-level learning equation by incorporating environmental uncertainty, observation duration, and individual learning patterns to better model human memory retrieval in dynamic, multi-object environments. We hypothesize that retrieval efficiency depends not only on frequency and recency of interactions but also on scene entropy, time spent observing, and a learning pattern parameter (α). Using a household task scenario in the AI2-THOR simulator, we analyzed search times as proxies for memory access. Our modified equation integrates entropy to reflect visual uncertainty, scales decay by observation duration, and includes α to capture task-specific learning dynamics. Results from eight participants revealed two distinct retrieval patterns, Increasing–Decreasing and Decreasing–Increasing , reflecting how prior learning influenced search behavior. Participants with shorter search times showed stronger memory retention and α is +1. Model evaluation showed that, compared to the original ACT-R equation (Mean Absolute Error [MAE] = .3580, correlation = .3078), the modified version achieved a lower average MAE of .1868 and a higher correlation of .7776. These results highlight the need for and importance of contextual and temporal factors in memory modeling and support the development of adaptive systems that account for learning patterns in visually complex environments.