memorysimulation

Memory simulation refers to the computational modeling of memory processes in order to understand, predict, or reproduce how memories are formed, stored, retrieved, and forgotten. It covers work aimed at explaining human memory phenomena as well as the development of memory systems for artificial agents. In cognitive science, memory simulators implement theories of memory architecture—such as working memory, episodic memory, and semantic memory—and use simulated tasks to compare predictions with human data. In artificial intelligence and robotics, memory simulation often focuses on designing memory architectures that allow agents to retain and retrieve past experiences, enhancing long-term autonomy and planning.

Common approaches include symbolic and rule-based models (ACT-R, Soar, the Atkinson-Shiffrin framework) and connectionist or neural models (Hopfield networks, recurrent neural networks, attention-based memory networks). Modern work frequently blends differentiable memory modules (differentiable neural computers, memory-augmented neural networks) with end-to-end learning. Bayesian and probabilistic models provide explanations of uncertainty in memory encoding and retrieval. Researchers also study memory phenomena such as consolidation, recollection, context effects, interference, and forgetting curves within simulators.

Applications span experimental psychology to test hypotheses about memory, clinical research on aging and amnesia, and the development of AI systems with human-like recall capabilities. Simulations support education, cognitive rehabilitation design, and the evaluation of memory-enhancing strategies. Challenges include capturing the complexity of human memory, validating models against diverse data, and scaling simulations to realistic tasks.