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How humans and machines make sense of current inputs for relation reasoningand question-answering while putting the perceived information into context ofour past memories, has been a challenging conundrum in cognitive science andartificial intelligence. Inspired by human brain's memory system and cognitivearchitectures, we propose a PMI framework that consists of perception, memoryand inference components. Notably, the memory module comprises working andlong-term memory, with the latter endowed with a higher-order structure toretain extensive and complex relational knowledge and experience. Through adifferentiable competitive write access, current perceptions update workingmemory, which is later merged with long-term memory via outer productassociations, reducing information conflicts and averting memory overflow. Inthe inference module, relevant information is retrieved from two separatememory origins and associatively integrated to attain a more comprehensive andprecise interpretation of current perceptions. We exploratively apply our PMIto improve prevailing Transformers and CNN models on question-answering taskslike bAbI-20k and Sort-of-CLEVR datasets, as well as detecting equilateraltriangles, language modeling and image classification tasks, and in each case,our PMI enhancements consistently outshine their original counterpartssignificantly. Visualization analyses reveal that relational memoryconsolidation, along with the interaction and integration of information fromdiverse memory sources, substantially contributes to the model effectiveness oninference tasks.

A Framework for Inference Inspired by Human Memory Mechanisms