Memory is arguably an essential ingredient of general intelligence. The Atkinson–Shiffrin memory model , categorizes memory into “working” and “long-term” memory frameworks, besides an initial sensory register. Working memory (WM) acts as a temporary and limited store of information to be used in cognitive tasks that can be addressed with a small amount of steps. Long-term memory (LTM) instead acts as a more permanent storage of information previously “rehearsed”. We hypothesize that integrating some LTM mechanism should address the issue of catastrophic forgetting commonly faced by computational models capable of working memory. This is the phenomenon in which the performance on previously learned tasks is compromised upon learning a new task. We extend the biologically plausible working memory model AuGMEnT  by integrating a LTM repetition-based thresholding mechanism. We analyse the performance of our model with and without the LTM mechanism on two tasks learned model performance differ from the non-LTM-enabled model in that their performance in the original task does not drop upon learning a new task**. This underlines the clear necessity of LT memory-like mechanisms for continual learning, which is undeniably present in biological agents.
We analyse the performance of our model with and without the LTM mechanism on two tasks learned sequentially. We also compare to the performance of monkeys as a measure of biological plausibility.
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