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From a continuous stream of experience, how does the human brain determine what parts to remember and what to forget? It is extensively documented that humans are prone to systematic biases in these decisions. Such systematic biases are often construed as byproducts of adaptive processes. We argue that the computational resource constraints on memory can be formalised in the normative framework of lossy compression and that optimal adaptation to the constraints can be achieved by exploiting a generative model of the environment for compression. Recent advances in machine learning yielded powerful tools to approximate such solutions. In this study, we harness these advances to show that generative compression can explain a wide variety of memory phenomena including the effects of domain expertise on recall, gist based distortions in recalling lists of semantically related words and the influence of contextual cues in memory for hand drawn sketches.

Rate distortion trade-off in human memory