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We develop the few-shot continual learning task from first principles andhypothesize an evolutionary motivation and mechanism of action for executivefunction as a contrastive value policy which resamples and relabels perceptiondata via hindsight summarization to minimize attended prediction error, similarto an online prompt engineering problem. This is made feasible by the use of amemory policy and a pretrained network with inductive biases for a grammar oflearning and is trained to maximize evolutionary survival. We show how thismodel of executive function can be used to implement hypothesis testing as astream of consciousness and may explain observations of human few-shot learningand neuroanatomy.

Executive Function: A Contrastive Value Policy for Resampling and Relabeling Perceptions via Hindsight Summarization?