The leading theory about what we are doing when we dream is that we are sorting through our experiences of the last day or so, consolidating some experiences into memories for long-term storage, and discarding the rest. That does not explain, though, why our dreams are so often so exquisitely weird.
A new theory, the Overfitting Brain Hypothesis (OBH), proposed by Erik Hoel of Tufts University, suggests that perhaps the brain’s sleeping analysis of experiences is akin to machine learning.
The goal of machine learning is to supply an algorithm with a data set, a “training set,” in which patterns can be recognized and from which predictions that apply to other unseen data sets can be derived.
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