[D] Optimizing mean loss vs extremal loss

When training neural networks, we typically borrow from the statistics practice of MLE with IID data and minimize the mean of a loss function per sample.

However, to a first approximation, biological natural selection has had to mitigate extreme negative outcomes (that is, prevent death) instead of optimizing average outcomes. I wonder if this accounts for some of the difference in inductive priors between animal brains and our current neural networks.

So who’s run the following experiment (or something similar) …

approximation data death function loss machinelearning mean mle natural negative networks neural networks per practice sample statistics training