We survived comparisons to hydraulics, clockwork, steam, circuits, information processing, and networks. I'm positive we'll survive comparisons to attention and models too.
If it's an argument from pedantism, let us remind ourselves to be language descriptivist than language prescriptivists. Prescriptivists never win.
So instead of being a useful, if imperfect, analogy from an outside domain, trying to use ML attention to understand biological attention just results in confusion (like this article).
Neuroscientists aren't going to tell ML researchers they can't use "attention", but nor are they going to pay attention to them (pun intended). Neural networks are almost always too divergent from biology to shed light on real neurons.
1. https://www.thoughtco.com/polysemy-words-and-meanings-169164...