ML attention is nothing like human attention. I think it’s madness to attempt to map concepts from one field we barely understand to another field we also barely understand just because they use overlapping language.
Having done some research into human attention, I have to agree with Hommel et al: No one knows what attention is [1].
In current ANNs "attention" is quite well defined: how to weigh some variables based on other variables. But anthropomorphizing such concepts indeed muddies things more than it clarifies. Including calling interconnected summation units with non-linear transformations "neural networks".
But such (wrong) intuition pumping terminology does attract, well, attention, so they get adopted.
In current ANNs "attention" is quite well defined: how to weigh some variables based on other variables. But anthropomorphizing such concepts indeed muddies things more than it clarifies. Including calling interconnected summation units with non-linear transformations "neural networks".
But such (wrong) intuition pumping terminology does attract, well, attention, so they get adopted.
[1] https://link.springer.com/article/10.3758/s13414-019-01846-w