Good go players have a lot of explicit knowledge, and give names to the concepts they have. See http://senseis.xmp.net/?ReferenceSection

When playing, they have a strategy, which they could explain to other go players. They don't just recognize patterns or do brute-force look-ahead. The same is true for good chess players.

There's typically an extra layer (or more) with humans. "Because this puts your Bishop in wasabi which makes it harder for your opponent to extract his Kinglet, making it more likely to win."

Deep nets have layers too. We need to assign names to the nodes of the layers. That's the challenge of interpretability in neural nets.

Explanations for choices have nothing to do with nodes or layers.

No, it isn't, exactly or inexactly.