Date Sep 24, 2022
A pretty common model of how we go about thinking is this:
We stick with this model although I see a fair number of instances where the model doesn’t really match observations.
For instance, every morning, when our dogs wake up, they are eager to get started on the day. What this means is that one of the humans gets out of bed, the dogs each grab a toy, and there’s a headlong rush to the other end of the house so the dogs can go outside.
Every morning, the dogs must drop their ‘indoor’ toys and not take them outside.
And every morning, without fail, Eastman goes through a lengthy deliberation. He knows that he must drop the toy before I open the door. He wants to go outside. He also wants to hold his toy and not drop it.
Every single day, Eastman goes through this slow, gradual process. Slowly but surely, he bends his head lower and lower, as whatever is generating the potential driving the decision to drop the toy gradually wins out over whatever is generating the potential to keep the toy in his mouth. And then eventually, he manages to relinquish the toy, and he’s instantly in “open the door, open the door, open the door!” mode.
It seems to me this is much more akin to an analog computation than some digital computation which snaps from holding the toy to letting it go.
And yes, I’m aware that it’s pretty easy for a digital process to simulate an analog one. I’ve done enough software to do continuous system simulation to know how that works. But it’s going the long way ’round to conclude our brains are running a continuous system simulation, as opposed to just directly doing analog computation. When you hear hoofbeats, think horses, not unicorns.