This is a fairly good summary of my argument against why we are in a simulation.

I’m certain (indeed more certain than I likely should be, which may be meta-meta memory?!) that what you say that the end is the case. There’s almost certainly a bias towards error correction over direct recall. Certainly my experience is of testing wrong answers in my head before alighting on the right one.

That implies a set up more like an adversarial neural network (I’m not saying this is actually how it is, just trying to draw an analogy from something I understand), as opposed to a function in code. But that seems like a bit of a waste, but also means that two (or more) distinct processes could be working on the same task?

That’s very helpful thank you. I read the abstract of the paper, I think it might take me a couple of goes to really grok it. I think it’s testing why are more likely to correct a wrong answer given on a test (in a subsequent test), if they are enthusiastically told it’s right the first time. This is compared to if they are told that they might be wrong!

Given it’s the first time I’ve heard of this, I’m finding even the premise a challenge! ‘Hypercorrection’ apparently, for anyone not going to the paper.

What I’ve read of the article, meta memory seems to be more about our ability to judge how well we know something, rather than evaluate if our recall is correct.

I say ‘rather’… The concepts are obviously (or maybe not obviously!) related, but that sounds like assigning a score to the information we possess. While my original question was around evaluating knowledge as incorrect after recall.

That’s why the engine analogy doesn’t quite work for me. It’s not one answer, it’s two! So if it is an engine, it’s one that drives the car both forwards and backwards initially, and then switches off the one it doesn’t need.

I’m definitely going to read more into these concepts though. Thanks again for the links!