Charan Ranganath: belief

Transcript context

…So it’s used for learning languages, for learning facts, for that generic semantic information type of memories. Yeah, and I think this falls into a category. We’ve done other modeling. One of these is a published study in PLOS Computational Biology where we showed that another way, which is, I think, related to the spacing effect is what’s called the testing effect. So the idea is that if you’re trying to learn words, let’s say in Spanish or something like that, and this doesn’t have to be words, it could be anything, you test yourself on the words. That act of testing yourself helps you retain it better over time than if you just studied it. So from traditional learning theories, some learning theories, anyway, this seems weird, why would you do better giving yourself this extra error from testing yourself rather than just giving yourself perfect input that’s a replica of what it is that you’re trying to learn? I think the reason is is that you get better retention from that error, that mismatch that we talked about. So what’s happening in our model, it’s actually conceptually similar to what happens with backprop in AI or neural networks. So the idea is that you expose, “Here’s the bad connections, and here’s the good connections.” So we can keep the parts of the cell assembly that are good for the memory and lose the ones that are not so good. But if you don’t stress test the memory, you haven’t exposed it to the error fully. So that’s why I think this is a thing that I come back to over and over again, is that you will retain information better if you’re constantly pushing yourself to your limit. If you are feeling like you’re coasting, then you’re actually not learning, so it’s like- You should always be stress testing the memory system.…

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