Relevance Realisation, Predictive Processing, Flow

Okay, and we're away. Welcome everybody to the third episode of Active Inference Insights, brought to you by the Active Inference Institute. I'm your host, Darius Parvizzi-Wayne, and today I am absolutely thrilled to be able to speak to John Viveki. John is an award-winning professor of psychology, cognitive science, and Buddhist philosophy at the University of Toronto. He is also the presenter of the renowned YouTube series Awakening from the Meaning Crisis, as well as the newer After Socrates. His work focuses on 4E cognitive science, which holds that cognition is embodied, embedded, enacted, and extended beyond the brain. In particular, John explores relevance realisation, our adaptive ability to zero in on salient information in a world of near-infinite complexity. Last year, he, Mark Miller, and Brett Anderson wrote the paper Predictive Processing and Relevance Realisation, exploring convergent solutions to the frame problem, which proposes that trade-offs in precision weighting, a key second-order dynamic in predictive processing hierarchies, are at the heart of our ability to be intelligently ignorant. And that alignment of predictive processing and relevance realisation is exactly what we're going to be talking about today. John, welcome to the show. Thank you so much for joining us. It's an absolute treat. Thank you, Darius. A great pleasure. I'm hoping that Mark does make it on in December. That'll be great. Yeah, absolutely. But in his absence, because Mark was meant to be here today, but he's not unfortunately able to make it. Perhaps, so this podcast is acting as a kind of primer, an introduction for our audience to the critical themes in cognitive science and psychology and biology, as well as maths and physics, as they are right now. So I think it would be a worthwhile place to start if you could just unpick what relevance realisation is, and then we'll come to how it aligns with predictive processing. Sure. So relevance realisation is, sort of inverts the way common sense. Common sense is there's a lot that is obvious to us. And we know what, it's obvious what you should pay attention to, we're sometimes mistaken, but it's obvious what we should be remembering and what we should be doing. And that's all obvious. And we just run from that. Our job as cognitive scientists is to explain how the brain generates that obviousness, that salience landscapes that makes the right things stand out as relevant to us, so that we can do what is astonishing, solve a wide variety of problems in a wide variety of domains. You're a general problem solver, which is just astonishing. I mean, you can learn Albanian history, you could learn about swimming or rock climbing, you can take up the study of dinosaurs in the Jurassic period, like it's just, you know, and it's just amazing and astonishing. And there is good empirical evidence, you have something like a general intelligence, there seems to be general capacity, there's individual variation and talent, some people are better at learning this domain than other domains. But they're way back from Spearman, we know there's some sort of general ability, I'm going to propose that the general ability are two interlocking things, the anticipation and relevance realization. What I mean by a general ability is these are kind of meta problems, these are the two big problems you have to solve when you're doing any other specific problem solving. And what makes relevance realization so hard, generating that obviousness so mysterious, actually, that's what Scott Atron says, he says that what science does is it makes what common sense to take takes to be obvious, mysterious. Here's a table in front of me. But physics says, well, what is that really, anything mysterious, and it's made out of quarks and all this sort of stuff. It's the same sort of thing here. Because when you think about it, outside of common sense, you realize that there's just an overwhelming amount of information that's…