Textbook Group meeting for Parr, Pezzulo, Friston 2022 .

All right, greetings everyone. It is September 9th, 2022. It's the 15th meeting for Cohort 1, and we are discussing Chapter 6. We're in the first of two discussions on Chapter 6 of the textbook. We're going to go to the questions page and start there, where we have one question that we explored a bit last week in our welcome back, and then there's at least one question prepared, but anyone is welcome to add more questions here while we turn first to this question, what are the four steps in the recipe to construct an active inference model? And can we make templates that facilitate people walking through some of these stages? How much model building do people prefer to be engaged in in this section of the textbook group? Do people want to have one or more model that they're personally developing? Do they want those to be scaffolded in a shared page to help increment models along and see models in different stages? Yeah, Brock, and anyone else? It's the same problem as the first half of the textbook without math. It's not going to go well. So this half without modeling, it's going to be quite difficult, I think. So I think we should just get that out of the way that it's, yeah, it's not about whether you want to or not or whatever. If you want to just understand it at a cursory, philosophical level, then maybe not. But if you actually want to understand it, then yeah, we are going to have to model something some way. It doesn't have to be the most complex, most precise one. But yeah. I wonder if there are, if we could develop like maybe a couple examples or simple ones that people might like the model. I don't know. Those are all going to be purely agent based, but I think that's a good question. Thanks. Thanks. I agree. I wanted to frame it as a question, but I agree with that perspective. And following along, as we structure a few specific cases, the rat and the T-maze, the eye circadian, which are used in the textbook, then some of the examples that are used recurrently in the literature, like birdsong and a few others. And for these, many scripts exist. And helping people who might not have the setup, either by with some walkthroughs. Okay. Here's how you get Octave running. So you can do this MATLAB script, or here's the standalone DEM demo, because this textbook really only gives the pointer to MATLAB scripts and methods. Like when they say that the standard schemes can be applied, they mean a MATLAB script. And that's exactly what the step-by-step guide, model stream one, is built around. And chapter seven on modeling in discrete time is going to be akin to the step-by-step guide, but it's not step-by-step-by-step-by-step. It's more like it takes two bigger steps. Okay. How is this four-step recipe or active inference modeling similar or different than approaches that people have seen for systems modeling from other frameworks? For example, in a reinforcement learning or cybernetic modeling. So something more recent and computational or something more pre-formal or computational. Should this recipe be surprising to people coming from a certain background? Are there sub-steps that are relevant to consider? Brock, yes. I don't have extensive formal modeling experience, but I just necessarily kind of most of the things that I've done for the last half decade like entail some form of this. And I don't see how you could model anything without going through these basic questions here. Like this is, it's a bit, I don't see how this is specific or exclusive or whatever to active inference. It's a bit ambiguous to me how that, you know, I mean, besides specifically the generative model, I guess. But which system are we modeling? Another way to ask that is what questions are we trying to answer? Or who, I think Lyle brought this up. Like who are you making the model for sort of thing? Because the same, like you were saying just earlier about the particular choice of how you're modeling it, it's just a choice on that system. And…