Information flow in context-dependent hierarchical Bayesian inference

hello and welcome everyone to the active inference lab and to the active inference live stream we're here in active inference live stream 17.0 and it's march 3rd 2021 i'm daniel and i'm here with two of my colleagues who can introduce themselves blue my name is sarah davis i'm a person who's interested in things i'm a student in leibniz university in a master's program in philosophy of science and i've been an engineer and a bunch of other things so i'm just generally interested in how it's all connected i'm blue knight i'm an independent research consultant based out of new mexico cool well we're here for a fun discussion so thanks both for joining at the active inference lab we're an experiment in online team communication learning and practice related to active inference you can find us at our links and socials this is a recorded and an archived and a hastily produced live stream so please provide us with feedback so that we can be improving on our work all perspectives and backgrounds are welcome here and we'll follow good live stream etiquette so here we are in paper number 17 and we've just completed our first quarterly active inference lab round table so check that out on our channels if you haven't and today we're going to be setting the context for 17.1 and 17.2 the paper that we're discussing in 17 is information flow in context dependent hierarchical bayesian inference by chris fields and james f glazebrook it was published in october 2020 and what's funny is that the dot zero videos have always been about context set in context and we even wrote in the previous videos the video's introduction and its context it's not a review or the final word and then it got really meta with this paper because it's a paper about context the punchline or a punchline of this paper is that we can integrate various approaches from mathematics mostly to achieve a scale-free formulism that leads to an interpretation of nested systems where communicating systems are engaged in active inference and distinguished by informational or statistical patterns or they're engaged in something that's related to active inference let's say because it's pretty out there with what it is and it's a challenging but also a fun paper so we're gonna start with the goals and the claims and the abstract and at that point it might be like wait what with the roadmap headers or with abstract claims it's like driving through a country where you don't speak the language perhaps and that's okay because the bulk of this video in the keywords are going to be going through and kind of unpacking a lot of the key terms that are gonna matter for even just understanding the lay of the land and then we've pulled out a few other key topics some quotations didn't go as much into the formalisms of the paper itself but we're looking forward to doing that with the authors in 17.1 and so we're just looking forward to that in 17.1.2 we'll be discussing the same paper so read it and save and submit your questions and get in touch if you want to participate and also we're looking forward to your chats uh during this presentation giving us a little support along the way so here we go with the goals and claims of the paper under discussion so here are um the goals and the claims so maybe blue do you want to read the author's goals and claims sure so the goals in the paper are twofold first to model intrinsic or true contextuality using the general category theoretic methods of two spaces and channel theory and second to employ this formulation to reconstruct hierarchical bayesian inference in a context-dependent way it goes on to say here we have formulated an approach to intrinsic contextuality in general category theoretic terms a set of observations exhibits intrinsic contextuality if no cocoon can be constructed over the observables that produce them oh cone cocoon who knows how to say it um so they're doing a couple of things here they have two main goals so it's great that…