Predictive Coding: a Theoretical and Experimental Review

Hello and welcome everyone. It's April 28th, 2022 and we're here in ActInfLab livestream number 43.0 discussing Predictive Coding, A Theoretical and Experimental Review. Welcome to the ActInfLab. We're a participatory online lab that is communicating, learning, and practicing applied active inference. You can find us at links here on the slide. This is recorded in an archived livestream, so please provide us with feedback so we can improve our work. All backgrounds and perspectives are welcome and we'll be following video etiquette for livestreams. Head over to activeinference.org if you want to learn more about how to participate in the livestreams or anything else happening in ActInfLab. Okay. We're here today to learn and discuss the paper Predictive Coding, A Theoretical and Experimental Review by Baron Milledge, Anil Seth, and Christopher Buckley. The video is just an introduction and a contextualization for some of the ideas and some of the details of the paper in the broad sense. It's not a review or a final word. It's, as with some of these other technical, dense papers, it's like an opening for those who have technical questions at the learning side of things or at the more advanced research side of things to come get involved because this is a technical paper but also hopefully as we'll unpack it has a lot of cool biological and philosophical implications. So in 43.0 we're going to say hello and introduce some big questions, then cover the aims and claims of the paper, the abstract, and the roadmap. And then pretty much just jump right in and we're going to focus a lot on the introduction, the context, and the single layer predictive coding model. And then we'll go a little bit faster through the later sections of the paper talking about some generalizations of predictive coding and some important points that the authors raise. So if you want to participate in 43.1 or .2 in the coming weeks, just let us know. Okay, so we can say hello and give any information that we'd like and also just maybe something that we thought was exciting or something that motivated us to get involved in this quite involved paper. So I'm Daniel. I'm a researcher in California and I was curious to learn more about how predictive coding and predictive processing related to active inference. also just about how different models framed anticipatory systems. So over to you, Maria. Hello, Daniel and everyone. I have a bipolar degree in psychology and I am a master's candidate in philosophy of science in the University of Sao Paulo, Brazil. And I am researching the relationship between predictive processing and illusionist theories of consciousness. And I think what brought me here today was my wish to start learning about the formalisms because I don't really see it in philosophy. And I don't actually need it for my dissertation, but eventually I wanted to continue my work on predictive processing. So eventually I had to start it and I just thought about maybe that's a good idea to come here and minimize my uncertainties. Awesome. Through inference and or through action. And also thanks a lot, Brock, for helping in the dot zero. So we'll just start with the big questions. And these are the kind of questions that might motivate someone or interest them in this paper without even mentioning active inference per se. But these are like some big questions that get touched upon. What is the formal basis of predictive coding? How is predictive coding used or useful? What are some areas of current and future development? And what is the relationship between predictive coding and active inference? And hopefully we'll draw out more questions. Anything else to add about this? No, not really. Cool. Here is Predictive Coding, a Theoretical and Experimental Review. I think the first version was from 2021, but the second revised version was 22 by Millage, Seth and Buckley. And just to review the core claim and then some of the aims and directions they set out,…