A step-by-step tutorial on active inference and its application to empirical data

hello everyone welcome to the active inference lab this is the first active inference model stream active inference model stream 1.0 and i'm really excited for today's conversation i'm daniel friedman and just to introduce the other participants today uh ryan go for it uh yep hi i'm uh ryan uh i'm from the florida institute of brain research hi i'm christopher i'm a phd student at the mrc cognition brain sciences unit which is based at the university of cambridge hi i'm max murphy i just completed my phd at university of kansas in bioengineering uh with a focus on neural engineering awesome thank you everyone for participating and for ryan and christopher two of the authors of this awesome work we're going to be exploring so this is the first in a several part series that is going to be highlighting several perspectives and addressing questions related to the active inference tutorial paper of smith at all called a step-by-step tutorial on active inference and its applications to empirical data so the idea here is for those who are working with empirical data to learn about active inference as a method and also for those in the active inference community to be learning about some of the methods that apply active inference if you're listening you're participating and if you have any questions during the live stream feel free to post it in the youtube live chat and we'll try to address it during or after this presentation that we're about to get if you have questions after the live stream please feel free to leave it in a comment form and we'll try to address it and integrate your input in future sessions and to learn more and to participate check out activeinference.org or any of the information in the video's description so that's all the information uh or metadata for this video the way this is gonna work today is we're going to do some introductory questions uh just sort of like asking what in general is this work about what motivated the authors to write the paper the way that they did and then both ryan and christopher are going to share their screens for part of the presentation and they're going to show us a few different things about the work that they've done and then we have a couple of questions prepared on our side but also we're going to be looking at a live chat if anyone has questions so just post it whenever you feel like it in the live chat and then we'll again try to address it so as i stated the intro questions and then we'll go to the presentations so first intro question to uh the authors is what is this work what is exciting about it what motivated you to work on it um okay so uh so just to kind of reintroduce myself a little bit more so i'm uh ryan smith so i'm an investigator at the laureate institute for brain research in tulsa oklahoma and the um the focus of our institute is um primarily on uh neuroimaging and sort of neuroscience approaches to understanding um psychology and psychiatry with a focus on sort of treating psychiatric disorders um and so for a while now there has been um the use of simpler computational models primarily reinforcement learning models or drift diffusion models things like that um out there for researchers for who are working with empirical data who so there are good resources out there for people to learn those methods um to apply them to data in their own research um however at the moment so active inference is a sort of much newer field especially it's sort of a formulation in terms of uh partially observable markov decision processes and there isn't really to date a really clear sort of combined place to learn the sort of practical methods to um to build these sorts of models and um to then sort of apply them to uh task behavior in empirical studies um so the kind of motivation for this paper this tutorial was to allow somebody you know so new students or somebody who's sort of like an early earlier you know junior faculty things like that who wants to go in this…