The predictive global neuronal workspace: A formal active inference model of visual consciousness

hello and welcome i think that we're going now this is the active inference live stream it's active inference live stream 18 on march 17 2021 welcome to the active inference lab everyone we are a participatory online lab that is communicating learning and practicing applied active inference you can find us at any of these links here this is a recorded and an archived live stream so please provide us with feedback so that we can improve on our work all backgrounds and perspectives are welcome here and we'll be using good video etiquette for live streams and stephen thanks so much for joining always awesome to have you on for the conversations and uh this will be very fun convo here looking at the calendar we can see at the short and linked that we're down in the end of march 2021 in number 18 which is going to be about the paper the predictive global neuronal work space a formal active inference model of visual consciousness by christopher white and ryan smith and this video in 18.0 is going to be about setting the context for 18.1 and 0.2 whether participating in these discussions with the authors if you're listening to this before those events happen or contextualizing um in sort of a bi-directional way for listening to those later discussions so if you're in the active inference community this might be exposing you to some new questions and perspectives if you're from outside the active inference community or you're not familiar with it then this will be highlighting how active inference is an integrative framework across a few different other ideas these dot zero videos are just introductions to the ideas because you could take a whole course of learning on any one of them so they're just little suggestions and hints related to the ideas and where this paper is going as far as the punch line is that we can build on this predictive global neuronal work space model and think about it in being implemented by active inference type models and get new insights predictions and hopefully explanations and applications and in 18 we're gonna pretty much go through a lot of the paper as far as their goals and the questions and many of their copious figures and it'll be good to talk to ryan and christopher when they join for the stream for 18.1 and 0.2 we're going to be discussing this paper so save and submit your questions and get in touch with us if you want to participate okay so here is the paper predictive global neuronal work space a formal active inference model of visual consciousness before we read the goals and claims uh stephen maybe you could just say hello and introduce yourself and what got you excited in this paper just personally before we dive into like the paper itself thanks daniel yeah well this this my background is that i'm doing a practice based phd um at the solomon's institute of applied psychology so i'm interested in how active inference can help understand some of the processes that i've been working on for understanding pre-reflective phenomenological awareness and people's sense-making so that kind of ties in to how i'm also interested in um ryan and christopher's work because they're bringing this into the psychological domain but also bringing that into the phenomenological domain in ways which are practically predictive and so that's really really exciting and i'm i see a lot of opportunities for this modeling work and i've actually spoken to ryan about this to try and link in with other ways of uh people doing sense making etc so and then this particular paper um i think is really interesting because we've got a lot of exciting um philosophical ideas around global work space concepts in neuroscience and an area that i i've only partly explored but to have this integrated into a predictive model that can be tested at certain scales in certain ways and to have people who've got the sort of rigor and the kind of the ability to focus down and choose um experimental and practical ways to to explore this which…