Textbook Group meeting for Parr, Pezzulo, Friston 2022 .

[Music] yeah okay it's recording um so for for Joseph for Daniel for the rest of the Institute who's uh following along remotely uh or via YouTube um welcome to the active inference Institute uh we do a uh weekly textbook group readings uh twice on Thursdays once in the morning once in the evening uh if you're if you're roughly in the Western Hemisphere and um yeah so so today we're going over chapter 7 in cohort 7 um and we'll be talking about essentially the Exemplar discret State space models and active inference that would be hidden Markov models and uh partially observable decision- making processes we have a nice Taz example which is pretty classic in in the field as well as in kind of Behavioral and and and and Neuroscience and neur neuroimaging studies in general and uh again we're in the second half or part two of the book which is highly dedicated to to constructing uh essentially active inference models as opposed to part one which is much more focused on Theory and and definitions um and so it's for anyone who's interested in computational modeling whether it be doing it themselves or getting a clearer grasp of kind of what are these computational models or cognitive models that we're putting together in order to say collaborate with a uh Team of of people who do do comp cognitive modeling then it it's very worthwhile to spend time with the second half of the book and then furthermore there are just there are many discreet State spaces in in in a very general sense in a in a physical sense and a kind of decision making and and and and human belief sense it's referring to scenarios where essentially if we think about it in data it's sort of like U categories it's thinking about binaries uh it's not so much thinking about uh fully continuous number series such as a range over one through a million where we could have so many different uh different values including with the decimal points um potentially an infinite number of values instead we're looking at say RGB color scales on old televisions so red blue green um we could have a distribution between the three of them in the sense of how much red how much blue and how much green uh but nonetheless it's it's a kind of um discreet uh categorization process so um yeah I normally I give a rather long uh kind of chapter summary but I'm happy to just kind of open up the floor if uh Joseph you have any particular questions or things you'd like to discuss here um I I would like to hear your summary um I also do have some questions um I haven't fully been able to understand um for example figure 72 so it if we could like I I I guess I just don't understand what plotted and and where the lines are coming from but um yeah I I'm I'm very interested in and trying to build these models and understand how to build them and um and so this seems like a really critical chapter for for me yeah yeah no that's great um yeah figure 72 which I I personally have not spent a lot of time with recently so coming back to it is rather interesting yeah so the the this is where the chapter it essentially opens with hidden marov models uh before we jump up to uh partially observable marov decision-making processes so it's like we have um I believe my is my screen still being shared appropriately or can you not see anything uh no I don't see your screen okay got it thanks for that just realized I doing a a little bit of uh display gymnastics um that should have done it so this is and a quick reference this is the Koda for the textbook group and so we have this available to all members and we have um not just a overview of the textbook group and you know dedicated uh meetings and and time availability and and all those sorts of things for the separate cohorts we also have a pretty good breakdown of the active inference textbook especially since it became open source so um so yeah figure 71 I know it's not the one you referenced but just to make sure we're like familiar with it enough this is…