I’d wish some predictive coding researcher would be so kind to give it a look, maybe somebody here knows someone?
Yeah, I haven’t had the time or energy to start cold-emailing predictive coding experts etc. Well, I tweet this article at people now and then :-P Also, I’m still learning, the picture is in flux, and in particular I still can’t really put myself in the head of Friston, Clark, etc. so as to write a version of this that’s in their language and speaks to their perspective.
During reading, I was a bit confused about the set of generative models or hypotheses. Do you have an example how this could concretely look like? For example, when somebody tosses me an apple, is there a generative model for different velocities and weights, or one generative model with an uncertainty distribution over those quantities? In the latter case, one would expect another updating-process acting “within” each generative model, right?
I put more at My Computational Framework for the Brain, although you’ll notice that I didn’t talk about where the generative models come from or their exact structure (which is not entirely known anyway). Three examples I often think about would be: the Dileep George vision model, the active dendrite / cloned HMM sequence learning story (biological implementation by Jeff Hawkins, algorithmic implementation by Dileep George) (note that neither of these have reward), and maybe (well, it’s not that concrete) also my little story about moving your toe.
I would say that the generative models are a consortium of thousands of glued-together mini-generative-models, maybe even as much as one model per cortical column, which are self-consistent in that they’re not issuing mutually-contradictory predictions (often because any given mini-model simply abstains from making predictions about most things). Some of the mini-model pieces stick around a while, while other pieces get thrown out and replaced constantly, many times per second, either in response to new sensory data or just because the models themselves have time-dependence. Like if someone tosses you an apple, there’s a set of models (say, in language and object-recognition areas) that really just mean “this is an apple” and they’re active the whole time, while there are other models (say, in a sensory-motor area) that say “I will reach out in a certain way and catch the apple and it will feel like this when it touches my hand)”, and some subcomponents of the latter one keep getting edited or replaced as you watch the apple and update your belief about its trajectory. I think “edited or replaced” is the right way to think about it—both can happen—but I won’t say more because now this is getting into low-level gory details that are highly uncertain anyway. :-P
Thanks a lot for the elaboration!
in particular I still can’t really put myself in the head of Friston, Clark, etc. so as to write a version of this that’s in their language and speaks to their perspective.
Just a sidenote, one of my profs is part of the Bayesian CogSci crowd and was fairly frustrated with and critical of both Friston and Clark. We read one of Friston’s papers in our journal club and came away thinking that Friston is reinventing a lot of wheels and using odd terms for known concepts.
For me, this paper by Sam Gershman helped a lot in understanding Friston’s ideas, and this one by Laurence Aitchison and Máté Lengyel was useful, too.
I would say that the generative models are a consortium of thousands of glued-together mini-generative-models
Cool, I like that idea, I previously thought about the models as fairly separated and bulky entities, that sounds much more plausible.