I’m not qualified to comment on the literature in general or how research goes—if you say that treating the brain as drawing actions from a Boltzmann distribution on this weird divergence is useful, I believe you. But it seems like you can extract very specific claims from Friston 2009, like the brain having a model from perceptions to a distribution over “causes” (model parameters), and each step of learning in the brain reducing the KL divergence (specifically!) between a mutable internal generative model of “causes” and the fixed sense-inferred “causes.” This is the sort of thing that I failed to find a justification for, and therefore am treating as having a tenuous relation to real brains. And I don’t think this is just nitpicking, because fixed inference of causes is used to get fixed motivations that have preferences over causes.
So we could quibble over the details of Friston 2009, *buuuuut*...
I don’t find it useful to take Friston at 110% of his word. I find it more useful to read him like I read all other cognitive modelers: as establishing a language and a set of techniques whose scientific rigor he demonstrates via their application to novel experiments and known data.
He’s no more an absolute gold-standard than, say, Dennett, but his techniques have a certain theoretical elegance in terms of positing that the brain is built out of very few, very efficient core mechanisms, applied to abundant embodied training data, instead of very many mechanisms with relatively little training or processing power for each one.
Rather than quibble over him, I think that this morning in the shower I got what he means on a slightly deeper level, and now I seriously want to write a parody entitled, “So You Want to Write a Friston Paper”.
I’m not qualified to comment on the literature in general or how research goes—if you say that treating the brain as drawing actions from a Boltzmann distribution on this weird divergence is useful, I believe you. But it seems like you can extract very specific claims from Friston 2009, like the brain having a model from perceptions to a distribution over “causes” (model parameters), and each step of learning in the brain reducing the KL divergence (specifically!) between a mutable internal generative model of “causes” and the fixed sense-inferred “causes.” This is the sort of thing that I failed to find a justification for, and therefore am treating as having a tenuous relation to real brains. And I don’t think this is just nitpicking, because fixed inference of causes is used to get fixed motivations that have preferences over causes.
So we could quibble over the details of Friston 2009, *buuuuut*...
I don’t find it useful to take Friston at 110% of his word. I find it more useful to read him like I read all other cognitive modelers: as establishing a language and a set of techniques whose scientific rigor he demonstrates via their application to novel experiments and known data.
He’s no more an absolute gold-standard than, say, Dennett, but his techniques have a certain theoretical elegance in terms of positing that the brain is built out of very few, very efficient core mechanisms, applied to abundant embodied training data, instead of very many mechanisms with relatively little training or processing power for each one.
Rather than quibble over him, I think that this morning in the shower I got what he means on a slightly deeper level, and now I seriously want to write a parody entitled, “So You Want to Write a Friston Paper”.