Agreed. ‘Rest in bed as much as possible but grudgingly take the actions needed to stay alive’ sounds a lot like depression, but there exist non-depressed people who need explaining.
I wonder if the conversion from mathematics to language is causing problems somewhere. The prose description you are working with is ‘take actions that minimize prediction error’ but the actual model is ‘take actions that minimize a complicated construct called free energy’. Sitting in a dark room certainly works for the former but I don’t know how to calculate it for the latter.
In the paper I linked, the free energy minimizing trolleycar does not sit in the valley and do nothing to minimize prediction error. It moves to keep itself on the dynamic escape trajectory that it was trained with and so predicts itself achieving. So if we understood why that happens we might unravel the confusion.
>I wonder if the conversion from mathematics to language is causing problems somewhere. The prose description you are working with is ‘take actions that minimize prediction error’ but the actual model is ‘take actions that minimize a complicated construct called free energy’. Sitting in a dark room certainly works for the former but I don’t know how to calculate it for the latter.
There’s absolutely trouble here. “Minimizing surprise” always means, to Friston, minimizing sensory surprise under a generative model: −logp(s|m) . The problem is that, of course, in the course of constructing this, you had to marginalize out all the interesting variables that make up your generative model, so you’re really looking at −log∫r∫Ψp(s,r,Ψ|m) or something similar.
Mistaking “surprise” in this context for the actual self-information of the empirical distribution of sense-data −logp(s) makes the whole thing fall apart.
>In the paper I linked, the free energy minimizing trolleycar does not sit in the valley and do nothing to minimize prediction error. It moves to keep itself on the dynamic escape trajectory that it was trained with and so predicts itself achieving. So if we understood why that happens we might unravel the confusion.
If you look closely, Friston’s downright cheating in that paper. First he “immerses” his car in its “statistical bath” that teaches it where to go, with only perceptual inference allowed. Then he turns off perceptual updating, leaving only action as a means of resolving free-energy, and points out that thusly, the car tries to climb the mountain as active inference proceeds.
Agreed. ‘Rest in bed as much as possible but grudgingly take the actions needed to stay alive’ sounds a lot like depression, but there exist non-depressed people who need explaining.
I wonder if the conversion from mathematics to language is causing problems somewhere. The prose description you are working with is ‘take actions that minimize prediction error’ but the actual model is ‘take actions that minimize a complicated construct called free energy’. Sitting in a dark room certainly works for the former but I don’t know how to calculate it for the latter.
In the paper I linked, the free energy minimizing trolleycar does not sit in the valley and do nothing to minimize prediction error. It moves to keep itself on the dynamic escape trajectory that it was trained with and so predicts itself achieving. So if we understood why that happens we might unravel the confusion.
>I wonder if the conversion from mathematics to language is causing problems somewhere. The prose description you are working with is ‘take actions that minimize prediction error’ but the actual model is ‘take actions that minimize a complicated construct called free energy’. Sitting in a dark room certainly works for the former but I don’t know how to calculate it for the latter.
There’s absolutely trouble here. “Minimizing surprise” always means, to Friston, minimizing sensory surprise under a generative model: −logp(s|m) . The problem is that, of course, in the course of constructing this, you had to marginalize out all the interesting variables that make up your generative model, so you’re really looking at −log∫r∫Ψp(s,r,Ψ|m) or something similar.
Mistaking “surprise” in this context for the actual self-information of the empirical distribution of sense-data −logp(s) makes the whole thing fall apart.
>In the paper I linked, the free energy minimizing trolleycar does not sit in the valley and do nothing to minimize prediction error. It moves to keep itself on the dynamic escape trajectory that it was trained with and so predicts itself achieving. So if we understood why that happens we might unravel the confusion.
If you look closely, Friston’s downright cheating in that paper. First he “immerses” his car in its “statistical bath” that teaches it where to go, with only perceptual inference allowed. Then he turns off perceptual updating, leaving only action as a means of resolving free-energy, and points out that thusly, the car tries to climb the mountain as active inference proceeds.