Concerning your first point, that the designer has to hand-insert that all-important sign bit.
So how do humans come up with these sign bits?
I imagine a trial-and-error process of interacting with the controlled system.
During this, the person’s brain is generating an error signal derived directly or indirectly from an evolutionarily-fixed set point.
While trying to control the system manually using an initially random sign bit, I suppose the brain can analyze at a low level in the hardware that the error is 1) changing exponentially, and 2) has a positive or negative slope, as the case may be.
If the situation is exponential and the slope is positive, you synaptically weld the cortical representation of the controlled variable to the antagonist muscle of the one currently energized, and if negative, to the energized muscle itself.
Bayesian inference would enter as a Kalman filter used to calculate the controlled variable.
I suppose the process of acquiring the sign bit of the slope could not be separated from acquiring the model needed by the Kalman filter,
so some kind of bootstrapping process could be involved.
In his book “Neural Engineering...” (2004), Chris Eliasmith makes a case that the brain contains Kalman filters.
Is the evolutionary process responsible for the original hard-wired set point itself a controller?
I doubt it, because, to use Douglas Adams’ analogy, control principles to not seem to be involved in getting the shape of a puddle to match that of the hole it’s in.
Concerning your first point, that the designer has to hand-insert that all-important sign bit. So how do humans come up with these sign bits? I imagine a trial-and-error process of interacting with the controlled system. During this, the person’s brain is generating an error signal derived directly or indirectly from an evolutionarily-fixed set point. While trying to control the system manually using an initially random sign bit, I suppose the brain can analyze at a low level in the hardware that the error is 1) changing exponentially, and 2) has a positive or negative slope, as the case may be. If the situation is exponential and the slope is positive, you synaptically weld the cortical representation of the controlled variable to the antagonist muscle of the one currently energized, and if negative, to the energized muscle itself. Bayesian inference would enter as a Kalman filter used to calculate the controlled variable. I suppose the process of acquiring the sign bit of the slope could not be separated from acquiring the model needed by the Kalman filter, so some kind of bootstrapping process could be involved. In his book “Neural Engineering...” (2004), Chris Eliasmith makes a case that the brain contains Kalman filters.
Is the evolutionary process responsible for the original hard-wired set point itself a controller? I doubt it, because, to use Douglas Adams’ analogy, control principles to not seem to be involved in getting the shape of a puddle to match that of the hole it’s in.