especially given that deliberate actions in pursuit of a goal are highly discontinuous?

I'm not certain I understand your terms. If I interpret your words "classically", then of course I "know what you mean". However, if I'm viewing them through the PCT lens, those words make no sense at all, or are blatantly false.

When you drive a car and step on the brake, is that a "deliberate action" that's "discontinuous"? Classically, it seems obvious. PCT-wise, you're begging the question.

From the PCT perspective, the so-called "action" of braking is a chain of controls looking something like:

• Speed controller detects too-high speed, sets speed-change controller to "rapid decrease"

• Speed-change controller detects discrepancy between current acceleration and desired deceleration, sets braking controller to "braking hard"

• Braking controller notes we aren't braking, sets foot position to "on brake"

• Foot position controller detects foot is out of position, requests new leg position

• Leg position controller detects out of position, requests new leg speed/direction

• Leg speed controller detects not moving, requests increased muscle force

...etc., until

• Foot position controller detects approaching correct position, and lowers requested movement speed, until desired position is reached

• Speed controller observes drop of speed below its reference level, sets speed-change controller to "slow accelerate"

• Speed-change controller notices that current deceleration is below "slow accelerate" reference, sets "gas" controller to "slight acceleration"

• ...and so on, until speed stabilizes... and the foot goes up and down slightly on the gas... all very continuously.

So, there is nothing at all "discontinuous" about this. (Modulo the part where nerves effectively use pulse-width modulation to communicate "analog" values).

And it's precisely this stable continuity of design that makes PCT so elegant; it requires very little coordination (except hierarchically), and it scales beautifully, in the sense that mostly-identical control units can be used. Got a more complex animal? Need more sophisticated behavior? Just add controllers, or new layers of controllers.

Need a new skill? Learning grows in or assigns some new controllers, that measure derived perceptual quantities like "speed of the car", "braking", and "putting on the gas". (Which explains why procedural knowledge is more persistent than propositional knowledge - the controllers represent a hardware investment in knowledge.)

And within this model, actions are merely side-effects of disturbances to the regulated levels of perceptual variables, such as speed. I stopped the upward point of the hierarchy at the speed controller noticing a speed discrepancy, but the reason for that discrepancy could be you noticing you're late, or it could be that your "distance to next car" controller has issued a request to set the new "desired speed" to "less than the car in front of us". In either case, the "action" is the same, regardless of what "goal" -- or more likely, disturbance -- caused it to occur.

That being said, PCT does include "sequence" and "program" controller layers, that can handle doing things in a particular sequence or branching. However, even these are modeled in terms of a perceptual control hierarchy, ala TOTE loops. That is, you can build TOTE loops by wiring controllers together in relatively simple ways.

Reification of programs and "actions" through controller hierararchies is also a good strategy for building a fast machine out of slow components. Rather than share a few ultra-fast, complex components, PCT hierarchies depend on chains of similar, simultaneously-responding, cheap/dumb components, such that the fastest responses are required from the components that are generally nearest (network-wise) to the place where the signals need to be received or delivered to exert control.

These are just some of the obvious properties that make PCT-style design a good set of tradeoffs for designing living creatures, using similar constraints to evolution. (Such as the need to be able to start with primitive versions of the model, and gradually scale up from there.)

Okay, and what epistemic profit does this approach gain for you

As I said, it gives me a better idea of what to look for. After grasping PCT, I was able to identify certain "bugs" in my brain that had previously been more elusive. The time and hierarchy distinctions made it possible for me to identify what I was controlling for, rather than just looking at discrete action triggers, as I did in the past.

In this area, PCT provides a more compact model of what psychologists call "secondary gain" , hypnosis people call "symptom conversion", and NLP people call "ecology".

The idea is that when you take away one path for someone to get something (e.g. giving up smoking) they may end up doing something else to satisfy a need that was previously supported by the old behavior (e.g. chewing gum).

What psychologists, NLPers, and hypnosis people never had a good explanation for (AFAIK) is why it takes time for this substitution or reversion to occur! Similarly, why does it take time for people to stop persisting at trying to do something new?

This is an example of a complex behavioral property of humans that falls directly out of the PCT model without any specific attempt to generate it. Since high-level goals are integrated over a longer time period, it takes time for the error signal to rise, and then further time for the controller network reorganization process (part of the PCT model of learning) to find an alternative or extinguish the changed behavior.

I find PCT parsimonious because there are so many little quirks of human nature I know about, that would be naturally expected to occur if behavior was control-system driven in precisely the ways PCT predicts that it is... but which are just weird and/or unexplained under any other model that I know of.