>How does the parse tree get represented?

>Your question is about internal representation, but skips the process, and is about a representation that isn't the end of the process either.

I think you missed the essence of my conjecture. I'm not "skipping the process". I'm speculating that there is surprisingly little process to be done at all because language is "close to the metal" (to borrow an analogy).

Here's a simple but limited language. We can have electronic components like batteries and resistors and inductors as our basic noun objects, and our relational verb words are all just telling us if subjects and objects are connected in parallel or series. The only adjectives are numerical pre-fixes for amounts of voltage/resistance/whatever. A "." will suffice as a grammatical particle to mark "end of object". Its a bit verbose and cumbersome, but I can describe whatever circuit I want in these terms. In fact, the parse tree of this grammar can literally encode any loop-free circuit, with sibling branches in the parse tree corresponding to parallel connections and nested branches in the tree corresponding to series connections. I could literally use this grammar and vocab as a file format for an FPGA if I wanted to. One doesn't even need a fully general computation or additional reasoning abilities to "understand" this circuit language. A machine that physically builds the connections according to the grammatical rules as the words stream in would suffice. The semantics of this toy language don't require any further processing or translation.

Now here's the toy brain I envision. Purpose follows a tree structure. Things happen and things exist because someone wills them to be. When you get paid to work, the work serves the will of the company. When you do it for yourself, its your free will. When your computer does something, its because of your will (never its own free will). Each person is both a source of will power and a thing that exists as a matter of ground truth. Thus we can think of a person as a battery in a simplified analog model of our world.

For example you can describe a short circuit of one of these person-batteries with a mutually recursive circuit-statement: "I am because I want to be. I want to be because I am." This is a vacuous truth. You can also make more sophisticated paths from will power (want) to common ground (facts) as a way to encode a more complex scenario. All you have to do is put intermediate things in the path between "I want" and "I am". "I want [A] because [B] so that [C] or [D] which leads to [E] ... so that I continue to be." The full semantics of the objects in the broader context may or may not be known, but it doesn't matter. Once you have a spoken-circuit encoding the immediate dynamics of motivations (batteries in the circuit) and how they relate back to ground (resistors and inductors encoding a simplified simulation of a scenario), you can start modeling these dynamics as a literal analog electrical circuit.

In the special case of one power source in the circuit with enough voltage to overwhelm any other, this system of analog simulation should reduce to the ordinary semantics of True/False reasoning. The big voltage represents tautology. If A implies B, and A is connected to the tautology voltage, B is now at the tautology voltage.

All sorts of semantics and reasoning calculi can be encoded in this way. Its a powerful paradigm. Does it actually happen in the brain though? I have no idea.

I realize in retrospect it sounded like I was suggesting absolute dictionary word to single neuron translation. That's not what I meant. What I'm really suggesting is more like a wetware FPGA that does abstract reasoning about people/causation with analog circuit models. It so happens those circuits can be encoded into a spoken transfer format in a very literal way.

Now that I've elaborated further, is that at least developed enough to discuss?