Your observation is so vague and general to the point of being rather meaningless. Almost every physical theory is described by an underlying mapping between inputs.
The interesting point is the expression power of your model. : to take an example I am somehow familiar with, current large vocabulary speech recognizers have millions of parameters. They work relatively well, but they are very difficult to interpret, and it is hard to see how they help us understanding how speech recognition actually works in our brain.
To make a somehow flawed analogy, every Turing complete language is equivalent, but getting the machine code of a very large project is not very interesting if you want to understand it, while it is mostly enough if you just want to use it.
Do you have any reason to suspect this isn't how the brain works? Maybe language isn't a small set of high level rules. Why should we suspect it to be? The probablistic models seem to be very similar to how real people actually learn informal language. Formal languages of course have high-level rules, and these are well modelled algorithmically.
I don't particularly have any reason to believe one way or the other. Certainly, the probabilistic models for language are created "out of the blue" without any attempt to model how human learn languages.
The interesting point is the expression power of your model. : to take an example I am somehow familiar with, current large vocabulary speech recognizers have millions of parameters. They work relatively well, but they are very difficult to interpret, and it is hard to see how they help us understanding how speech recognition actually works in our brain.
To make a somehow flawed analogy, every Turing complete language is equivalent, but getting the machine code of a very large project is not very interesting if you want to understand it, while it is mostly enough if you just want to use it.