[YeGoblynQueenne]

I'm curious to know what you mean by "labelled information".

I'm guessing that what you are calling "labelled information" is various forms of encouragement or discouragement that could be considered positively and negatively "labelled" examples.

If that is the case, linguistics research back in the '70s found that infants get almost no negative examples of, in particular, language. For example, a parent will not correct a child by saying, "no you can't say 'eated' because then you could also say 'sitted'". Instead they will correct by saying "no, you should say 'ate'" etc. That is important because there was a famous proof in inductive inference (the precursor to computational learning theory) that languages higher in the Chomsky hierarchy than regular languages cannot be learned from positive examples alone. And yet, babies eventually learn to speak human languages, which are assumed to be at least context-free. Chomsky used these findings to support his claim of a "universal grammar" or innate language endowment [1].

If you are talking about multi-class labelling, that's even harder to imagine. In machine learning, a multi-class classifier will map inputs to some set of categorical labels (i.e. a set of integers) but the mapping from those labels to concepts that a human would recognise, such as 1:cat, 2:bat, 3:hat, etc, must be perormed manually, because the classifier and humans do not have a shared understanding of what e.g. "cat", "bat" and "hat" mean. The classifier only knows 1,2,3... etc, the human knows that "1 means cat". How would this lack of shared context be resolved between an adult and a baby, so that the adult could provide "multi-class labels"?

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[1] Sorry that I don't have any references for all this handy- I can try to dig some up if you're interested, but you could start by reading the wikipedia page on Language Identification in the Limit, which is about the famous result from inductive inference I mention (also known as Gold's result from the man who derived it):

https://en.wikipedia.org/wiki/Language_identification_in_the...

[Tainnor]

By now, the Chomskian approach to linguistics is not unchallenged anymore and there is some doubt on whether the "poverty of stimulus" argument holds any water (see e.g. [1]).

IMHO, modern cognitive science based approaches (such as by Tomasello and others) have a better chance of explaining how language is acquired than the hypothesising of the 70s.

I don't have time now to go into more references, but the question is far from settled.

[1] https://scottthornbury.wordpress.com/2015/06/07/p-is-for-pov...

[YeGoblynQueenne]

I agree that the matter is not settled [edit: in the sense that there is criticism of Chomskian linguistics, from linguists] and that there is debate on the poverty of the stimulus and universal grammar etc, but the post you link to is not a very good summary of it. I recommend Alexander Clark's "Linguistic Nativism and the Poverty of the Stimulus" for a good look on the subject from the non-Chomskian poit of view.

Note however that, as far as I understand it, there is no controversy about the lack of negative examples of language given to children by their parents.

[Tainnor]

Fair, I just looked for the first reference I could find. I haven't done any real linguistics in years, although I vividly remember the arguments. Especially that Evans & Levinson article 10 years or so back ("The Myth of language universals") which generated quite some heat. If I have time, I will check out your reference.

Not sure about the negative examples; but language acquisition was never my focus area anyway.

I would just generally be cautious about applying formal language theory too readily to linguistics, that's all I wanted to say.

[visarga]

> How would this lack of shared context be resolved between an adult and a baby, so that the adult could provide "multi class labels"?

The baby would do multi-modal learning - learning the associated sound (name) with an image (object).

I don't think the parent and baby lack a shared context. They are both agents in the same environment, who often interact and cooperate to achieve goals and maximise rewards. The baby understands the world much earlier than can speak, the context is there.

[YeGoblynQueenne]

I dont' know if it's a good idea to mix terminology from machine learning (or game theory?) in the subject of human learning, like you do. At some point the analogies become a bit too far-fetched. Parents and babies "are agents who interact to maximise rewards"? That just sounds like taking an analogy and running with it, and then putting it in a rocket and sending it to Mars. We have no idea why and how babies think or decide to behave how they behave.

This is one reason why I'm confused about the OP's use of "labelled information". Clearly that is a term borrowed from machine learning to describe something that happens in the real world- but, what?