[SiVal]

The alternative to UG is not behaviorism; there are countless alternatives. There are all sorts of learning algorithms that are more plausible than UG or behaviorism.

Yes, it IS clear that humans have an innate ability to LEARN languages, as you insist. Unfortunately, UG denies this, claiming that we CAN'T possibly learn anything as rich and complex as a human language in so short a time with so little, and such messy, input, and since humans have NO innate ability to LEARN human (first) languages, they must instead GROW them "like you grow teeth."

"The alternative to UG" isn't behaviorism, it's that languages are LEARNED.

>"Quite the contrary. Anyone starting fresh would probably start with an assumption about some innate ability to learn language."

You're so right, except that your claim is not contrary to me, it's contrary to UG. Now try to convince the modern linguists of your theory that humans have the innate ability to LEARN first languages and see how that goes.

[Afton]

You are substantially mis-characterizing modern linguistic theory/linguists. The 'grow them like you grow teeth' is meant to indicate that, given certain inputs/environment, a child will develop normal language function. You don't need to 'learn' it in the sense that you do need to learn e.g. how to read. The reason for the contrast (argues a linguist) is that we have some internal cognitive structures that react to certain kinds of input, namely linguistic input, and that that reaction is called 'learning your first language(s)'. They disagree with the 'common sense' approach, that learning your first language is just like learning anything else.

I would characterize the debate between linguists and a certain class of cognitive scientists like this:

CogSci: Hey, you keep talking about UG/Innate mechanisms! We don't like that/it seems implausible. Instead, we should just have general learning algorithms that can be utilized to learn language!

Ling: Cool! Show us! Show us!

CogSci: Well...Here's a machine learning model that can learn English past tense with the following training data.

Ling: Oh. Um. Hmmm. Yeah, the data is more complex than that. How far can you get with this data (unloads data by the truckful). Also: that looks like how adults learn things (the kinds of errors made), not really how kids learn language (they make different kinds of errors). Can you model that?

CogSci: It's a simple model! It can't handle that data. That's for a later paper! Also, we don't care about the error classifications, as long as it looks like learning.

Ling: Ok. Let us know when that paper comes out. Have you seen this Bantu data? It's pretty cool too.

CogSci: later: Ok, look. We didn't get the model to work, but we really think your multiplying entities. I mean, it's just crazy/biologically-implausible/ugly to postulate this innate knowledge.

Ling: Yup. But here's the deal. We can't manage to actually explain everything we want even if we postulate innate rules/knowledge like crazy. Maybe we have a fundamentally broken model. Maybe machine learning really will come and eat our babies (or maybe the kinds of things we're postulating will turn out to built on top of machine learning, as explanations at different levels). But so far, it's the best we've got.

Obviously, people write books on these arguments, so some massive simplification was done here. And there is some really cool work being done by general cognitive scientists in the language space.

[hasenj]

I'm not contradicting UG. From what I took out of the linguistics course I took at university, the idea is that part of this innate ability is an innate understanding of grammar, or rather we expect to learn grammar and so when we receive all the "input" our mind tries to make sense of it.