| 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. |