Monday, May 15, 2017

Thoughts on Yang et al. 2017

I feel like Universal Grammar (UG) was better defined by the end of this exposition (thanks, Y&al2017!), but now I want to have a heart-to-heart about the difference between “hierarchy” and “combination”. Still, I appreciated this convenient synthesis of evidence from the generative grammar tradition, especially as it relates to the kind of considerations I have as an acquisition modeler. 

Specific thoughts:

(1) Hierarchy vs. combination:
Part 2.2: While I’m a fan of hierarchical structures being everywhere in language, I wasn’t sure how connected the newborn n-syllable tasks were to the point about hierarchy. Why does being sensitive to the number of vowels (“vowel centrality”) indicate there must be hierarchical structure? For example, what if newborns hadn’t inferred hierarchy yet, but were simply sensitive to the more acoustically salient cue of vowels — wouldn’t we see the same results, even if all they really perceived was something like V V for “baku” and “alprim”?

Similarly with the babbling examples: How do we know these are hierarchical (vs. say, linear) structures? 

Similarly with the prosodic contour distinctions for the 6- to 12-week-olds: We know they perceive the prosodic contours, but not that they recognize the words and phrases in these languages. (In fact, we assume they don’t — they haven’t really managed reliable speech segmentation yet.) So how does recognizing prosodic contour distinctions over acoustic units relate to the hierarchical structure Merge gives?

My main issue is coming down to “combinatorial” vs. “hierarchical”. I think you can make combinations of things without those things being combined hierarchically. So these two terms don’t mean the same thing to me, which is why the evidence in section 2.2 doesn’t seem as compelling about hierarchy (though it is for combinations). Contrast this with the 2.3 examples of syntactic development, where c-command definitely is about hierarchy.

(2) UG: Initially, UG is described as domain-specific principles of language knowledge, without specifying whether these are innate principles or not (and also seeming to focus on the knowledge about language, rather than, say, knowledge about how to learn language (= learning mechanism)). But then, we see UG described as “internal constraints that hold across all linguistic structures”  — though this highlights the innate component, it now doesn’t seem to indicate these constraints have to be just about language. That is, they could be constraints that apply to language as well as other things, e.g., hierarchy, which they talk about as Merge. I’m thinking visual scene parsing is similar, where you have hierarchical chunks. So this would be a vision system version of Merge. 

A little later on, we see “Universal Grammar” as the “initial state of language development” that's “determined by our genetic endowment”, which reinforces the innate component, but hedges on whether this is innate knowledge of the structure of language, or innate knowledge about how to learn language. This latter interpretation becomes more salient when they describe UG as infants interpreting parts of the environment as linguistic experience. This seems to be about the perceptual intake, and is less about knowledge of language than knowledge about what could count as language (= learning mechanism). Maybe that’s a broader definition of what it means to be a “principle of language”?

Later on in part 3.2, we get to more canonical UG examples, which are the linguistic parameters. These feel much more obviously language-specific. If they’re meant to be innate (which is how they’re typically talked about), then there we go. 

Side note: I would dearly love to figure out if specific linguistic parameters like these are derivable from other more basic linguistic building blocks. I think this is where the Minimalist Program (MP) and the Principles & Parameters (P&P) representations can meet, with MP providing the core building blocks that generate the P&P variables. I just haven’t seen it explicitly done yet. But it feels very similar to the implicit vs. explicit hypothesis space distinction that Perfors (2012) discusses, where the linguistic parameters are the explicit hypotheses generated from the MP building blocks that are capable of generating all the hypotheses in the implicit hypothesis space.

Perfors, A. (2012). Bayesian models of cognition: what's built in after all? Philosophy Compass, 7(2), 127-138.

(3) Efficient computation: I really like seeing this term here as a core factor, though I’m tempted to make it “efficient enough computation”, especially if we’re going to eventually tie this kind of thing back to evolution.

(4) Rhetorical device danger: Section 3.1 has this statement that I think can get us into hot water later on: “[I]t follows that language learners never witness the whole conjugation table…fully fleshed out, for even a single verb.”  Now we’ve just thrown down the gauntlet for some corpus analyst to hunt through a large enough sample and find just one verb that does. It doesn’t affect the main point at all, but it’s the kind of thing that can be easily misunderstood (c.f., aux inversion input for arguing against Poverty of the Stimulus).

(5) Section 3.3: “…linguistic principles such as Structure Dependence and the constraint on co-reference [c-command]…are most likely accessible to children innately” — Yes! In the sense that these principles are allowed into the hypothesis space. Accessible is definitely the right (hedgy) word, rather than saying these are the only options period.

(6) Section 3.3, on Bayesian models of indirect negative evidence : ”…for this reason, most recent models  of indirect negative evidence explicitly disavow claims of psychological realism” — I find this a bit tricksy. Reading it, you might think: “Oh! The issue is that indirect negative evidence isn’t psychologically plausible to use.” But in actuality,  the “disavowal” is about a computational-level inference algorithm being psychologically real. As far as I know, there are no claims that the computation it’s doing with that algorithm isn’t psychologically real; rather, they assume humans approximate that computation (which uses indirect negative evidence).  

Related is the stated computational "intractability" of using indirect negative evidence: I admit, I find this weird. If we’re happy to posit alternative hypotheses in a subset-superset relationship, why is it so hard to posit predictions from those two hypotheses? The hard part seems to be about defining the hypotheses so explicitly in the first place, and that doesn’t seem to be the part that’s targeted as “psychologically intractable”. If anything, it seems to be the psychologically necessary part. (The description that follows this bit in section 3.3 seems to highlight this, where Y&al2017 talk about the superset grammar existing, even if the default is the subset grammar.)

(7) Section 4.1, on the importance of empirical details: I really appreciate the pitch to make proposals account for specific empirical details. This is something near and dear to my heart. Don’t just tell me your $beautiful_theory will solve all my language acquisition problems; show me exactly how it solves them, one by one. (Minimalism, I’m looking at you. And to be fair, that’s exactly what the next-to-last sentence of section 4.1. says.)

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