I thought this was a really lovely Cog Sci paper showcasing how to combine experimental & computational methodologies (and still make it all fit in 6 pages). The authors really tried to give the intuitions behind the modeling aspects, which makes this more accessible to a wider audience. The study does come off as a foundational one, given the many extensions that could be done (involving effects in younger word learners, cross-linguistics applications, etc.), but I think that's a perfectly reasonable approach (again, given the page limitations). I also thought the empirical grounding was really lovely for the computational modeling part, especially as relating to the concept priors. Granted, there are still some idealizations being made (more discussion of this below), but it's nice to see this being taken seriously.
Some more targeted thoughts:
--> One issue concerns the age of the children tested experimentally (4 years old) (and as Gagliardi et al. mention, a future study should look at younger word learners). The reason is that 4-year-olds are fairly good word learners (and have a vocabulary of some size), and presumably have the link between concept and grammatical category (and maybe morphology and grammatical category for the adjectives) firmly established. So it maybe isn't so surprising that grammatical category information is helpful to them. What would be really nice is to know when that link is established, and the interaction between concept formation and recognition/mapping to grammatical categories. I could certainly imagine a bootstrapping process, for instance, and it would be useful to understand that more.
--> The generative model assumes a particular sequence, namely (1) choose the syntactic category, (2) choose the concept, and (3) choose instances of that concept. This seems reasonable for the teaching scenario in the experimental setup, but what might we expect in a more realistic word-learning environment? Would a generative model still have syntactic category first (probably not), or instead have a balance between syntactic environment and concept? Or maybe it would be concept first? And more importantly, how much would this matter? It would presumably change the probabilities that the learner needs to estimate at each point in the generative process.
--> I'd be very interested to see the exact way the Mechanical Turk survey was conducted for classifying things as examples of kinds, properties, or both (and which words were used). Obviously, due to space limitations, this wasn't included here. But I can imagine that many words might easily be described as both kind & concept, if you think carefully enough (or maybe too carefully) about it. Take "cookie", for example (a fairly common child word, I think): It's got both kind (ex: food) and property aspects (ex: sweet) that are fairly salient. So it really matters what examples you give the participants and how you explain the classification you're looking for. And even then, we're getting adult judgments, where child judgments might be more malleable (so maybe we want to try this exercise with children too, if we can).
--> Also, on a related note, the authors make a (reasonable) idealization that the distribution of noun and adjective dimensions in the 30-month-old CDIs are representative of the "larger and more varied set of words" that the child experimental participants know. However, I do wonder about the impact of that assumption, since we are talking about priors (which drive the model to use grammatical category information in a helpful way). It's not too hard to imagine children whose vocabularies skew away from this sample (especially if they're older). Going in the other direction though, if we want to try to extend this to younger word learners, then the CDIs start to become a very good estimate of the nouns and adjectives these children know, so that's very good.