Some thoughts on Freudenthal et al. 2016

I think it’s always nice to see someone translate a computational-level approach to an algorithmic-level approach. The other main attempt I’ve seen for syntactic categorization is Wang & Mintz (2008) for frequent frames.

Wang, H., & Mintz, T. (2008). A dynamic learning model for categorizing words using frames. BUCLD 32 Proceedings, 525-536.

Here, F&al2016 are embedding a categorization strategy in an online item-based approach to learning word order patterns, and evaluating it against qualitative patterns of observed child knowledge (early noun-ish category knowledge and later verb-ish category knowledge).

An important takeaway seems to be making a qualitative distinction between preceding vs. following context. Interestingly, this is the essence of a frame as well.

Specific comments:

(1) Types vs tokens: It’s interesting to see F&al2016 get mileage by ignoring token frequency. This is a tendency that seems to show up in a variety of learning strategies (e.g., Tolerance Principle decisions about whether to generalize are based on consideration of types rather than tokens, which itself is tied to considerations of memory storage and retrieval: Yang 2005).

Yang, C. (2005). On productivity. Linguistic variation yearbook, 5(1), 265-302.

In the intro, F&al2016 note that their motivation is one of computational cost — they say it’s less work to collect just the word, rather than keep track of both the word and its frequency. I wonder how much of an additional burden that is though. It doesn’t seem like all that much work, and don’t we already track frequencies of so many things anyway?

Also, in the simulation section, F&al2016 say “MOSAIC does not represent duplicate utterances” -- so does this mean MOSAIC already has a type bias built into it? (In this case, at the utterance level.)

(2) The MOSAIC model: I love all the considerations of developmental plausibility this model encodes, which is why it’s so striking that they use orthographically transcribed speech as input. Usually this is verboten for models of early language acquisition (e.g., speech segmentation), because orthographic and phonetic words aren’t the same thing. But here, this comes back to an underlying assumption about the initial knowledge state of the learner they model. In particular, this learner has already learned how to segment speech in an adult-like way. This isn’t a crazy assumption for 12-month-olds, but it’s also a little idealized, given what we know about the persistence of segmentation errors. Still, this assumption is no different from what previous syntactic categorization studies have assumed. What makes it stand out here is the (laudable) focus on developmental plausibility. Future work might be how robust this learning strategy is to segmentation errors in the input.

(3) Distributed representations: The Redington et al categorization approach that uses context vectors reminds me strongly of current distributed representations to word meaning (i.e., word embedding: word2vec, GloVe). Of course, the word embedding approaches aren’t a transparent translation of words into their counts, but the underlying intuition feels similar.

(4) Developmental linking: The model basis F&al2016 use for how nouns emerge early as a category is due to the structure of English utterances, coupled with the utterance-final bias of MOSAIC. Does this mean languages with verbs in the final position should have children developing knowledge of the verb category earlier (e.g., Japanese)? If so, I wonder if we see any evidence of this from behavioral or computational work.

(5) Evaluation metrics: I want to make sure I understand the categorization evaluation metric. The model’s classification of a cluster was compared against “the (most common) grammatical class assigned to each word”, but there was also a pairwise metric used that doesn’t actually need to take into account what the cluster’s class is for precision. That is, if you’re using pairwise precision (accuracy) and recall (completeness), you just get all the pairs of words from your cluster and figure out how many are actually truly in the same category -- whatever that category is -- and that’s the number in the numerator. The number in the denominator depends on whether you’re comparing against all the pairs in that cluster (precision) or all the pairs in the true adult category (recall).  So, there’s only a need to decide what an individual cluster’s category is (noun or verb or something else entirely) when you're doing the recall part.

(6) Model interpretation: In order to understand F&al2016’s concern with the number of links over time (in particular, the problem with there being more links earlier on than later on), it probably would have helped to know more about what those links refer to. I think they’re related to how utterances are generated, with progressively longer versions of an utterance linked word by word. But then, how does that relate to syntactic categorization? A little later, F&al2016 mention these links as something that link nouns together vs links verbs together, which would then make sense from a syntactic categorization perspective. But this is then different from the original MOSAIC links. Maybe links are what happens when the Redington et al. analysis is done over the progressively longer utterances provided by MOSAIC? So it’s just another way of saying “these words are clustered together based on the clustering threshold defined”.

(7) Free parameters: It’s interesting that they had to change the thresholds for Table 1 vs Table 2. The footnote explains this by saying this allows “a meaningful overall comparison in terms of accuracy and completeness”. But why wouldn’t the original thresholds suffice for that? Maybe this has something to do with the qualitative properties you’re looking for from a threshold? (For instance, the original “frequency” threshold for frequent frames was motivated partly by frames that were salient “enough” to the child. I’m not sure what you’d be looking for in a threshold for this Redington et al. analysis, though. Some sort of similarity saliency?)

Relatedly, where did the Jaccard distance threshold of 0.2 used in Table 3 come from? (Or perhaps, why is a Jaccard threshold of 0.2 equivalent to a rank order threshold of 0.45?)

(8) Noun richness analysis: This kind of incremental approach to what words are in a noun category vs a verb category seems like an interesting hypothesis for what the non-adult noun and verb categories ought to look like. I’d love to test them against child production data from these same corpora using a Yang-style productivity analysis (ex: Yang 2011).

Yang, C. (2011). A statistical test for grammar. In Proceedings of the 2nd workshop on Cognitive Modeling and Computational Linguistics (pp. 30-38). Association for Computational Linguistics.

Some thoughts on Hochstein et al. 2017

As a cognitive modeler, I love having these kind of theoretically-motivated empirical data to think about. Here, I wonder if we can unpack different possible causes of the ASD children’s behavior using something like the RSA model. We have distinct patterns of behavior to account for with details on the exact experimental context, and a really interesting separation of two steps involved in appropriately using scalar implicatures (where it seems like the ASD kids fail to cancel the implicature when they should).

Other thoughts:

(1) After reading the introduction and the difference between the ignorance implicature and the epistemic step, I now have a renewed appreciation for symbolic representation. In particular, the text descriptions of each of these made my head spin for awhile, while the symbolic representation was immediately comprehensible (and then I later worked out my own text description). My take: ignorance implicature not(believe(p)) = I don’t know if p is true”; epistemic step believe(not(p))= “I know p specifically is not true (as opposed to other things I might believe about p or whatever else)”.

(2) The basic issue with prior experimental work that H&al2017 highlight is that the Truth-Value Judgment Task (TVJT) is not the normal language comprehension process. This is because normal language comprehension involves you inferring the world from the utterance expressed. In the TVJT, in contrast, you’re given the world and asked if you would say a particular utterance - which is why RSA models capturing the TVJT cast it as an utterance endorsement process instead. But this highlights how important naturalistic conversational usage may be for getting at knowledge in populations where accessing that knowledge may be more fragile (like kids). The Partial Knowledge Task of H&al2017 is an example of this, where we see something like a naturalistic task in which participants have to use their implicit calculation (or not) of the implicature to make a judgment about the state of the world.

(3) Interestingly, something like the partial knowledge task setup has already been implemented in the RSA framework by Goodman & Stuhlmueller 2013, and addresses neurotypical adult behavior about when implicatures are and (importantly) aren’t computed, depending on speaker knowledge. Notably, this is where we see an ASD difference in the H&Al2017 studies — ASD kids don’t seem to use their ignorance implicature computation abilities here, and instead go ahead with the scalar implicature calculation.

I wonder how the H&al2017 behavior patterns play out in an RSA model. Would it have  something to do with the recursive reasoning component if ASD kids don’t care about speaker knowledge? Or is there a way to keep the recursive social reasoning, but somehow skew probabilities to get this response behavior? (Especially since ASD Theory of Mind ability didn’t correlate with this response behavior.)

Goodman, N. D., & Stuhlmüller, A. (2013). Knowledge and implicature: Modeling language understanding as social cognition. Topics in Cognitive Science, 5(1), 173-184.

Some thoughts on Tanaka-Ishii 2017

It’s really interesting to see someone coming at language development from a very different perspective (here: statistical physics). Different terminology means different ways of talking about the same ideas — and this highlighted for me how comfortable I’ve become with my own terminology, and how foreign it can seem when someone uses different terminology (see comments on long-range dependency below).

Specific thoughts:

(1) Implications for language development

(a) I don’t find it all that surprising that early child productions have these long-range correlation properties. This may be because of my naive understanding of power-law relationships, but basically, power-law relationships aren’t a language-specific thing, so why shouldn’t they appear in early child productions too? It made me smile, though, to see this author then use the existence of long-range correlation as an argument for an “innate mechanism of the human language faculty”. I didn’t really see that thought cashed out later though, and maybe that’s for the best.

(b) In the discussion section, the author says “This would require more exhaustive knowledge of long-range memory in natural language, and the model would have to integrate more complex schemes that possibly introduce n-grams or grammar models.” — This made me smile, too. You mean we might need syntactic structure to explain language development? Couldn’t be.

(2) Equation 1, which is correlation at a distance s: I think it’s worth thinking about the intuition of this. It captures the similarity of two subsequences s distance apart, with respect to their deviation from the mean value. Interpretation for word frequency: same frequency (which differs from mean by some amount) s words away. So this means long-range correlation is a power-law relationship w.r.t correlation. That is, it’s a power-law in time for word usage by frequency, not just in overall frequency irrespective of time.

(3) Working with kid data

(a) The author talks about the analysis of one child’s utterances and how things are still under development, but the analysis is effectively over word use in sequences, so it’s not clear how complex the syntactic and semantic knowledge needs to be for this to occur. That is, it’s not surprising that a swath of data between two and five shows this relationship. More interesting would have been this analysis at two vs. three vs. four. Later in the paper, the author says “In early childhood speech, utterances are still lacking in full vocabulary, ungrammatical, and full of mistakes. Therefore, the long-range correlation of such speech must be based on a simple mechanism other than linguistic features such as grammar that we generally consider.” - This comes back to assumptions about what knowledge develops at what age. “Ungrammatical” isn’t very accurate, especially when we’re talking four- and five-year-olds.

(b) I love seeing the author leverage cross-linguistic data, but how old were these kids? Age matters a bunch. And how many words were in these datasets?

(4) Understanding the different generative models

(a) The Simon model is described as “the rich get richer”, which seems like the intuition for the Chinese Restaurant Process (CRP). I definitely understand that this is uniform sampling from previous elements (in time, this means sampling from the past), plus a little for a new element. Except then the Pitman-Yor can reduce to a CRP when a is 0, and Pitman-Yor is meant to be different from Simon. Based on Figure 10, there’s clearly a major difference (the autocorrelation isn’t there for Pitman-Yor), but the intuition of what’s different is hard to grasp.

(b) I’m not sure I understand the issue described here for the Simon model: “the vocabulary growth (proven to have exponent 1.0) is too fast”. Isn’t the Simon model meant to be about sequences in time? Or is the author referring to people who have tried to match child vocabulary development to a Simon model? Or maybe this refers to the left panel of the figures where sometimes we see divergence from a strict Zipf’s law?

Some thoughts on Dye et al. 2017

I really like the clean layout of this approach and its mathematical predictions, even if I sometimes had to re-read some of the pieces to make sense of them. (I suspect this may be due to the length limitations.) In particular, this strikes me as an example of good corpus work motivated by interesting theoretical questions, and which makes sure to connect the results to the bigger picture of language change, language acquisition (both first and second), and language use.

More specific thoughts:

(1) The abstract mentions smoothing information over discourse to make nouns “more equally predictable in context”— I had some trouble figuring out what this meant. It shows up again in the section discussing grammatical gender, in the context of uncertainty over an utterance. My best guess is that this means at any point along the utterance, we can predict what noun is coming with probabilities that are more uniform?

Possibly related: “While the average uncertainty following the determiners was similar across languages, German determiners supported much greater entropy reduction than their English equivalent.” — So does this mean the range of entropy reduction was greater for German, even if on average it all washed out compared to English? And if it does wash out on average, then is the German gender system on determiners helping communicative efficiency in general, compared to English? This seems like it’s related to this comment that occurs a bit later: “However, whereas German provided a substantial entropy offset, English provided none at all.” What does an entropy offset refer to?

Also related: Trying to understand what’s going on in Figure 1. Are the two blue lines English vs. German? If so, where are the 10.17 and 10.55 coming from? They seem like they refer to different noun frequencies (based on the y axis). Is the idea that the y axis shows how many more nouns could be used with a certain entropy? If so, then the way to read this is that the three dotted lines come from another calculation, but we see the nouniness of their effect on the y axis. And then, the way to interpret that is that more entropy yields more nouns….so decreasing entropy means more predictable which means fewer nouns…..which means lower lexical diversity? Or does having fewer nouns possible mean you get to be more precise, and so when you sum over all contexts, you get more lexical diversity? I think that’s what this comment indicates: “German speakers appear to use the entropy reduction provided by noun class to choose nouns that are more specific, resulting in greater nominal diversity.”

(2) I’m a little surprised by the claim that it’s mostly adult speakers who innovate — all the first language acquisition work I’ve seen would suggest the bottleneck of L1 acquisition is a non-trivial cause of change (which I’m equating to how “innovation” is used here.) This may be a bias on my part because of my own work on Old English to Middle English word order change, with the idea it was caused in no small part by selective filters on first language learning: Pearl & Weinberg 2007.

Pearl, L., & Weinberg, A. (2007). Input filtering in syntactic acquisition: Answers from language change modeling. Language learning and development, 3(1), 43-72.

(3) Following up on the idea that adjectives in English reduce noun entropy, can we then get adjective ordering out of this (and link it to perceived subjectivity, a la Scontras et al. 2017?) In particular, is the more subjective adjective, which is further away, “more discriminative” or “less definite”? (Less definite seems to be in the same vein.) Is perceived subjectivity somehow tied to frequency?

Scontras, G., Degen, J., & Goodman, N. D. (2017). Subjectivity predicts adjective ordering preferences. Open Mind, 1, 53-65.

Some thoughts on Perkins et al. 2017

I really enjoy seeing Bayesian models like this because it’s so clear exactly what’s built in and how. In this particular model, a couple of things struck me: 

(1) This learner needs to have prior (innate? definitely linguistic) knowledge that there are three classes of verbs with different properties. That actually goes a bit beyond just saying a verb has some probability of taking a direct object, which I think is pretty uncontroversial.

(2) The learner only has to know that its parsing is fallible, which causes errors — but notably the learner doesn’t need to know the error rate(s) beforehand. So, as P&al2017 note in their discussion, this means less specific knowledge about the filter has to be built in a priori.

Other thoughts:

(1) Thinking some about the initial stage of learning P&al2017 describe in section 2: So, this learner isn’t supposed to yet know that a wh-word can connect to the object of the verb. It’s true that knowing that specific knowledge is hard without already knowing which verbs are transitive (as P&al2017 point out). But does the learner know anything about wh-words looking for connections to things later in the utterance? For example, I’m thinking that maybe the learner encounters other wh-words that are clearly connected to the subject or object of a preposition: “Who ate a sandwich?” “Who did Amy throw a frisbee to?”. In those cases, it’s not a question of verb subcategorization - the wh-word is connecting to/standing in for something later on in the utterance. 

If the learner does know wh-words are searching for something to connect to later in the utterance, due to experience with non-object wh-words, then maybe a wh-word that connects to the object of a verb isn’t so mysterious (e.g., “What did John eat?”). That is, because the child knows wh-words connect to something else and there’s already a subject present, that leaves the object. Then, non-basic wh-questions actually can be parsed correctly and don’t have to be filtered out. They in fact are signals of a verb’s transitivity.

Maybe P&al2017’s idea is that this wh-awareness is a later stage of development. But I do wonder how early this more basic wh-words-indicate-a-connection knowledge is available.

(2) Thinking about the second part of the filter, involving delta (which is the chance of getting a spurious direct object due to a parsing error): I would have thought that this depended on which verb it was. Maybe it would help to think of a specific parsing error that would yield a spurious direct object. From section 5.1, we get this concrete example: “wait a minute”, with “a minute” parsed as a direct object. It does seem like it should depend on whether the verb is likely to have a direct object there to begin with, rather than a general direct object hallucination parsing error. I could imagine that spurious direct objects are more likely to occur for intransitive verbs, for instance.

I get that parsing error propensity (epsilon) doesn’t depend on verb, though.

(3) Thinking about the model’s target state: P&al2017 base this on adult classes from Levin (1993), but I wonder if it might be fairer to adjust that based on the actual child-directed speech usage (e.g., what’s in Table 2). For example, if “jump” was only ever used intransitively in this input sample, is it a fair target state to say it should be alternating? 

I guess this comes down to the general problem of defining the target state for models of early language learning. Here, what you’d ideally like is an output set of verb classes that corresponds to those of a very young child (say, a year old). That, of course, is hard to get. Alternatively, maybe what you want to have is some sort of downstream evaluation where you see if a model using that inferred knowledge representation can perform the way young children are attested to in some other task.

For example, one of the behaviors of this model, as noted in section 5.1, is that it assigns lots of alternating verbs to be either transitive or intransitive. It would be great to test this behaviorally with kids of the appropriate age to see if they also have these same mis-assignments.

(4) Related to the above about the overregularization tendencies: I love the idea that P&al2017 suggest in the discussion about this style of assumption (i.e.,“the parser will make errors but I don’t know how often”). They note that it could be useful for modeling cases of child overregularization. We certainly have a ton of data where children seem more deterministic than adults in the presence of noisy data. It’d be great to try to capture some of those known behavioral differences with a model like this.

Some thoughts on Stevens et al. 2017

It’s really nice to see an RSA model engaging with pretty technical aspects of linguistic theory, as S&al2017 do here. In these kinds of problems, there tend to be a lot of links to follow in the chain of reasoning, and it’s definitely not easy to adequately communicate them in such a limited space. (Side note: I forget how disorienting it can be to not know specific linguistics terms until I try to read them all at once in an abstract without a concrete example. This is a good reminder to those of us who work in more technical areas: Make sure to have concrete examples handy. The same thing is true for walking through the empirical details with the prosodic realizations as S&al2017 have here —  I found the concrete examples super-helpful.)

Specific thoughts:

(1) For S&al2017, “information structure” = inferring the QUD probabilistically from prosodic cues?

 (2) I think the technical linguistic material is worth going over, as it connects to the RSA model. For instance, I’m struggling a bit to understand the QUD implications for having incomplete answers vs. having complete answers, especially as it relates to a QUD’s compatibility with a given melody. 

For example, when we hear “Masha didn’t run QUICKLY”, the QUD is something like “How did Masha run?”. That’s an example of an incomplete answer. What’s a complete answer version of this scenario, and how does this impact the QUD? Once I get this, then I think it makes complete sense to use the utility function defined in equation (10). 

(3) I was struck by S&al2017’s notational trick, where they get out of the recursive social reasoning loop of literal listener to speaker to pragmatic listener. Here, it’s utility function to speaker to hearer because they’re presumably trying to deemphasize the social reasoning aspect? Or they just thought it made more sense described this way?

(4) About those results:

Figure 2: It’s nice to see modelers investigating the effect of the rationality (softmax) parameter in the speaker function. From the look of Figure 2, speakers need to be pretty darned rational indeed (really exaggerate endpoint behavior) in order to get any separation in commitment certainty predictions. 

Thinking about this intuitively, we should expect the LH Name condition (MASHA didn’t run quickly) to continue to be ambivalent about commitment to Masha running at all. That definitely shows up. I think. (Actually, I wonder if if might have been more helpful to ask participants to rate things on a scale from 1 (No, certainly not) to 7 (Yes, certainly so). That seems like it would make a 4 score easier to interpret (4 = maybe yes, maybe no). Here, I’m a little unsure how participants were interpreting the middle of the scale. I would have thought “No, not certain” would be the “maybe yes, maybe no” option, and so we would expect scores of 1. This is something of an issue when we come to the quantitative fit of the model results to the experimental results. Is the behavioral difference shallow just because of the way humans were asked to give their answers?  The way the model probability is calculated in (16) suggests that the model is operating more under the 1 = “no, certainly not” version (if I’m interpreting it correctly - -you have the “certainly yes” option contrasted with the “certainly not” option).

Clearly, however, we see a shift up in human responses in Figure 3 for the LH Adverb condition (Masha didn’t run QUICKLY), which does accord with my intuitions. And we get them from the model in Figure 2, as long as that rationality parameter is turned way up. (Side note: I’m a little unclear about how to interpret the rationality parameter, though. We always hedge about it in our simulation results. It seems to be treated as a noise parameter, i.e., humans are noisy, so let’s use this to capture some messy bits of their behavior. In that case, maybe it doesn’t mean much of anything that it has to be turned up so high here.)

Thoughts on Orita et al. 2015

I really appreciated how O&al2015 used the RSA modeling framework to make a theory (in this case, about discourse salience) concrete enough to implement and then evaluate against observable behavior. As always, this is the kind of thing I think modeling is particularly good at, so the more that we as modelers emphasize that, the better.

Some more targeted thoughts:

(1) The Uniform Information Density (UID) Hypothesis assumes receiving information in chunks of approximately the same size is better for communication. I was trying to get the intuition of that down -- is it that new information is easier to integrate if the amount of hypothesis adjustment needed based on that new information is always the same? (And if so, why should that be exactly? Some kind of processing thing?)

Related: If I’m understanding correctly, the discourse salience version of the UID hypothesis means more predictable forms become pronouns. This gets cashed out initially as the surprisal component of the speaker function in (3) (I(words; intended referent, available referent)), which is just about vocabulary specificity (that is, inversely proportional w.r.t how ambiguous the literal meaning of the word is). Then 3.2 talks about how to incorporate discourse salience. In particular, (4) incorporates the literal listener interpretation given the word, and (5) is just straight Bayesian inference where the priors over referents are what discourse salience affects. Question: Would we need these discourse-salience-based priors to reappear in the pragmatic listener level if we were using that level? (It seems like they belong there too, right?)

Speaking of levels, since O&al2015 are modeling speaker productions, is the S1 level the right level? Or should they be using an S2 level, where the speaker assumes a pragmatic listener is the conversational partner? Maybe not because we usually save the S2 level for metalinguistic judgments like endorsements in a truth-value judgment task?

(2) Table 1: Just looking at the log likelihood scores, it seems like frequency-based discourse salience is the way to go (and this effect is much more pronounced in child-directed speech). However, the text in the discussion by the authors notes how the recency-based discourse salience version has better accuracy scores, though most of that is due to the proper name accuracy since every model is pretty terrible at pronoun accuracy. I’m not entirely sure I follow the authors’ point about why the accuracy and log likelihood scores don’t agree on the winner. If the recency-based models return higher probabilities for a proper name, shouldn’t that make the recency-based log likelihood score better than the frequency-based log likelihood score? Is the idea that some proper names get all the probability (for whatever reason) for the recency-based version, and this so drastically lowers the probabilities of the other proper names that a worse log likelihood results?

But still, no matter what, discourse saliency looks like it’s having the most impact (though there’s some impact of expression cost). In the adult-directed dataset, you can actually get pretty close to the best log likelihood with the -cost frequency-based version (-1017) vs. the complete  frequency-based version (-958). But if you remove discourse salience, things get much, much worse (-6904). Similarly, in the child-directed dataset, the -cost versions aren’t too much worse than the complete versions, but the -discourse version is horrible.

All that said, what on earth happened with pronoun accuracy? There’s clearly a dichotomy between the proper name results and the pronoun results, no matter what model version you look at (except maybe the adult-directed -unseen frequency-based version).

(3) In terms of next steps, incorporating visual salience seems like a natural step when calculating discourse saliency. Probably the best way to do this is as a joint distribution in the listener function for the prior? (I also liked the proposed extension that involves speaker identity as part of the relevant context.) Similarly, incorporating grammatical and semantic constraints seems like a natural extension that could be implemented the same way. Probably a hard part is getting plausible estimates for these priors?

Thoughts on Yoon et al. 2017

I really enjoyed seeing another example of a quantitative framework that builds a pipeline between behavioral data and modeling work. The new(er?) twist in Y&al2017 for me is using Bayesian data analysis to do model-fitting after the behavioral data were collected (originally collected to evaluate the unfitted model predictions). It definitely seems like the right thing to do for model validation. More generally, this pipeline approach seems like the way forward for a lot of different language science questions where we can’t easily manipulate the factors we want experimentally. (In fact, you can see some of the trouble here about how to interpret the targeted behavioral manipulations even still.)

More targeted thoughts:

(1) I liked seeing this specific implementation of hedging, which is a catch-all term for a variety of behaviors that soften the content (= skew towards face-saving). It’s notable that the intuition seems sensible (use more negation when you want to face-save), but the point of the model and subsequent behavioral verification is to concretely test that intuition. Just because something’s sensible in theory doesn’t mean it’s true in practice. 

A nice example of this for me was the prediction in Figure 2 that more negations occur when the goal is both social and informative (both-goal), rather than just social. Basically, the social-only speaker tells direct white lies, while the informative-only speaker just tells the truth, so neither uses negation as much as the both-goal speaker for negative states.

(2) I think I need to unpack that first equation in the Polite RSA model. I’m not familiar with the semicolon notation — is this the joint utility of the utterance (w) ….given the state (s)….and given the goal weights (epistemic and social)? (This is what shows up P_S2.) The rest I think I follow: the first term is the epistemic weight * the negative surprisal of L0; the second term is the social weight * the value for those states that are true for L0; the third term is the cost of the utterance (presumably in length, as measured by words).

(3) Figure 1: How funny that “it wasn’t terrible” is accepted at near ceiling when the true value is “good” (4 out of 5) or “amazing” (5 out of 5).  Is this some kind of sarcasm/curmudgeonly speaker component at work?

(4) For the production experiment, I keep thinking this is the kind of thing where a person’s own social acuity might matter. (That is, if the poem was 2 out of 5, and a tactful vs. direct person is asked what they’d say to make someone else feel good, you might get different responses.) I wonder if they got self-report on how tactful vs. direct their participants thought they were (and whether this actually does matter).

I also have vague thoughts that this is the kind of task you could use to indirectly gauge tactful vs. direct in neurotypical people (say, for HR purposes) as well as in populations that struggle with non-literal language. This might explain some of the significant deviations in the center panel of Figure 2 for the low states (1 and 2): the participants for social-only used negation (rather than white lies, presumably) much more than predicted. (Though maybe not once the weights and free parameters are inferred — the fit is pretty great.)

Maybe this social acuity effect comes out in the Bayesian data analysis, which inferred the participant weights. There wasn’t much participant difference between the social-only weight and the both weight (0.57 vs. 0.51). Again, I’d be really curious to see if this separated out by participant social acuity.

(5) I found the potential (non?)-difference between “it wasn’t amazing” and “it wasn’t terrible” really interesting. I keep trying to decide if I differ in how I deploy them myself. I think I do — if I’m talking directly to the person, I’ll say “it wasn’t terrible”; if I’m talking to someone else about the first person’s poem, I’ll say “it wasn’t amazing”. I’m trying to ferret out why I have those intuitions, but it probably has something to do with what Y&al2017 discuss at the very end about the speaker’s own face-saving tactics.

Thoughts on Meylan et al. 2017 in press

I really like how M&al2017 have mathematically cashed out the competing hypotheses (and given the intuitions of how Bayesian data analysis works — nice!). But something that I don’t quite understand is this: The way the model is implemented, isn’t it a test for Noun-hood rather than Determiner-hood (more on this below)? There’s nothing wrong with testing Noun-hood, of course, but all the previous debates involving analysis of Determiners and Nouns have been arguing over Determiner-hood, as far as I understand.

Something that also really struck me when trying to connect these results to the generativist vs. constructionist debate: How early is earlier than expected for abstract category knowledge to develop? This seems like the important thing to know if you’re trying to interpret the results for/against either perspective (more on this below too).

Specific thoughts:

(1) How early is earlier than expected?

(a) In the abstract, we already see the generativist perspective pitched as “the presence of syntactic abstractions” in children’s “early language”. So how early is early?  Are the generativists really all that unhappy with the results we end up seeing here where a rapid increase in noun-class homogeneity starts happening around 24 months? Interestingly, this timing correlates nicely with what Alandi, Sue, and I have been finding using overlap-based metrics for assessing categories in VPs like negation and auxiliary (presence of these in adult-ish form between 20 and 24 months for our sample child).

(b) Just looking at Figure 3 with the split-half data treatment, it doesn’t look like there’s a lot of increase in noun-class-ness (productivity) in this age range. Interestingly, it seems like several go down (though I know this isn’t true if we’re using the 99.9% cutoff criterion). Which team is happy with these results, if we squint and just use the visualizations as proxies for the qualitative trends? While the generativists would be happy with no change, they’d also be surprised by negative changes for some of these kids. The constructivists wouldn’t be (they can chalk it up to “still learning”), but then they’d expect more non-zero change, I think.

(c)  The overregularization hypothesis is how M&al2017 explain the positive changes in younger kids and the negative changes for older kids. In particular, they say older kids have really nailed the NP —> Det N rule, and so use more determiner noun combinations that are rare for adults. So, in the terms of the model, what would be happening is that more nouns get their determiner preferences skewed towards 0.5 than really ought to be, I think. If that happens, then shouldn’t the distribution be more peaked around 0.5 in Figure 1? If so, that would lead to higher values of v. So wouldn’t we expect even higher v values (i.e., a really big increase) if this is what’s going on, rather than a decrease to lower v values?  Maybe the idea is that the peak in v is happening because of overregularization, and then the decrease is when kids settle back down. That is, adult-like knowledge of a noun category existing is when we get the peak v value (which may in fact be higher than actual adult values). Judging from Figure 4, it looks like this is happening between 2 and 3. Which is pretty young. Which would make generativists happy, I think? So the conclusion that “these results are broadly consistent with constructivist hypotheses” is somewhat surprising to me. I guess it all comes back to how early is earlier than expected.

(d) Sliding-window results: If we continue with this idea that a peak in v value is the indication kids have hit adult-like awareness of a category (and may be overregularizing), what we should be looking for is when that first big peak happens (or maybe big drop after a peak). Judging from the beautifully rainbow-colored Figure 5, it looks like this happens pretty early on for a bunch of these kids (Speechome, Eve, Lily, Nina, Aran, Thomas, Alex, Adam, Sarah).  So the real question again: how early is earlier than expected? (I feel like this is exactly the question that pops up for standard induction problem/Poverty of the Stimulus arguments.)

(2) Modeling:  

(a) I like how their model involves both categories, rather than just Determiner. This is exactly what Alandi and I realized when we started digging into the assumptions behind the different quantitative metrics we examined.

(b) I also like that M&al2017 explicitly do a side-by-side model comparison (direct experience = memorized amalgam from the input vs. productive inference from abstracted knowledge). Bayesian data analysis is definitely suited for this, and then you can compare which representational hypothesis best fits over different developmental stages for a given child. Bonus for this modeling approach: the ability to estimate confidence intervals.

We can see this in the model parameters, too: n = impact of input (described as ability to “match the variability in her input” = memorized amalgams), v = application of knowledge across all nouns (novel productions = “produce determiner-noun pairs for which she has not received sufficient evidence from caregiver input” = abstract category). It’s really nice to see the two endpoint hypotheses cashed out mathematically.

(c) Testing noun-hood: If I understand this correctly, each noun has a determiner preference (0 = all “a”, 1 = all “the”, 0.5 = half each). Cross-noun variability is then drawn from an underlying common noun distribution if all nouns are from same class (testing whether “nouns behave in a more class-like fashion”). So, this seems like testing for Noun-hood based on Determiner usage, which I quite like.  But it’s interesting that M&al2017 describe this as testing “generalization of determiner use across nouns”, which makes it seem like they’re testing for Determiner instead. I would think that if they want to test for Determiner, they’d swap which one they’re testing classhood for (i.e., have determiners with a noun preference, and look for individual determiner values to all be drawn from the same underlying Determiner distribution).

(3) Critiquing previous approaches involving the overlap score: 

M&al2017 say that overlap might increase simply because children heard more determiner+noun pairs in the input (i.e., it’s due to memorization, and not abstraction). I’m not sure I follow this critique, though — I’m more familiar with Yang’s metrics, of course, and those do indeed take a snapshot of whether the current data are compatible with fully productive categories vs. memorized amalgams from the input. The memorized amalgam assessment seems like it would indeed capture whether children’s output is compatible with memorized amalgams (i.e., more determiner+noun pairs in their input). 

(4) Data extraction (from appendix): 

(a) I like that they checked whether the nouns should be collapsed together or if instead morphological variants should be treated separately (e.g., dog/dogs as one or two nouns). In most analyses I’ve seen, these would be treated as two separate nouns. 

(b) Also, the supplementary material really highlights the interesting splits in PNAS articles, where all the stuff you’d want to know to actually replicate the work isn’t in the main text. 

(c) Also, yay for github code! (Thanks, M&al2017 — this is excellent research practice.)

(5) M&al2017 highlight the need for dense naturalistic corpora in their discussion - I feel like this is an awesome advertisement for the Branwauld corpus: http://ucispace.lib.uci.edu/handle/10575/11954. Seriously. It may not have as much child-directed as the Speechome, but it has a wealth of longitudinal child-produced data. (Our sample from 20 to 24 months has 2154 child-produced VPs, for example, which doesn’t sound too bad when compared to Speechome’s 4300 NPs.)

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.)

Thoughts on Han et al. 2016 + Piantadosi & Kidd 2016 + Lidz et al. 2016

As with our previous reading, I really appreciate the clarity with which the arguments are laid out by H&al2016, P&K2016’s reply, and L&al2016’s reply-to-the-reply. I can also see where some confusion is arising in the debates surrounding this — there seems to be genuine ambiguity in the way terminology is used to describe the different perspectives about the source of linguistic knowledge (e.g., what “endogenous” actually refers to — more on this below). I also really like seeing a clear, concrete example of solving an induction problem that involves fairly abstract knowledge, and using knowledge internal to the learner to do so.

Specific thoughts:

(1) Endogenous: 

It’s interesting that the basic distinction drawn in the opening paragraph of H&al2016 is between domain-general vs. language-specific innate mechanisms, which is different than simply endogenous vs. not (that is, it’s a question of which endogenous it is): “…did the data…allow for construction of knowledge through general cognitive mechanisms…or did that experience play more of a triggering role, facilitating the expression of abstract core knowledge…”

I think the reply by P&K2016 hits on an interesting terminology issue. For H&al2016, endogenous means “internal to the child”; in contrast, P&K2016 seem to go with the more narrow definition of “genetically specified with no external influence”. This then makes P&K2016 question what to make of parents having different grammars than their kids. For H&al2016, I think the point is simply that something internal to the child  — and not solely genetic — is responsible. It’s possible that the internal something developed from a combination of genetics & other data experience, but it’s clearly something that can differ between parents and children. (General point: Just because something’s genetic doesn’t mean it doesn’t interact with the environment to produce the observed result. Concrete example: Height depends on genetics and nutrition.) 

This issue about what kind of endogenous knowledge (rather than simply is it or isn’t it endogenous) is also something P&K2016 pick up on in their reply. They specifically bring up domain-general endogenous factors as possibilities (“differences in memory, motivation, or attention”) and note that the “root cause of the variation may not even be linguistic”. This, as far as I can tell, doesn’t go against H&el2016’s original point. So, it seems like P&K2016 are targeting a more specific position than H&al2016 argued in their paper, though H&al2016’s initial introductory wording suggested that more specific position.

I think L&al2016’s reply-to-the-reply reflects the ambiguity in this position — they note that their paper provides evidence for “endogenous linguistic content”. While the basic reading of this is simply “knowledge about language that’s internal” (and so silent about whether the origin of this knowledge is domain-specific or domain-general), I think it’s easy to interpret this as arguing for the origin of that knowledge to also be language-specific. The final paragraph of L&al2016’s reply underscores this interpretation, as they argue against domain-general mechanisms like memory, attention, and executive function being the source of the endogenous linguistic knowledge. And that, of course, is what P&K2016 (and many others) aren’t fond of. 

(2) Empiricism, P&K2016’s closing: What’s a “reasonable version” of empiricism? My (perhaps naive) understanding was that empiricism believes everything is learned and nothing is innate, which I didn’t think anyone believed anymore. I thought that as soon as you believe even one thing is innate (no matter what flavor of innate it is), you’re by definition a nativist. Maybe this is another example of terminology being used differently by the different perspectives.

(3) One of the interesting things about the experiments in H&al2016 is that the experimental stimuli could be the driving force of grammatical choice. That is, there’s a possibility that people did have multiple grammars before the experiment, but selected one during the course of the experiment and then learned it. This is one way that could happen:

(a) When finally presented with data that require a choice in the verb-raising parameter, participants make that choice. 

(b) Primed by the previous choice (which may have involved some internal computation that was effortful and which they don’t want to repeat), participants stick with it throughout the first test session, thereby reinforcing that choice. 

(c) This prior experience is then reactivated in the second test session a month later, and used as a prior in favor of whichever option was previously chosen. 

If this is what happened, then by the act of testing people, we enable the convergence on a single option where there were previously multiple ones - how quantum mechanics of us…