[This is a quick writing exercise for in-progress work with Charles Reiss. Sorry if it doesn’t make sense out of context.]

An anonymous reviewer asks:

I wonder how the author(s) would reconcile this learning model with the evidence that both children and adults seem to aggressively generalize phonotactic restrictions from limited data (e.g. just [p]) to larger, unobserved natural classes (e.g. [p f b v]). See e.g. the discussion in Linzen & Gallagher (2017). If those results are credible, they seem much more consistent with learning minimal feature specifications for natural classes than learning maximal ones.

First, note that Linzen & Gallagher’s study is a study of phonotactic learning, whereas our proposal concerns induction of phonological rules. We have been, independently but complementarily, quite critical of the naïve assumptions inherent in prior work on this topic (e.g., Gorman 2013, ch. 2; Reiss 2017, §6); we have both argued that knowledge of phonotactic generalizations may require much less grammatical knowledge than is generally believed.

Secondly, we note that Linzen & Gallagher’s subjects are (presumably; they were recruited on Mechanical Turk and were paid $0.65 USD for their efforts) adults briefly exposed to an artificial language. While we recognize that adult “artificial language learning” studies are common practice in psycholinguistics, it is not clear what such studies contribute to our understanding of phonotactic acqusition (whatever the phonotactic acquirenda turn out to be) by children robustly exposed to realistic languages in situ.

Third, the reviewer is incorrect; the result reported by Linzen & Gallagher (henceforth L&G) is not consistent with minimal generalization. Let us grant—for sake of argument—that our proposal about rule induction in children is relevant to their work on rapid phonotactic learning in adults. One hypothesis they entertain is that their participants will construct “minimal classes”:

For example, when acquiring the phonotactics of English, learners may first learn that both [b] and [g] are valid onsets for English syllables before they can generalize to other voiced stops (e.g., [d]). This generalization will be restricted to the minimal class that contained the attested onsets (i.e., voiced stops), at least until a voiceless stop onset is encountered.

If by a “minimal class” L&G are referring to a natural class which is consistent with the data and has an extension with the fewest members, then presumably they would endorse our proposal of feature maximization, since the class that satisfies this definition is the most fully specified empirically adequate class. However, it is an open question whether or not such a class would actually contain [d]. For instance, if one assumes that major place features are bivalent, then the intersection of the features associated with [b, g] will contain the specification [−coronal], which rules out [d].

Interestingly, the matter is similarly unclear if we interpret “minimal class” intensionally, in terms of the number of features, rather than in terms of the number of phonemes the class picks out. The (featurewise-)minimal specification for a single phone (as in the reviewer’s example) is the empty set, which would (it is generally assumed) pick out any segment. Then, we would expect that any generalization which held of [p], as in the reviewer’s example, to generalize not just to other labial obstruents (as the reviewer suggests), but to any segment at all. Minimal feature specification cannot yield a generalization from [p] to any proper subset of segments, contra the anonymous reviewer and L&G. An adequate minimal specification which picks out [p] will pick out just [p].; L&G suggest that maximum entropy models of phonotactic knowledge may have this property, but do not provide a demonstration of this for any particular implementation of these models.

We thank the anonymous reviewer for drawing our attention to this study and the opportunity their comment has given us to clarify the scope of our proposal and to draw attention to a defect in L&G’s argumentation.

References

Gorman, K. 2013. Generative phonotactics. Doctoral dissertation, University of Pennsylvania.
Linzen, T., and Gallagher, G. 2017. Rapid generalization in phonotactic learning. Laboratory Phonology: Journal of the Association for Laboratory Phonology 8(1): 1-32.
Reiss, C. 2017. Substance free phonology. In S.J. Hannahs and A. Bosch (ed.), The Routledge Handbook of Phonological Theory, pages 425-452. Routledge.