In this post, I want to explain why I’ve recently become interested in distinctions between implicit and explicit learning (also procedural and declarative memory – see below on the connection), and provide a quick overview of the literature I’ve been able to assimilate, as well as mention some things that seem particularly interesting to investigate with respect to phonological learning. The literature is vast, and there is thus far very little that has been done in this area in phonology (with the notable exception of the English past tense), so there’s lots of room for reading, thinking, talking and research, and I’d really welcome others’ thoughts!
The distinction came up for me in joint research with Elliott Moreton and Katya Pertsova . We (EM and JP) developed a MaxEnt model of phonotactic learning, which we found out was virtually identical to a model of visual category learning that had been developed, and then abandoned, in the late 80s. It was abandoned because it made the wrong predictions about the relative difficulty of types of visual category. To our initial surprise, when EM and KP tested the learning of the phonotactic analogues of those category types, the predictions of our model were supported. After some more thought and reading of the psychological literature, the result seemed less surprising, but no less interesting. The classic visual category learning experiments are prototypical exercises in explicit learning: you are told you are to learn a rule separating two types of object, the relevant features are extremely salient and verbalizable (e.g. color, shape, size), in training you are asked to classify objects and are given feedback, and you are sometimes even asked for your current hypothesis about the rule. When the methodology is changed so that learning becomes even somewhat less explicit (even by omitting the instruction to seek rules ) the order of difficulty changes in the direction of the predictions of our model. Phonotactic learning is typically (and naturally) more implicit: the relevant phonological features are difficult, if not impossible to verbalize, and training proceeds by providing only positive examples of one category (i.e. words that are “in” the language). Our conclusion / hypothesis is that implicit (typical phonotactic) learning is well characterized by “cue-based” models like ours, while explicit (classic visual category) learning is not – “rule-based” models do better in that domain. See our paper on what we mean by “cue-based” and “rule-based”.
Implicit / explicit learning distinctions have a long and controversial history in psychology, and also connect to a very heated debate across psychology and linguistics. The recent debate in category learning in cognitive psychology , like the older psycho-linguistic debate , revolves around the question of whether there are two separate learning systems. Two systems models in both domains make links to a distinction between declarative and procedural memory (, ). As far as I can tell, no connections have yet been made between these two two systems literatures (I haven’t even found any cross-references yet). Future attempts to make these connections will need to confront the fact “rule-based learning” is implicit in the psycho-linguistic literature, but explicit in category learning. The two systems view in language makes a distinction between knowledge of words (explicit, declarative) and knowledge of rules for how words combine (implicit, procedural) – a quite famous distinction, thanks to Pinker. The term “rule” in category learning is by definition a generalization that one can make explicitly, and is thus linked with declarative memory. So those are the differences. But there are some ways in which the meaning of rule does overlap across the domains – rules are supposed to express relatively simple generalizations (e.g. broad in application in linguistics, with sometimes a complexity metric applied to choose amongst rules, simple in terms of featural makeup in visual category learning).
In thinking and talking about the potential extensions of two systems ideas to phonology, I prefer the terms “explicit” vs. “implicit” over “declarative” vs. “procedural” as well as “words” vs. “rules”. On the first alternative, there is also no obvious way in which knowledge of phonotactics is procedural. On the second, the relatively implicit learning that we have been studying, the learning of phonotactics, is the learning of generalizations over the phonological shape of words – “rules” about “words”. Some evidence that there is a common cognitive underpinning between syntactic rules and phonotactic rules comes from the fact that both can elicit a Late Positive Component / P600 in ERP studies (see  for some recent work and references).
A clear direction for research is to try to get evidence of which memory systems (assuming that there are in fact multiple systems) are recruited in different types of phonological learning. A pioneering study in this respect  finds evidence of declarative memory being linked to what they call analogy (in fact an opaque interaction), and procedural memory linked to simple concatenation of an affix. There are a number of issues with this study, and lots of directions for further work. One of its further results that I find intriguing is that no correlation was found between working memory and in success in phonological learning. There seems to be a quite solid association between working memory and success in the classic visual category learning paradigm mentioned above . Does working memory capacity correlate with success in phonotactic learning? (Probably not.) With any other aspect of phonological learning? (Probably: see the literature on individual differences and the phonological loop, which is a type of working memory.) More broadly, are there robust individual differences in phonotactic learning and other kinds of phonological learning? One reason to be optimistic that artificial language learning might lead to insight into individual differences is that it seems to have been used in as one of the measures in all of the language learning aptitude tests that are predictive of success in natural language learning . Individual differences in implicit learning tend not to be very robust (see  for a review), so if we find a measure that has good test-retest reliability, this could be a contribution of broad interest (has anyone done test-retest with Saffran-style TP learning?) There are interesting potential connections between implicit learning in language and music that seem worth exploring . And dreaming really big, we might imagine doing very large scale studies of individual differences over the web, and collecting genetic samples. Finally, getting back to phonological theory (and to earth), if it is the case that different aspects of phonology are learned using different cognitive sub-systems (see  for some recent ERP evidence), this should have deep consequences for how we model knowledge of phonology and its learning.
 Moreton, Elliott, Joe Pater and Katya Pertsova. 2014. Phonological concept learning. Ms, University of North Carolina and University of Massachusetts Amherst. Resubmitted to Cognitive Science August 2014, revised version of 2013 paper, comments still very much welcome. Kurtz, K. J., K. R. Levering, R. D. Stanton, J. Romero and S. N. Morris (2013). Human learning of elemental category structures: revising the classic result of Shepard, Hovland, and Jenkins (1961). Journal of Experimental Psychology: Learning, Memory, and Cognition 39(2), 552–572.  Newell, B.R., Dunn, J.C., & Kalish, M. (2011). Systems of category learning: Fact or fantasy? In B.H. Ross (Ed) The Psychology of Learning & Motivation Vol 54,. 167-215 PDF  Pinker, S. & Prince, A. (1988) On language and connectionism: Analysis of a parallel distributed processing model of language acquisition. Cognition, 28, 73-193. Reprinted in S. Pinker & J. Mehler (Eds.) (1988) Connections and symbols. Cambridge, MA: MIT Press.  Ullman, M. T. (2004). Contributions of neural memory circuits to language: The declarative/procedural model. Cognition, 92(1-2). 231-270.  Ashby, F. G. and W. T. Maddox (2005). Human category learning. Annual Review of Psychology 56, 149–178.  Sanders, L., J. Pater, C. Moore-Cantwell, R. Staubs and B. Zobel. 2014. Adults Quickly Acquire Phonological Rules from Artificial Languages. Ms., UMass Amherst, available on request.
 Wong PCM, Ettlinger M, Zheng J (2013) Linguistic Grammar Learning and DRD2-TAQ-IA Polymorphism. PLoS ONE 8(5): e64983. doi:10.1371/journal.pone.0064983
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