Phonological Concept Learning

Abstract

Linguistic and non-linguistic pattern learning have been studied separately, but we argue for a comparative approach. Analogous inductive problems arise in phonological and visual pattern learning. Evidence from three experiments shows that human learners can solve them in analogous ways, and that human performance in both cases can be captured by the same models. We test GMECCS (Gradual Maximum Entropy with a Conjunctive Constraint Schema), an implementation of the Configural Cue Model (Gluck & Bower, ) in a Maximum Entropy phonotactic-learning framework (Goldwater & Johnson, ; Hayes & Wilson, ) with a single free parameter, against the alternative hypothesis that learners seek featurally simple algebraic rules ("rule-seeking"). We study the full typology of patterns introduced by Shepard, Hovland, and Jenkins () ("SHJ"), instantiated as both phonotactic patterns and visual analogs, using unsupervised training. Unlike SHJ, Experiments 1 and 2 found that both phonotactic and visual patterns that depended on fewer features could be more difficult than those that depended on more features, as predicted by GMECCS but not by rule-seeking. GMECCS also correctly predicted performance differences between stimulus subclasses within each pattern. A third experiment tried supervised training (which can facilitate rule-seeking in visual learning) to elicit simple rule-seeking phonotactic learning, but cue-based behavior persisted. We conclude that similar cue-based cognitive processes are available for phonological and visual concept learning, and hence that studying either kind of learning can lead to significant insights about the other.

Rapid generalization in phonotactic learning

“…A range of proposed psychological mechanisms is compatible with this pattern of results, however. We have focused on an interpretation in which participants judged the test words for acceptability by evaluating whether the test words followed one or more probabilistic generalizations extracted during the exposure phase (Albright & Hayes, 2003;Hayes & Wilson, 2008;Frank & Tenenbaum, 2011;Linzen & O'Donnell, 2015;Moreton et al, 2015). Yet the same results may be consistent with a view in which participants evaluate the similarity between the consonant pattern of the test word and their memories of the consonant patterns in the exposure words (Goldinger, 1998;Nosofsky, 1986;Redington & Chater, 1996).…”

“…In Experiment 2a, for example, only four consonants were ever repeated in a word: , 5 It is unclear if the specific procedure advocated by Hayes and Wilson (2008) would be sufficient to simulate the results; we were unable to the run the code available online on our materials since it requires at least 3000 training items. GMECCS, the other published maximum entropy model (Moreton et al, 2015), does not make clear predictions about the relationship between the amount of exposure data and the generalization being acquired; the authors do report gradual convergence towards the target distribution after multiple steps ('trials') of their learning algorithm, but the relationship between the number of trials and the number of observed data points is unclear (hundreds of such 'trials' appear to correspond to a single training example). See Linzen and O'Donnell (2015) for an implementation of a maximum entropy model that is sensitive to the amount of training data in a more straightforward fashion.…”

“…This constitutes indirect negative evidence that may cause the learner to favor a narrower hypothesis, in this case that only [b] and [d] are legal onsets. Most probabilistic models will exhibit this behavior (Hayes & Wilson, 2008;Linzen & O'Donnell, 2015;Moreton et al, 2015).…”

“…Other models consider representations at multiple levels of generality from the earliest stages of learning, without waiting for multiple sounds to support a particular dimension of generalization (Hayes & Wilson, 2008;Moreton et al, 2015;Linzen & O'Donnell, 2015).…”

“…In maximum entropy models such as the Hayes and Wilson (2008) model or GMECSS (Moreton et al, 2015), for example, the well-formedness of a sound is derived from a linear combination of the weights associated with each of the phonological classes that the sound belongs to: The well-formedness of a [b] is determined by the weight for [b] and the weight for the class of voiced stops, among the various other classes that [b] belongs to. Learning the phonotactics of the language consists in determining the set of weights that is most consistent with the statistical distribution of sounds in the language.…”