title: 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.
authors.affiliation: Department of Linguistics, University of North Carolina.
authors.forename: Elliott
authors.initials: E
authors.lastname: Moreton
authors.affiliation: Department of Linguistics, University of Massachusetts.
authors.forename: Joe
authors.initials: J
authors.lastname: Pater
authors.affiliation: Department of Linguistics, University of North Carolina.
authors.forename: Katya
authors.initials: K
authors.lastname: Pertsova
mesh_terms: D003210:Concept Formation; D003463:Cues; D006801:Humans; D007858:Learning; D008037:Linguistics; D008962:Models, Theoretical
keywords: Complexity; Concept learning; Implicit learning; Inductive bias; Maximum Entropy; Phonotactic learning; Replicator Equation