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.

It is commonly assumed that patterns of syncretism in inflectional paradigms are restricted in some way. In this article, I show how such restrictions can reflect cognitive constraints on language learning. Namely, I construct a learning algorithm that is biased toward certain types of affix distributions in paradigms, thereby rendering them systematic. In developing this algorithm, I rely on the traditional notions of underspecification and blocking, but recast them in terms of learners' biases toward generalization strategies based on cross-situational intersections and default reasoning. This algorithm allows us to test claims about systematicity of syncretism using typological data and language acquisition studies. In the last part of the article, I present a crosslinguistic survey of verbal agreement paradigms that supports the algorithm's predictions.

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