Abstract
How do grammars assess the well-formedness of words with multiple phonotactic violations? Certain models predict that as the strength of phonotactic restrictions decrease, forms that violate multiple restrictions should be less acceptable than expected, in a pattern we term super-linear cumulativity. We test this prediction using a series of Artificial Grammar Learning experiments, in which we vary the number of exceptions to phonotactic patterns in artificial languages. We find that super-linear cumulativity is indeed observed in the conditions with the weakest restrictions. Strikingly, participants exhibit super-linear cumulativity even when the trained language does not contain evidence for it.
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