We examine how well categorical and probabilistic phonotactic learning models extract grammars which predict Polish speakers' acceptability judgments of words with varied initial consonant clusters. Polish is an especially interesting language to look at because of its rich inventory of sonority-sequencing defying consonant clusters, often as a result of yer-deletion. In line with results by Gorman (2013) and Durvasula (2020), we find that the categorical baselines considered here generally outperformed the Hayes and Wilson's (2008) maximum-entropy based phonotactic learner. We conclude that gradient acceptability judgments do not provide unambiguous evidence for gradient, probabilistic grammars.

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