The present study provides empirical evidence for Heinz’s (2010) Subregular Hypothesis, which predicts that some gaps found in the typology of phonotactic patterns are due to learnability—more specifically, that only phonotactic patterns with specific computational properties are humanly learnable. The study compares the learnability of two long-distance harmony patterns that differ typologically (attested vs. unattested) and computationally (Strictly Piecewise vs. Locally Testable) using the artificial-language-learning paradigm. The results reveal a general bias toward learning the attested, Strictly Piecewise pattern, exactly as the Subregular Hypothesis predicts.