I propose a learning-based account of phonological-tier-like representations. I argue that humans show a proclivity for tracking adjacent dependencies, and propose a learning algorithm that incorporates this by tracking only adjacent dependencies. The model changes representations in response to being unable to predict the surface form of alternating segments—a decision governed by the Tolerance Principle, which allows for learning despite the sparsity and exceptions inevitable in naturalistic data. Tier-like representations emerge from the algorithm, which achieves high-accuracy learning over natural language data, handles crosslinguistic complexities like neutral segments and blockers, and makes correct experimental predictions about human behavior.