How do neural network models of quasiregular domains learnto represent knowledge that varies in its consistency withthe domain, and generalize this knowledge appropriately?Recent work focusing on spelling-to-sound correspondencesin English proposes that a graded “warping” mechanismdetermines the extent to which the pronunciation of a newlylearned word should generalize to its orthographic neighbors.We explored the micro-structure of this proposal by training anetwork to pronounce new made-up words that were consistentwith the dominant pronunciation (regulars), were comprisedof a completely unfamiliar pronunciation (exceptions), orwere consistent with a subordinate pronunciation in English(ambiguous). Crucially, by training the same spelling-to-soundmapping with either one or multiple items, we tested whethervariation in adjacent, within-item context made a givenpronunciation more able to generalize. This is exactly whatwe found. Context variability, therefore, appears to act as amodulator of the warping in quasiregular domains.