Abstract This paper presents ENHAnCE, an algorithm that simultaneously learns a predictive model of the input stream and generates representations of the concepts being observed. Following cognitivelyinspired models of event segmentation, ENHAnCE uses expectation violations to identify boundaries between temporally extended patterns. It applies its expectation-driven process at multiple levels of temporal granularity to produce a hierarchy of predictive models that enable it to identify concepts at multiple levels of temporal abstraction. Evaluations show that the temporal abstraction hierarchies generated by ENHAnCE closely match handcoded hierarchies for the test data streams. Given language data streams, ENHAnCE learns a hierarchy of predictive models that capture basic units of both spoken and written language: morphemes, lexemes, phonemes, syllables, and words.