On the discovery of novel wordlike units from utterances: An artificial-language study with implications for native-language acquisition

In 4 experiments, adults were familiarized with utterances from an artificial language. Shea utterances occurred both in isolation and as part of a longer utterance, either at the edge or in the middle of the longer utterance. After familiarization, participants' recognition memory for fragments of the long utterance was tested. Recognition was greatest for the remainder of the longer utterance after extraction of the short utterance, but only when the short utterance was located at the edge of the long utterance. These results support the incremental distributional regularity optimization (INCDROP) model of speech segmentation and word discovery, which asserts that people segment utterances into familiar and new wordlike units in such a way as to minimize the burden of processing new units. INCDROP suggests that segmentation and word discovery during native-language acquisition may be driven by recognition of familiar units from the start, with no need for transient bootstrapping mechanisms.