How should we evaluate models of segmentation in artificial language learning?

Alhama, Raquel G.; Scha, R.; Zudema, Willem

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How should we evaluate models of segmentation in artificial language learning?

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Alhama, R. G., Scha, R., & Zudema, W. (2015). How should we evaluate models of segmentation in artificial language learning? In N. A. Taatgen, M. K. van Vugt, J. P. Borst, & K. Mehlhorn (Eds.), Proceedings of ICCM 2015 (pp. 172-173). Groningen: University of Groningen.

Cite as: https://hdl.handle.net/21.11116/0000-0002-56DC-6

Abstract

One of the challenges that infants have to solve when learn- ing their native language is to identify the words in a con- tinuous speech stream. Some of the experiments in Artificial Grammar Learning (Saffran, Newport, and Aslin (1996); Saf- fran, Aslin, and Newport (1996); Aslin, Saffran, and Newport (1998) and many more) investigate this ability. In these ex- periments, subjects are exposed to an artificial speech stream that contains certain regularities. Adult participants are typ- ically tested with 2-alternative Forced Choice Tests (2AFC) in which they have to choose between a word and another sequence (typically a partword, a sequence resulting from misplacing boundaries).