How should we evaluate models of segmentation in artificial language learning?
Alhama, R. G., Scha, R., & Zudema, W.
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.
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).