Modeling Human Performance on Statistical Word Segmentation Tasks

Michael C. Frank, Sharon Goldwater, Vikash Mansinghka, Thomas L. Griffiths, Joshua B. Tenenbaum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

What mechanisms support the ability of human infants, adults, and other primates to identify words from fluent speech using distributional regularities? In order to better characterize this ability, we collected data from adults in an artificial language segmentation task similar to Saffran, Newport, and Aslin (1996) in which the length of sentences was systematically varied between groups of participants. We then compared the fit of a variety of computational models--including simple statistical models of transitional probability and mutual information, a clustering model based on mutual information by Swingley (2005), PARSER (Perruchet & Vintner, 1998), and a Bayesian model. We found that while all models were able to successfully complete the task, fit to the human data varied considerably, with the Bayesian model achieving the highest correlation with our results
Original languageEnglish
Title of host publicationProceedings of the 29th Annual Conference of the Cognitive Science Society
Number of pages6
Publication statusPublished - 2007

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