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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Not all words are equally acquired: transitional probabilities and instructions affect the electrophysiological correlates of statistical learning Soares, Ana Paula; Gutiérrez-Domínguez, Francisco-Javier; Vasconcelos, Margarida Fátima Gomes. - : Frontiers Media, 2020 BASE Show details 2 Segmentability Differences Between Child-Directed and Adult-Directed Speech: A Systematic Test With an Ecologically Valid Corpus Cristia, Alejandrina; Dupoux, Emmanuel; Bernstein Ratner, Nan... In: EISSN: 2470-2986 ; Open Mind ; https://hal.archives-ouvertes.fr/hal-02274050 ; Open Mind, MIT Press, 2019, 3, pp.13-22. ⟨10.1162/opmi_a_00022⟩ (2019) BASE Show details 3 The neural correlates of statistical learning in a word segmentation task: An fMRI study Karuza, Elisabeth A.; Newport, Elissa L.; Aslin, Richard N.... In: ISSN: 0093-934X ; Brain and Language, Vol. 127, No 1 (2013) pp. 46-54 (2013) BASE Show details 4 Verbs are LookING Good in Language Acquisition Jon A. Willits; Mark S. Seidenberg; Jenny R. Saffran In: http://141.14.165.6/CogSci09/papers/584/paper584.pdf BASE Show details 5 Modeling Human Performance in Statistical Word Segmentation Michael C. Frank; Sharon Goldwater; Vikash Mansinghka; Tom Griffiths; Joshua Tenenbaum In: http://cocosci.berkeley.edu/tom/papers/wordseg2.pdf 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. Keyword: Bayesian; language acquisition; Statistical learning; word segmentation URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.6889 http://cocosci.berkeley.edu/tom/papers/wordseg2.pdf BASE Hide details 6 Modeling Human Performance in Statistical Word Segmentation Michael C. Frank; Sharon Goldwater; Vikash Mansinghka... In: http://www.stanford.edu/~sgwater/papers/cogsci07_final.pdf BASE Show details

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