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Willems, R. M., & Cristia, A. (2018). Hemodynamic methods: fMRI and fNIRS. In A. M. B. De Groot, & P. Hagoort (
Eds. ), Research methods in psycholinguistics and the neurobiology of language: A practical guide (pp. 266-287). Hoboken: Wiley. -
Willems, R. M., & Van Gerven, M. (2018). New fMRI methods for the study of language. In S.-A. Rueschemeyer, & M. G. Gaskell (
Eds. ), The Oxford Handbook of Psycholinguistics (2nd ed., pp. 975-991). Oxford: Oxford University Press. -
Wittek, A. (1998). Learning verb meaning via adverbial modification: Change-of-state verbs in German and the adverb "wieder" again. In A. Greenhill, M. Hughes, H. Littlefield, & H. Walsh (
Eds. ), Proceedings of the 22nd Annual Boston University Conference on Language Development (pp. 779-790). Somerville, MA: Cascadilla Press. -
Witteman, J., Karaseva, E., Schiller, N. O., & McQueen, J. M. (2023). What does successful L2 vowel acquisition depend on? A conceptual replication. In R. Skarnitzl, & J. Volín (
Eds. ), Proceedings of the 20th International Congress of the Phonetic Sciences (ICPhS 2023) (pp. 928-931). Prague: Guarant International.Abstract
It has been suggested that individual variation in vowel compactness of the native language (L1) and the distance between L1 vowels and vowels in the second language (L2) predict successful L2 vowel acquisition. Moreover, general articulatory skills have been proposed to account for variation in vowel compactness. In the present work, we conceptually replicate a previous study to test these hypotheses with a large sample size, a new language pair and a
new vowel pair. We find evidence that individual variation in L1 vowel compactness has opposing effects for two different vowels. We do not find evidence that individual variation in L1 compactness
is explained by general articulatory skills. We conclude that the results found previously might be specific to sub-groups of L2 learners and/or specific sub-sets of vowel pairs. -
Zavala, R. (2000). Multiple classifier systems in Akatek (Mayan). In G. Senft (
Ed. ), Systems of nominal classification (pp. 114-146). Cambridge University Press. -
Zhang, Y., Yurovsky, D., & Yu, C. (2015). Statistical word learning is a continuous process: Evidence from the human simulation paradigm. In D. Noelle, R. Dale, A. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings, & P. P. Maglio (
Eds. ), Proceedings of the 37th Annual Meeting of the Cognitive Science Society (CogSci 2015) (pp. 2422-2427). Austin: Cognitive Science Society.Abstract
In the word-learning domain, both adults and young children are able to find the correct referent of a word from highly ambiguous contexts that involve many words and objects by computing distributional statistics across the co-occurrences of words and referents at multiple naming moments (Yu & Smith, 2007; Smith & Yu, 2008). However, there is still debate regarding how learners accumulate distributional information to learn object labels in natural learning environments, and what underlying learning mechanism learners are most likely to adopt. Using the Human Simulation Paradigm (Gillette, Gleitman, Gleitman & Lederer, 1999), we found that participants’ learning performance gradually improved and that their ability to remember and carry over partial knowledge from past learning instances facilitated subsequent learning. These results support the statistical learning model that word learning is a continuous process.
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