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  1.  19
    All Words Are Not Created Equal: Expectations About Word Length Guide Infant Statistical Learning.Jenny R. Saffran & Casey Lew-Williams - 2012 - Cognition 122 (2):241-246.
    Infants have been described as 'statistical learners' capable of extracting structure (such as words) from patterned input (such as language). Here, we investigated whether prior knowledge influences how infants track transitional probabilities in word segmentation tasks. Are infants biased by prior experience when engaging in sequential statistical learning? In a laboratory simulation of learning across time, we exposed 9- and 10-month-old infants to a list of either disyllabic or trisyllabic nonsense words, followed by a pause-free speech stream composed of a (...)
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  2.  9
    Individual Differences in Nonverbal Prediction and Vocabulary Size in Infancy.Tracy Reuter, Lauren Emberson, Alexa Romberg & Casey Lew-Williams - 2018 - Cognition 176:215-219.
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  3. Neural Synchrony Predicts Children's Learning of Novel Words.Elise A. Piazza, Ariella Cohen, Juliana Trach & Casey Lew-Williams - 2021 - Cognition 214:104752.
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  4.  1
    Improving the Generalizability of Infant Psychological Research: The ManyBabies Model.Ingmar Visser, Christina Bergmann, Krista Byers-Heinlein, Rodrigo Dal Ben, Wlodzislaw Duch, Samuel Forbes, Laura Franchin, Michael C. Frank, Alessandra Geraci, J. Kiley Hamlin, Zsuzsa Kaldy, Louisa Kulke, Catherine Laverty, Casey Lew-Williams, Victoria Mateu, Julien Mayor, David Moreau, Iris Nomikou, Tobias Schuwerk, Elizabeth A. Simpson, Leher Singh, Melanie Soderstrom, Jessica Sullivan, Marion I. van den Heuvel, Gert Westermann, Yuki Yamada, Lorijn Zaadnoordijk & Martin Zettersten - 2022 - Behavioral and Brain Sciences 45.
    Yarkoni's analysis clearly articulates a number of concerns limiting the generalizability and explanatory power of psychological findings, many of which are compounded in infancy research. ManyBabies addresses these concerns via a radically collaborative, large-scale and open approach to research that is grounded in theory-building, committed to diversification, and focused on understanding sources of variation.
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