Infants rapidly learn word-referent mappings via cross-situational statistics.

By Linda B. Smith, Chen Yu. Cognition, 2008

932 citations recorded by the source corpus.

Subfields: Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/bcaaa81648c1a6b733a0d142a573a2a855ee1cc8

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In Developmental and Educational Psychology

Citations in the corpus

PSYCHOLOGICAL SCIENCE Research Article Using Speakers ’ Referential Intentions to Model Early Cross-Situational Word Learning (positive): “Recent work has provided strong evidence that both adults and children are able to learn cross-situational associations between words and objects even in the absence of individually unambiguous trials (Smith & Yu, 2008; Vouloumanos, 2008; Yu & Smith, 2007).”
Visually Grounded Learning of Keyword Prediction from Untranscribed Speech (2017, background): “Speciﬁcally, we are interested in the setting considered in [15,16], where natural images of scenes are paired with spoken descriptions, and neither the images nor speech are labelled.”
Word learning emerges from the interaction of online referent selection and slow associative learning (2012, background): “Adults (Yu & Smith, 2007) and infants (Smith & Yu, 2008) were exposed to small artificial lexica that contained such regularities across trials.”
Putting Education in “Educational” Apps (2015, background): “…the evidence that infants use statistical reasoning across a variety of domains is clear (Saffran, Aslin, & Newport, 1996; Saffran & Wilson, 2003; L. Smith & Yu, 2008; Xu & Garcia, 2008; Yu & Smith, 2007; Yurovsky, Yu, & Smith, 2012), other research has shown that when social cues are provided,…”
Child-Directed Speech Is Infrequent in a Forager-Farmer Population: A Time Allocation Study. (2019, neutral): “Some theorists believe that words can be learned in a “big data” fashion, simply calculating statistics between context (and objects in the context) and words heard (Smith & Yu, 2008)—a process that may require great amounts of data to get rid of spurious correlations.”
The Novel Object and Unusual Name (NOUN) Database: A collection of novel images for use in experimental research (2016, background): “…NOUN Database depict multi-part, multi-colored, real threedimensional (3D) objects as opposed to simple geometric shape configurations (e.g., L. B. Smith & Yu, 2008; Wu, Gopnik, Richardson, & Kirkham, 2011) or seemingly animate objects (e.g., Gauthier & Tarr, 1997; Mather, Schafer, &…”
Computational modeling of phonetic and lexical learning in early language acquisition: Existing models and future directions (2012, neutral): “, [89,90]).”
Statistical word learning at scale: the baby's view is better. (2013, neutral): “Understanding the nature of this uncertainty, and explaining how young learners nonetheless manage to learn object names, is a major theoretical problem in the study of early word learning (Markman, 1990; Tomasello & Barton, 1994; Smith & Yu, 2008).”
Social and Discourse Contributions to the Determination of Reference in Cross-Situational Word Learning (2013, background): “Since recent work suggests that children are able to use co-occurrence to link words to referents (Yu & Ballard, 2007; L. Smith & Yu, 2008; Vouloumanos, 2008; Vouloumanos & Werker, 2009), this kind of “filtering” account would be a way for learners to integrate crosssituational and social…”
Real-world visual statistics and infants' first-learned object names (2017, background): “Instead, young learners could keep track of the potential referents that co-occur with a word across different situations and use that aggregated data to statistically determine the likely referents of to-be-learned words [18].”
Discovering linguistic structures in speech: models and applications (2014, neutral): “Various factors such as cross-situational learning that may contribute to learning word meaning were also investigated in previous work [170, 47].”
Lookit (Part 1): A New Online Platform for Developmental Research (2017, result): “…to the left/right of the screen was less than 5% of trial length, also in line with lab estimates, although agreement is typically reported regarding whether the child was looking left, right, or away before summing those intervals (e.g., 91% by Smith & Yu, 2008; 98% by Yuan & Fisher, 2009).”