What You Learn Is What You See: Using Eye Movements to Study Infant Cross-Situational Word Learning

Autor/inn/en	Yu, Chen; Smith, Linda B.
Titel	What You Learn Is What You See: Using Eye Movements to Study Infant Cross-Situational Word Learning
Quelle	In: Developmental Science, 14 (2011) 2, S.165-180 (16 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1363-755X
DOI	10.1111/j.1467-7687.2010.00958.x
Schlagwörter	Eye Movements; Attention; Associative Learning; Human Body; Infants; Language Acquisition; Word Recognition; Visual Stimuli; Learning Processes; Inferences; Child Development + Suchen Sie Ihr Suchwort? Augenbewegung; Aufmerksamkeit; Menschlicher Körper; Infant; Toddler; Toddlers; Kleinkind; Sprachaneignung; Spracherwerb; Worterkennung; Learning process; Lernprozess; Inference; Inferenz; Kindesentwicklung
Abstract	Recent studies show that both adults and young children possess powerful statistical learning capabilities to solve the word-to-world mapping problem. However, the underlying mechanisms that make statistical learning possible and powerful are not yet known. With the goal of providing new insights into this issue, the research reported in this paper used an eye tracker to record the moment-by-moment eye movement data of 14-month-old babies in statistical learning tasks. Various measures are applied to such fine-grained temporal data, such as looking duration and shift rate (the number of shifts in gaze from one visual object to the other) trial by trial, showing different eye movement patterns between strong and weak statistical learners. Moreover, an information-theoretic measure is developed and applied to gaze data to quantify the degree of learning uncertainty trial by trial. Next, a simple associative statistical learning model is applied to eye movement data and these simulation results are compared with empirical results from young children, showing strong correlations between these two. This suggests that an associative learning mechanism with selective attention can provide a cognitively plausible model of cross-situational statistical learning. The work represents the first steps in using eye movement data to infer underlying real-time processes in statistical word learning. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10