A Bootstrapping Model of Frequency and Context Effects in Word Learning

Autor/inn/en	Kachergis, George; Yu, Chen; Shiffrin, Richard M.
Titel	A Bootstrapping Model of Frequency and Context Effects in Word Learning
Quelle	In: Cognitive Science, 41 (2017) 3, S.590-622 (33 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0364-0213
DOI	10.1111/cogs.12353
Schlagwörter	Vocabulary Development; Linguistic Theory; Context Effect; Familiarity; Nouns; Ambiguity (Context); Word Frequency; Learning Processes; Associative Learning; Models + Suchen Sie Ihr Suchwort? Wortschatzarbeit; Linguistische Theorie; Word analysis; Frequency; Wortanalyse; Häufigkeit; Learning process; Lernprozess; Analogiemodell
Abstract	Prior research has shown that people can learn many nouns (i.e., word--object mappings) from a short series of ambiguous situations containing multiple words and objects. For successful cross-situational learning, people must approximately track which words and referents co-occur most frequently. This study investigates the effects of allowing some word-referent pairs to appear more frequently than others, as is true in real-world learning environments. Surprisingly, high-frequency pairs are not always learned better, but can also boost learning of other pairs. Using a recent associative model (Kachergis, Yu, & Shiffrin, 2012), we explain how mixing pairs of different frequencies can bootstrap late learning of the low-frequency pairs based on early learning of higher frequency pairs. We also manipulate contextual diversity, the number of pairs a given pair appears with across training, since it is naturalistically confounded with frequency. The associative model has competing familiarity and uncertainty biases, and their interaction is able to capture the individual and combined effects of frequency and contextual diversity on human learning. Two other recent word-learning models do not account for the behavioral findings. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01