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• W2894512214 abstract "Cross-situational word learning, wherein a learner combines information about possible meanings of a word across multiple exposures, has previously been shown to be a very powerful strategy to acquire a large lexicon in a short time. However, this success may derive from idealizations that are made when modeling the word-learning process. In particular, an earlier model assumed that a learner could perfectly recall all previous instances of a word's use and the inferences that were drawn about its meaning. In this work, we relax this assumption and determine the performance of a model cross-situational learner who forgets word-meaning associations over time. Our main finding is that it is possible for this learner to acquire a human-scale lexicon by adulthood with word-exposure and memory-decay rates that are consistent with empirical research on childhood word learning, as long as the degree of referential uncertainty is not too high or the learner employs a mutual exclusivity constraint. Our findings therefore suggest that successful word learning does not necessarily demand either highly accurate long-term tracking of word and meaning statistics or hypothesis-testing strategies." @default.
• W2894512214 created "2018-10-05" @default.
• W2894512214 creator A5001130167 @default.
• W2894512214 creator A5070656514 @default.
• W2894512214 date "2018-09-28" @default.
• W2894512214 modified "2023-09-27" @default.
• W2894512214 title "Cross-situational learning of large lexicons with finite memory" @default.
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• W2894512214 hasPublicationYear "2018" @default.
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