有限记忆条件下大词汇的跨情境学习
Cross-situational learning of large lexicons with finite memory
摘要：
跨情境单词学习，其中学习者在多次接触中结合关于单词的可能含义的信息，先前已被证明是在短时间内获得大量词汇的非常强大的策略。然而，这种成功可能来自于对单词学习过程建模时的理想化。特别是，早期的模型假设学习者可以完美地回忆起一个词的使用和推断，得出有关其含义的所有先前的情况。在这项工作中，我们放松了这一假设，并确定了一个模型的跨情境的学习者谁忘记随着时间的推移词义协会的表现。我们的主要发现是，它是可能的，这个学习者获得一个人类规模的词汇与词的曝光和记忆衰退率与儿童词汇学习的实证研究是一致的成年，只要参考的不确定性的程度不是太高，或学习者采用互斥约束。因此，我们的研究结果表明，成功的单词学习并不一定需要高度准确的长期跟踪单词和含义的统计数据或假设测试策略。
英文摘要：
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.
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