EARLY WORD LEARNING--COMPUTATIONAL AND BEHAVIORAL TESTS

描述（申请人的摘要）：儿童是惊人的单词学习者。 尽管每个新单词的可能含义数量是巨大的， 孩子们学单词很快，而且似乎不费什么力气。 先前 研究表明，孩子们有偏见，只考虑一些 一个新词的可能含义。 然而，单词学习的起源 偏见和机制，他们运作的时刻到时刻的基础上， 尚未确定。 拟议的项目解决了这一差距，在我们的 理解单词学习。 在三个项目中，我将测试 词汇学习偏差是从统计学角度发展出来的假说 孩子们学习的语言和类别。 的具体问题 讨论的是：（1）什么是语言中的统计量 儿童的输入;（2）一个简单的统计学习者是否能够 复制特定单词学习偏见的发展;（3）如果 在孩子们知道被改变的单词中发现的统计数据， 特定词汇学习偏差的发展轨迹变化？ 这些问题在神经网络模拟中得到了解决 特定单词学习偏差的发展;实验研究， 单词学习偏差的自然发展会被教学改变 儿童词汇;以及在词汇学习中出现的变化建模 从词汇训练中。

DESCRIPTION (Applicant's Abstract): Children are amazing word learners. Even though the number of possible meanings for each novel word is immense, children learn words quickly and with seemingly little effort. Previous research has suggested that children are biased to only consider some of the possible meanings for a new word. However, the origin of word learning biases and the mechanisms by which they operate on a moment-to-moment basis has yet to be determined. The proposed project addresses this gap in our understanding of word learning. In three projects, I will test the hypothesis that word learning biases develop out of statistical regularities in the language and categories children learn. The specific questions addressed are: (1) what are the statistical regularities in the language input of children; (2) can a simple learner of statistical regularities replicate the development of specific word-learning biases; and (3) if the statistical regularities found in the words children know are altered, does the developmental trajectory of specific word learning biases by change? These questions are addressed in neural network simulations of the development of specific word learning biases; experimental studies in which the natural development of a word learning bias is altered by teaching children words; and in modeling of the changes in word learning that arise from the lexical training.