EARLY WORD LEARNING--COMPUTATIONAL AND BEHAVIORAL TESTS

描述（申请人的摘要）：孩子们是惊人的单词学习者。 尽管每个新词的可能含义数不胜数， 孩子们学习单词的速度很快，而且似乎不费什么力气。 上一页 研究表明，孩子们有偏见，只考虑其中的一些 新词的可能含义。 然而，单词学习的起源 偏见及其即时运作的机制 尚未确定。 拟议的项目解决了我们的这一差距 对单词学习的理解。 我将在三个项目中测试 假设单词学习偏差是由统计规律产生的 儿童学习的语言和类别。 具体问题 解决的问题是：（1）语言的统计规律是什么 儿童的投入； (2) 能够简单的学习统计规律 复制特定单词学习偏差的发展； (3) 如果 儿童所知道的单词中发现的统计规律发生了改变， 特定单词学习偏差的发展轨迹有何变化？ 这些问题在神经网络模拟中得到解决 特定单词学习偏差的发展；实验研究其中 单词学习偏差的自然发展会因教学而改变 儿童的话；以及对单词学习中出现的变化进行建模 来自词汇训练。

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.