Modeling Statistical Insensitivity: Sources of Suboptimal Behavior
Modeling Statistical Insensitivity: Sources of Suboptimal Behavior
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DOI：
10.1111/cogs.12373
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发表时间：
2017-01-01
影响因子：
2.5
通讯作者：
Lidz, Jeffrey
中科院分区：
文献类型：
--
作者：
Gagliardi, Annie;Feldman, Naomi H.;Lidz, Jeffrey
获得名词类（语法性别）语言的儿童提供了充分的统计信息，可以表征名词在这些类中的分布，但是他们使用此信息来对新名词进行分类与最佳贝叶斯分类器的预测有所不同。我们使用合理的分析来研究儿童对不符合其输入中统计特征的表面分布的分布进行最佳分类的假设。我们提出了三种方式可能会出现儿童明显的统计不敏感性，并发现这三种方法提供了解决儿童行为与最佳分类器之间差异的方法。第四个模型结合了其中两个建议，发现儿童的不敏感性最好是在分类过程中忽略某些特征的偏见，而不是无法在学习过程中编码这些功能。这些结果提供了对儿童对名词类别的知识的了解，并突出了从输入中的统计信息与儿童学习过程相互作用的复杂方式。
Children acquiring languages with noun classes (grammatical gender) have ample statistical information available that characterizes the distribution of nouns into these classes, but their use of this information to classify novel nouns differs from the predictions made by an optimal Bayesian classifier. We use rational analysis to investigate the hypothesis that children are classifying nouns optimally with respect to a distribution that does not match the surface distribution of statistical features in their input. We propose three ways in which children's apparent statistical insensitivity might arise, and find that all three provide ways to account for the difference between children's behavior and the optimal classifier. A fourth model combines two of these proposals and finds that children's insensitivity is best modeled as a bias to ignore certain features during classification, rather than an inability to encode those features during learning. These results provide insight into children's developing knowledge of noun classes and highlight the complex ways in which statistical information from the input interacts with children's learning processes.