Modeling cross-situational word-referent learning: prior questions.
Modeling cross-situational word-referent learning: prior questions.
复制标题
DOI：
10.1037/a0026182
复制
发表时间：
2012-01
影响因子：
5.4
通讯作者：
Smith, Linda B.
中科院分区：
文献类型：
--
作者：
Yu, Chen;Smith, Linda B.
成人和幼儿都拥有强大的统计计算能力——他们可以通过聚合跨上下文的跨情境统计信息，从涉及许多单词和许多所指对象的高度模糊上下文中推断出单词的所指对象。这种能力已经通过假设检验模型和联想学习模型得到了解释。本文描述了一系列模拟研究和分析，旨在了解两类模型提出的不同学习机制及其相互关系。在不同的信息选择、计算和决策规范下检查了假设检验模型的变体和简单或愚蠢的关联机制。至关重要的是，模型的这三个组成部分以复杂的方式相互作用。这些模型说明了数据输入量和强大计算之间的基本权衡：通过选择更多信息，哑关联模型可以模仿通过使用更少数据的假设检验模型完成的强大学习。然而，由于模型组成部分之间的相互作用，关联模型可以模仿各种假设检验模型，通过不同的内部组件产生相同的学习模式。这些模拟论证了组合方法对人类统计学习的重要性：对有助于人类学习者统计学习的过程进行实验分解，并具有可以独立和共同评估的内部组件的模型。
Both adults and young children possess powerful statistical computation capabilities—they can infer the referent of a word from highly ambiguous contexts involving many words and many referents by aggregating cross-situational statistical information across contexts. This ability has been explained by models of hypothesis testing and by models of associative learning. This article describes a series of simulation studies and analyses designed to understand the different learning mechanisms posited by the 2 classes of models and their relation to each other. Variants of a hypothesis-testing model and a simple or dumb associative mechanism were examined under different specifications of information selection, computation, and decision. Critically, these 3 components of the models interact in complex ways. The models illustrate a fundamental tradeoff between amount of data input and powerful computations: With the selection of more information, dumb associative models can mimic the powerful learning that is accomplished by hypothesis-testing models with fewer data. However, because of the interactions among the component parts of the models, the associative model can mimic various hypothesis-testing models, producing the same learning patterns but through different internal components. The simulations argue for the importance of a compositional approach to human statistical learning: the experimental decomposition of the processes that contribute to statistical learning in human learners and models with the internal components that can be evaluated independently and together.
登录
查看更多内容
影响因子：
4
作者：
Diesendruck, G;Markson, L
通讯作者：
Markson, L
DOI：
10.1037/0278-7393.22.2.458
发表时间：
1996-03-01
影响因子：
2.6
作者：
Billman, D;Knutson, J
通讯作者：
Knutson, J
影响因子：
1.8
作者：
Colunga, Eliana;Smith, Linda B.;Gasser, Michael
通讯作者：
Gasser, Michael
影响因子：
7.8
作者：
Fontanari, Jose F.;Tikhanoff, Vadim;Perlovsky, Leonid I.
通讯作者：
Perlovsky, Leonid I.
影响因子：
5.4
作者：
ANDERSON, JR
通讯作者：
ANDERSON, JR