Teaching Games : Statistical Sampling Assumptions for Learning in Pedagogical Situations
Teaching Games : Statistical Sampling Assumptions for Learning in Pedagogical Situations
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教学游戏：教学情境中学习的统计抽样假设
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Noah D. Goodman
中科院分区：
文献类型：
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作者：
Patrick Shafto;Noah D. Goodman
许多学习和推理发生在教学情境中——教师选择例子的目的是让学习者推断出教师心目中的概念。在本文中，我们提出了一个教学环境下的教与学模型，该模型预测了教师应该选择什么例子，以及给定教师的例子，学习者应该推断什么。我们提出了两个实验使用的实验范式称为矩形游戏。第一个实验将人们的推断与定性模型预测进行比较。第二个实验测试人们在教学抽样不合适的情况下，排除其他解释，并建议人们使用情境适当的抽样假设。最后，我们讨论了归纳推理和认知发展中更广泛工作的联系，并概述了未来工作的领域。人类的许多学习和推理都是在教学环境中进行的。在学校里，老师通过例子和问题向学生传授数学、科学和文学知识。从孩子很小的时候起，父母就通过微妙的目光和直接的警告，教会孩子物体和行为的词语，以及文化和个人偏好。教学设置——一个智能体选择信息传递给另一个智能体以传达概念的设置——主宰着人类的学习和推理。如果学习者对教师如何采样信息的假设反映了这种有目的的采样，那么学习者可能能够在教学情境中做出更强的推断。抽样假设是学习者为了更好地解释统计学习的证据而对数据来源做出的假设。最近的研究表明，即使是婴儿对观察数据的抽样过程也很敏感（Xu & Tenenbaum, 2007），而当老师对数据进行抽样时，幼儿会做出定性不同的推断（Gergely, Egyed, & Kiraly, 2007）。人类学习的计算模型并没有关注产生观察数据的抽样过程，当它们这样做时，假设的过程相对简单。例如，Fried和Holyoak（1984）通过假设样本是均匀随机生成的（弱抽样）来建模类别学习，Tenenbaum（1999）通过假设样本是从真实概念随机生成的（强抽样）来建模正面数据的学习。O O O O O O O O O x
Much of learning and reasoning occurs in pedagogical situations – situations in which teachers choose examples with the goal of having a learner infer the concept the teacher has in mind. In this paper, we present a model of teaching and learning in pedagogical settings which predicts what examples teachers should choose and what learners should infer given a teachers’ examples. We present two experiments using an experimental paradigm called the rectangle game. The first experiment compares people’s inferences to qualitative model predictions. The second experiment tests people in a situation where pedagogical sampling is not appropriate, ruling out alternative explanations, and suggesting that people use contextappropriate sampling assumptions. We conclude by discussing connections to broader work in inductive reasoning and cognitive development, and outline areas of future work. Much of human learning and reasoning goes on in pedagogical settings. In schools, teachers impart their knowledge to students about mathematics, science, and literature through examples and problems. From early in life, parents teach children words for objects and actions, and cultural and personal preferences through subtle glances and outright admonitions. Pedagogical settings – settings where one agent is choosing information to transmit to another agent for the purpose of communicating a concept – dominate human learning and reasoning. If learners’ assumptions about how teachers sample information reflected this purposeful sampling, then learners might be able to make much stronger inferences in pedagogical situations. Sampling assumptions are assumptions that a learner makes about the source of data, in order to better interpret the evidence for statistical learning. Recent research suggests that even infants are sensitive to the sampling processes that underlie observed data (Xu & Tenenbaum, 2007) and young children make qualitatively different inferences when data are sampled by a teacher (Gergely, Egyed, & Kiraly, 2007). Computational models of human learning have not focused on the sampling processes that generate observed data, and when they do the assumed processes are relatively simple. For example, Fried and Holyoak (1984) modeled category learning by assuming examples are generated uniformly at random (weak sampling) and Tenenbaum (1999) modeled learning from positive data by assuming examples are generated at random from the true concept (strong sampling). O O O O O O X
DOI：
10.1037//0278-7393.10.2.234
发表时间：
1984
期刊：
Journal of experimental psychology. Learning, memory, and cognition
影响因子：
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作者：
Fried,LS;Holyoak,KJ
通讯作者：
Holyoak,KJ