论文标题
积极学习学习因果关系
Actively learning to learn causal relationships
论文作者
论文摘要
人们如何积极学习学习？也就是说，人们如何以及何时选择促进长期学习和选择更有益的行动的行动？我们在积极的因果学习领域中探索这些问题。我们提出了一个分层的贝叶斯模型，它通过预测人们不仅追求有关因果关系的信息，而且还涉及因果过度的信息，而且还涉及有关因果关系的抽象信念$ \ unicode {x2014} $摘要的因果关系，这些信念跨越了多种情况，并限制了我们在每种情况下如何学习具体情况。在两个主题间操作的两个主动“泡沫检测器”实验中，我们的模型得到了参与者行为的定性趋势和基于个体差异的模型比较的支持。我们的结果表明，当在积极的因果学习问题之间存在抽象相似之处时，人们很容易就这些相似之处学习和转移疏忽大意。此外，人们利用这些夸张的人来促进长期积极学习。
How do people actively learn to learn? That is, how and when do people choose actions that facilitate long-term learning and choosing future actions that are more informative? We explore these questions in the domain of active causal learning. We propose a hierarchical Bayesian model that goes beyond past models by predicting that people pursue information not only about the causal relationship at hand but also about causal overhypotheses$\unicode{x2014}$abstract beliefs about causal relationships that span multiple situations and constrain how we learn the specifics in each situation. In two active "blicket detector" experiments with 14 between-subjects manipulations, our model was supported by both qualitative trends in participant behavior and an individual-differences-based model comparison. Our results suggest when there are abstract similarities across active causal learning problems, people readily learn and transfer overhypotheses about these similarities. Moreover, people exploit these overhypotheses to facilitate long-term active learning.
