Nelson Jonathan D, Cottrell Garrison W
Computational Neurobiology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Rd., La Jolla, CA 92037 1099, USA.
Neurocomputing (Amst). 2007 Aug 1;70(13-15):2256-2272. doi: 10.1016/j.neucom.2006.02.026. Epub 2007 Jan 2.
It has been unclear whether optimal experimental design accounts of data selection may offer insight into evidence acquisition tasks in which the learner's beliefs change greatly during the course of learning. Data from Rehder and Hoffman's eye movement version of Shepard, Horland and Jenkins' classic concept learning task provide an opportunity to address these issues. We introduce a principled probabilistic concept-learning model that describes the development of subjects' beliefs on that task. We use that learning model, together with a sampling function inspired by theory of optimal experimental design, to predict subjects' eye movements on the active learning version of that task. Results show that the same rational sampling function can predict eye movements early in learning, when uncertainty is high, as well as late in learning when the learner is certain of the true category.
目前尚不清楚，关于数据选择的最优实验设计理论是否能为证据获取任务提供见解，在这些任务中，学习者的信念在学习过程中会发生很大变化。来自雷德和霍夫曼对谢泼德、霍兰德和詹金斯经典概念学习任务的眼动实验版本的数据，为解决这些问题提供了契机。我们引入了一个有原则的概率概念学习模型，该模型描述了受试者在该任务中信念的发展。我们使用该学习模型，结合受最优实验设计理论启发的采样函数，来预测受试者在该任务的主动学习版本中的眼动。结果表明，相同的理性采样函数能够预测学习初期不确定性较高时的眼动，以及学习后期学习者确定真实类别时的眼动。
