Sequential Expectations: The Role of Prediction-Based Learning in Language
Sequential Expectations: The Role of Prediction-Based Learning in Language
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DOI：
10.1111/j.1756-8765.2009.01072.x
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发表时间：
2010-01-01
影响因子：
3
通讯作者：
Tomblin, J. Bruce
中科院分区：
文献类型：
--
作者：
Misyak, Jennifer B.;Christiansen, Morten H.;Tomblin, J. Bruce
基于预测的过程似乎在语言中发挥着重要作用。然而，很少有研究试图测试预测学习和自然语言处理之间的关系。本文建立在现有的统计学习工作，使用一种新的范式研究预测依赖关系的在线学习。在这种范式中，引入了一个新的“预测任务”，它提供了一个敏感的个体差异指数，用于开发概率序列期望。在三个相互关联的实验中，预测任务及其结果被用来桥接非邻接处理背景下统计学习和语言之间的经验关系的知识。我们首先绘制了学习非邻接的轨迹，记录了预测学习的个体差异。随后的简单循环网络模拟，然后密切捕捉人类的表现模式，在新的范式。最后，预测表现的个体差异与参与者对自然语言中复杂的长距离依赖关系的句子处理密切相关。
Prediction-based processes appear to play an important role in language. Few studies, however, have sought to test the relationship within individuals between prediction learning and natural language processing. This paper builds upon existing statistical learning work using a novel paradigm for studying the on-line learning of predictive dependencies. Within this paradigm, a new "prediction task'' is introduced that provides a sensitive index of individual differences for developing probabilistic sequential expectations. Across three interrelated experiments, the prediction task and results thereof are used to bridge knowledge of the empirical relation between statistical learning and language within the context of nonadjacency processing. We first chart the trajectory for learning nonadjacencies, documenting individual differences in prediction learning. Subsequent simple recurrent network simulations then closely capture human performance patterns in the new paradigm. Finally, individual differences in prediction performances are shown to strongly correlate with participants' sentence processing of complex, long-distance dependencies in natural language.