Yeari Menahem, van den Broek Paul
Bar Ilan University, Ramat-Gan, Israel.
Leiden University, Leiden, Netherlands.
Behav Res Methods. 2016 Sep;48(3):880-96. doi: 10.3758/s13428-016-0749-6.
It is a well-accepted view that the prior semantic (general) knowledge that readers possess plays a central role in reading comprehension. Nevertheless, computational models of reading comprehension have not integrated the simulation of semantic knowledge and online comprehension processes under a unified mathematical algorithm. The present article introduces a computational model that integrates the landscape model of comprehension processes with latent semantic analysis representation of semantic knowledge. In three sets of simulations of previous behavioral findings, the integrated model successfully simulated the activation and attenuation of predictive and bridging inferences during reading, as well as centrality estimations and recall of textual information after reading. Analyses of the computational results revealed new theoretical insights regarding the underlying mechanisms of the various comprehension phenomena.
读者所拥有的先前语义（一般）知识在阅读理解中起着核心作用，这是一个被广泛接受的观点。然而，阅读理解的计算模型尚未在统一的数学算法下整合语义知识模拟和在线理解过程。本文介绍了一种计算模型，该模型将理解过程的景观模型与语义知识的潜在语义分析表示相结合。在对先前行为研究结果的三组模拟中，该整合模型成功模拟了阅读过程中预测性推理和搭桥推理的激活与衰减，以及阅读后文本信息的中心性估计和回忆。对计算结果的分析揭示了关于各种理解现象潜在机制的新理论见解。
