Department of Linguistics, Stanford University, Stanford, CA 94305, United States; Collaborative Research Center 1102, Saarland University, Saarbrücken, 66123, Germany.
School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh, EH8 9AB, United Kingdom.
Cognition. 2023 Jan;230:105289. doi: 10.1016/j.cognition.2022.105289. Epub 2022 Oct 5.
Research on human reading has long documented that reading behavior shows task-specific effects, but it has been challenging to build general models predicting what reading behavior humans will show in a given task. We introduce NEAT, a computational model of the allocation of attention in human reading, based on the hypothesis that human reading optimizes a tradeoff between economy of attention and success at a task. Our model is implemented using contemporary neural network modeling techniques, and makes explicit and testable predictions about how the allocation of attention varies across different tasks. We test this in an eyetracking study comparing two versions of a reading comprehension task, finding that our model successfully accounts for reading behavior across the tasks. Our work thus provides evidence that task effects can be modeled as optimal adaptation to task demands.
人类阅读研究长期以来已经证明，阅读行为表现出特定任务的效果，但构建能够预测人类在给定任务中表现出何种阅读行为的通用模型一直具有挑战性。我们引入了 NEAT，这是一种人类阅读中注意力分配的计算模型，基于这样一种假设，即人类阅读在注意力经济性和任务成功之间进行权衡优化。我们的模型使用当代神经网络建模技术实现，并对注意力分配如何在不同任务中变化做出明确和可测试的预测。我们在一项眼动研究中对两个阅读理解任务版本进行了测试，发现我们的模型成功地解释了两个任务中的阅读行为。因此，我们的工作提供了证据，表明任务效应可以建模为对任务需求的最佳适应。
