A large body of work in psycholinguistics has focused on the idea that online language comprehension can be shallow or `good enough': given constraints on time or available computation, comprehenders may form interpretations of their input that are plausible but inaccurate. However, this idea has not yet been linked with formal theories of computation under resource constraints. Here we use information theory to formulate a model of language comprehension as an optimal trade-off between accuracy and processing depth, formalized as bits of information extracted from the input, which increases with processing time. The model provides a measure of processing effort as the change in processing depth, which we link to EEG signals and reading times. We validate our theory against a large-scale dataset of garden path sentence reading times, and EEG experiments featuring N400, P600 and biphasic ERP effects. By quantifying the timecourse of language processing as it proceeds from shallow to deep, our model provides a unified framework to explain behavioral and neural signatures of language comprehension.

翻译：心理语言学的大量研究聚焦于一个观点：在线语言理解可以是浅层的或“足够好”的——在时间或可用计算资源的约束下，理解者可能形成对输入信息看似合理但不准确的解释。然而，这一观点尚未与资源约束下的计算形式化理论建立联系。本文利用信息论将语言理解建模为准确性与处理深度之间的最优权衡，其中处理深度被形式化为从输入中提取的信息比特数，且随处理时间增加。该模型将处理努力量化为处理深度的变化，并将其与脑电图信号和阅读时间相关联。我们通过大规模花园路径句阅读时间数据集，以及包含N400、P600和双相事件相关电位效应的脑电图实验验证了该理论。通过量化语言处理从浅层到深层的时间进程，我们的模型为解释语言理解的行为与神经特征提供了统一框架。