The functional interpretation of language-related ERP components has been a central debate in psycholinguistics for decades. We advance an information-theoretic model of human language processing in the brain in which incoming linguistic input is processed at first shallowly and later with more depth, with these two kinds of information processing corresponding to distinct electroencephalographic signatures. Formally, we show that the information content (surprisal) of a word in context can be decomposed into two quantities: (A) shallow surprisal, which signals shallow processing difficulty for a word, and corresponds with the N400 signal; and (B) deep surprisal, which reflects the discrepancy between shallow and deep representations, and corresponds to the P600 signal and other late positivities. Both of these quantities can be estimated straightforwardly using modern NLP models. We validate our theory by successfully simulating ERP patterns elicited by a variety of linguistic manipulations in previously-reported experimental data from six experiments, with successful novel qualitative and quantitative predictions. Our theory is compatible with traditional cognitive theories assuming a `good-enough' shallow representation stage, but with a precise information-theoretic formulation. The model provides an information-theoretic model of ERP components grounded on cognitive processes, and brings us closer to a fully-specified neuro-computational model of language processing.

翻译：语言相关ERP成分的功能解释几十年来一直是心理语言学的核心争论。我们提出了一种基于信息论的人脑语言处理模型，其中输入的语料首先进行浅层处理，随后进行更深层次处理，这两种信息处理方式对应着不同的脑电图特征。形式上，我们证明语境中词汇的信息内容（惊奇度）可分解为两个量值：（A）浅层惊奇度，表征词汇的浅层处理难度，对应N400信号；（B）深层惊奇度，反映浅层与深层表征间的差异，对应P600信号及其他晚期正波。这两个量值均可通过现代NLP模型直接估算。我们通过成功模拟六项实验报告中各类语言操作引发的ERP模式验证了该理论，并实现了新颖的定性与定量预测。本理论与假定“足够好”浅层表征阶段的传统认知理论兼容，但采用了精确的信息论表述。该模型建立了基于认知过程的ERP成分信息论模型，使我们更接近构建完整规范的语言处理神经计算模型。