Information‐theoretic complexity metrics, such as Surprisal (Hale, 2001; Levy, 2008) and Entropy Reduction (Hale, 2003), are linking hypotheses that bridge theorized expectations about sentences and observed processing difficulty in comprehension. These expectations can be viewed as syntactic derivations constrained by a grammar. However, this expectation‐based view is not limited to syntactic information alone. The present study combines structural and non‐structural information in unified models of word‐by‐word sentence processing difficulty. Using probabilistic minimalist grammars (Stabler, 1997), we extend expectation‐based models to include frequency information about noun phrase animacy. Entropy reductions derived from these grammars faithfully reflect the asymmetry between subject and object relatives (Staub, 2010; Staub, Dillon, & Clifton, 2017), as well as the effect of animacy on the measured difficulty profile (Lowder & Gordon, 2012; Traxler, Morris, & Seely, 2002). Visualizing probability distributions on the remaining alternatives at particular parser states allows us to explore new, linguistically plausible interpretations for the observed processing asymmetries, including the way that expectations about the relativized argument influence the processing of particular types of relative clauses (Wagers & Pendleton, 2016).

中文翻译：

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在相对从句处理中量化结构性和非结构性期望

信息理论的复杂性度量，例如 Surprisal (Hale, 2001; Levy, 2008) 和 Entropy Reduction (Hale, 2003)，正在将假设联系起来，这些假设将关于句子的理论期望与观察到的理解处理难度联系起来。这些期望可以被视为受语法约束的句法推导。然而，这种基于期望的观点不仅限于句法信息。本研究在逐字句处理难度的统一模型中结合了结构和非结构信息。使用概率极简语法（Stabler，1997），我们扩展了基于期望的模型以包括关于名词短语动画的频率信息。从这些语法导出的熵减少忠实地反映了主体和客体亲属之间的不对称性（Staub，2010；Staub、Dillon 和 Clifton, 2017)，以及动画对测量难度分布的影响 (Lowder & Gordon, 2012; Traxler, Morris, & Seely, 2002)。将特定解析器状态下剩余备选方案的概率分布可视化，使我们能够探索新的、语言上合理的对观察到的处理不对称性的解释，包括对相对化论证的期望影响特定类型关系从句的处理方式 (Wagers & Pendleton, 2016 ）。