语法结构引导信息保持，提升语言理解效率
Syntactically-guided Information Maintenance in Sentence Comprehension
- 根据句法结构选择性维护关键信息，降低认知负担
- 预测目标数量与未完成依存关系数共同影响处理速度
- 日语数据中体现语法驱动的维护权衡，英语中不明显
在实时语言理解中，维持上下文信息至关重要，但其认知成本高且会减慢处理速度。我们假设理性语言使用者会依据句法结构有选择地保留对后续预测至关重要的信息。在此视角下，两个因素影响维护成本：预测头的数量和未完成依存关系的数量。尽管学界将两者视为竞争性假说，我们的模型认为二者不可简化为彼此。我们在自然阅读时间数据集（日语）中验证了这一点，该语言中两因素表现尤为清晰。结果还显示，放缓处理以维护信息的读者更受益于可预测性，进一步支持该理论。然而这些模式在英语中不显著，提示需进一步探讨句法在不同语言记忆高效处理中的作用。
原文摘要 · Abstract (English)
Maintaining information in context is essential in successful real-time language comprehension, but maintenance is cognitively costly and can slow processing. We hypothesize that rational language users selectively maintain information that is crucial for future prediction, guided by syntactic structure. Under this view, two factors affect maintenance cost: the number of predicted heads and the number of incomplete dependencies. Although these factors have been treated as competing hypotheses in the literature, our account predicts that they are not reducible to one another. We show this is the case in a naturalistic reading time dataset in Japanese, a language in which the two factors contrast particularly clearly. We further show that there is a tradeoff such that readers that slow down for maintenance tend to benefit more from predictability, providing additional support for the proposed account. These patterns are not evident in English, however, and we highlight some issues to be resolved to understand the contribution of syntax in memory-efficient processing of various languages.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。