Abstract Behavioral measures of incremental language comprehension difficulty form a crucial part of the empirical basis of psycholinguistics. The two most common methods for obtaining these measures have significant limitations: eye tracking studies are resource-intensive, and self-paced reading can yield noisy data with poor localization. These limitations are even more severe for web-based crowdsourcing studies, where eye tracking is infeasible and self-paced reading is vulnerable to inattentive participants. Here we make a case for broader adoption of the Maze task, involving sequential forced choice between each successive word in a sentence and a contextually inappropriate distractor. We leverage natural language processing technology to automate the most researcher-laborious part of Maze – generating distractor materials – and show that the resulting A(uto)-Maze method has dramatically superior statistical power and localization for well-established syntactic ambiguity resolution phenomena. We make our code freely available online for widespread adoption of A-maze by the psycholinguistics community.

中文翻译：

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Maze Made Easy：更好、更轻松地测量增量处理难度

摘要 语言理解难度增加的行为测量是心理语言学经验基础的重要组成部分。获得这些测量值的两种最常见的方法有很大的局限性：眼动追踪研究是资源密集型的，自定进度阅读会产生定位较差的嘈杂数据。对于基于网络的众包研究而言，这些限制更为严重，因为眼动追踪不可行，而且自定进度的阅读容易受到注意力不集中的参与者的影响。在这里，我们为更广泛地采用迷宫任务提供了一个案例，涉及在句子中的每个连续单词和上下文不合适的干扰项之间进行顺序强制选择。我们利用自然语言处理技术来自动化迷宫中研究人员最费力的部分——生成干扰材料——并表明由此产生的 A(uto)-Maze 方法对于完善的句法歧义解决现象具有显着优越的统计能力和定位。我们在网上免费提供我们的代码，以便心理语言学社区广泛采用 A-maze。