Using temporarily ambiguous garden-path sentences ("While the team trained the striker wondered ...") as a test case, we present a latent-process mixture model of human reading behavior across four different reading paradigms (eye tracking, uni- and bidirectional self-paced reading, Maze). The model distinguishes between garden-path probability, garden-path cost, and reanalysis cost, and yields more realistic processing cost estimates by taking into account trials with inattentive reading. We show that the model is able to reproduce empirical patterns with regard to rereading behavior, comprehension question responses, and grammaticality judgments. Cross-validation reveals that the mixture model also has better predictive fit to human reading patterns and end-of-trial task data than a mixture-free model based on GPT-2-derived surprisal values. We discuss implications for future work.

翻译：本研究以暂时性歧义的"花园路径句"（例如"While the team trained the striker wondered..."）为测试案例，提出了一种涵盖四种阅读范式（眼动追踪、单向与双向自控步速阅读、迷宫任务）的人类阅读行为潜在过程混合模型。该模型能够区分花园路径概率、花园路径代价与再分析代价，并通过考虑注意力不集中阅读试次，获得更符合实际认知负荷的加工代价估计。研究表明，该模型能够复现重读行为、理解问题回答及语法判断方面的实证规律。交叉验证显示，相较于基于GPT-2惊奇度值的无混合模型，本混合模型对人类阅读模式及试次结束任务数据具有更好的预测拟合度。最后，本文探讨了该研究对未来工作的启示。