Under surprisal theory, linguistic representations affect processing difficulty only through the bottleneck of surprisal. Our best estimates of surprisal come from large language models, which have no explicit representation of structural ambiguity. While LLM surprisal robustly predicts reading times across languages, it systematically underpredicts difficulty when structural expectations are violated -- suggesting that representations of ambiguity are causally implicated in sentence processing. Particle filter models offer an alternative where structural hypotheses are explicitly represented as a finite set of particles. We prove several algorithmic consequences of particle filter models, including the amplification of garden-path effects. Most critically, we demonstrate that resampling, a common practice with these models, inherently produces real-time digging-in effects -- where disambiguation difficulty increases with ambiguous region length. Digging-in magnitude scales inversely with particle count: fully parallel models predict no such effect.

翻译：在惊奇度理论下，语言表征仅通过惊奇度这一瓶颈影响处理难度。我们对惊奇度的最佳估计来自大语言模型，但这类模型缺乏结构歧义的显式表征。虽然大语言模型计算的惊奇度能稳健预测跨语言的阅读时间，但在结构预期被违背时系统性地低估处理难度——这表明歧义的显式表征在句子处理中具有因果作用。粒子滤波器模型提供了一种替代方案，其中结构假说被显式表示为有限粒子集。我们证明了粒子滤波器模型的若干算法后果，包括花园路径效应的增强。最关键的是，我们证明重采样——这类模型的常见操作——会内在地产生实时固化效应：即消歧难度随歧义区域长度增加而增大。固化效应的强度与粒子数量成反比：完全并行模型预测不存在此类效应。