用粒子滤波模型揭示句子处理中歧义放大与越陷越深现象
Algorithmic Consequences of Particle Filters for Sentence Processing: Amplified Garden-Paths and Digging-In Effects
- 用粒子滤波显式表示句法假设，模拟人类理解过程
- 重采样机制导致歧义区域越长、解歧越难，且与粒子数成反比
- 适用于研究语言理解中认知负荷的动态机制，适合计算语言学和认知科学读者
根据意外度理论，语言表征仅通过意外度瓶颈影响处理难度。目前对意外度的最佳估计来自大语言模型，但它们缺乏对结构歧义的显式表征。尽管LLM意外度能跨语言预测阅读时间，但在结构预期被违反时系统性低估处理难度，暗示歧义表征在句法处理中具有因果作用。粒子滤波模型提供替代方案，将句法假设显式表示为有限粒子集。我们证明了该模型的若干算法后果，包括花园路径效应的放大。最关键的是，我们展示重采样这一常见操作会内在产生实时越陷越深效应——解歧难度随歧义区域长度增加而上升。越陷深度与粒子数成反比：完全并行模型不预测此效应。
原文摘要 · Abstract (English)
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.
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