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This paper analyzes the algorithmic consequences of using particle filters to model human sentence processing, focusing on how they handle structural ambiguity. It proves that particle filter models, unlike surprisal-based models, amplify garden-path effects and, critically, inherently produce "digging-in" effects during disambiguation due to the resampling mechanism. The magnitude of the digging-in effect is shown to scale inversely with the number of particles, highlighting a trade-off between representational capacity and processing difficulty predictions.
Particle filter models of sentence processing inherently predict "digging-in" effects—where disambiguation difficulty increases with the length of the ambiguous region—a phenomenon not captured by surprisal-based models.
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.
MIT CSAIL