Algorithmic Consequences of Particle Filters for Sentence Processing: Amplified Garden-Paths and Digging-In Effects

TL;DR

This paper explores how particle filter models, which explicitly represent structural ambiguity, can explain certain real-time effects in sentence processing, such as amplified garden-path effects and digging-in effects, unlike traditional surprisal-based models.

Contribution

It proves algorithmic consequences of particle filter models, demonstrating how resampling causes digging-in effects and how these effects depend on particle count, advancing understanding of sentence processing mechanisms.

Findings

Particle filter models amplify garden-path effects.

Resampling causes real-time digging-in effects.

Digging-in effects decrease as particle count increases.

Abstract

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.