---
title: "句法信念更新：花园路径句处理困难的新解释"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2606.27206v1"
permalink: "https://feed.astralor.com/items/itm_81795aa59b092d1d090d7a20"
published: "2026-06-25T16:02:14.000Z"
score: 53
channel: "ai"
tags: ["句法信念更新","广义Rényi散度","心理语言学","概率句法分析"]
---

# 句法信念更新：花园路径句处理困难的新解释

## 速览

花园路径句对人类语言处理造成困难——句子前缀引导听者走向一种解释，直到听到一个关键词语表明初始解释错误。词汇意外性通常能很好地预测句子处理难度，但对花园路径句却失效。本文提出一种替代方法：主动预测句法树的概率分布（句法信念），并在每个新词后更新该分布。如果处理者被引入歧途，句法信念将出错，并在关键词语处需要大幅更新。使用广义Rényi散度衡量更新幅度，该度量依赖于词汇项，但完全独立于词汇项的概率。句法信念更新更好地拟合了人类阅读时间数据，提示了心理语言学中超越词汇意外性的纯非词汇替代方案的新研究方向。

## 关键点

- 花园路径句的处理困难无法由词汇意外性有效预测
- 提出句法信念更新模型，维护句法树的概率分布并逐词更新
- 使用广义Rényi散度量化更新幅度，与词汇概率无关
- 模型更好地拟合人类阅读时间数据，揭示非词汇处理机制

## 全文

Garden path sentences present a processing difficulty for humans -- the sentence prefix leads the listener towards one interpretation, until the listener hears a critical word that shows that the initial interpretation was wrong. Lexical surprisal, a measure that usually predicts sentence processing difficulty quite well, fails to provide good predictions for garden path sentences. We propose an alternative that actively predicts a probability distribution over syntactic trees (its syntactic belief) and updates that distribution after each new word. If a processor is led down a garden path, syntactic beliefs will be wrong and will require a large update at the critical word. The magnitude of the update is measured with a generalized Rényi divergence. Crucially, this metric is dependent on lexical items, but is fully independent of the probability of lexical items. This Syntactic Belief Update provides a better fit to the human reading time data on garden path sentences. This suggests a new research direction examining purely non-lexical alternatives to surprisal for psycholinguistics.

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