大规模证据表明,单词可预测性对阅读时间的对数效应
Cory Shain1, Clara Meister2, Tiago Pimentel3
1Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139.
概括
人类语言理解包括理解单词的可预测性. 这项研究发现可预测性和处理难度之间存在对数关系,这表明概率推理发挥着关键作用.
科学领域:
- 认知科学 认知科学
- 心理语言学 心理语言学
- 计算语言学 计算语言学
背景情况:
- 实时语言理解涉及复杂含义的快速解码.
- 单词可预测性影响处理难度,但潜在的认知过程仍在争论中.
- 现有的理论提出线性 (预期处理) 或对数/超对数 (概率推理) 效应.
研究的目的:
- 研究语言理解中的可预测性效应背后的认知过程.
- 为了确定字的可预测性和处理需求之间的准确关系.
- 要区分预测处理和概率推理模型.
主要方法:
- 对六个大规模阅读数据集的分析.
- 使用各种统计语言模型估计下一个词的概率.
- 用先进的非线性回归技术建模阅读时间.
主要成果:
- 在数据集中观察到词语可预测性对处理难度的一致的对数效应.
- 这一发现支持了处理需求以对数方式扩展而具有可预测性的模型.
- 结果挑战了预测纯线性关系的理论.
结论:
- 概率推理是人类语言处理的关键组成部分.
- 语言理解的认知成本以对数的方式与单词可预测性相对应.
- 这项研究为推断在实时语言理解中的作用提供了强有力的经验支持.
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