来自语言模型的惊喜可以预测ERP在处理预测-参数结构中只有在被代理偏好原则丰富的情况下才能预测ERP.
Eva Huber1,2, Sebastian Sauppe1,2,3, Arrate Isasi-Isasmendi1,2
1Department of Comparative Language Science, University of Zurich, Zurich, Switzerland.
Neurobiology of language (Cambridge, Mass.)
|April 22, 2024
概括
人工神经网络语言模型捕捉了人类的句子处理. 然而,人类语言理解也需要代理优先原则,特别是在德语,巴斯克语和印度语等语言的动词结尾句子中.
科学领域:
- 认知科学 认知科学
- 计算语言学 计算语言学
- 神经科学是一个神经科学.
背景情况:
- 人工神经网络 (ANN) 语言模型越来越多地模拟人类的句子处理,预测像N400振幅这样的神经信号.
- 这些模型的成功引发了这样一个问题:人类语言处理是否仅仅依赖于一般的ANN架构和语言输入.
- 反映语义或语法处理困难的N400效应是心理语言学的关键神经测量.
研究的目的:
- 通过检查动词结尾语言中的N400效应,测试ANN单独解释人类句子处理的假设.
- 研究人类语言理解中"代理者偏好"原则的必要性.
- 探索惊喜和代理优先原则影响的跨语言变异.
主要方法:
- 在处理德语,巴斯克语和印度语的动词结尾句子时分析N400效应 (幅度和地形).
- 使用堆叠的贝叶斯概括增值模型来预测N400数据.
- 比较基于模型的surprisal与surprisal和代理优先原则的组合的预测能力.
主要成果:
- 在德语,巴斯克语和印度语的动词结尾句子中的N400效应最好通过将基于模型的surprisal与Agent Preference原则相结合来预测.
- 代理优先原则,它偏向于将模两可的名词短语解释为代理,对于解释重新分析效应是必要的.
- 代理人优先原则的影响在跨语言上有所不同,在德语中最弱,在印度语中最强,在巴斯克语中最强,与语法特征相关,允许未标记的NP作为患者发挥作用.
结论:
- 语言模型需要结合代理偏好原则来提高其神经生物学可信性.
- 人类语言处理理论受益于将意外估计与诸如代理偏好 (Agent Preference) 等原则相结合,这些原则可能具有不同的进化起源.
- 跨语言的结构性质会影响处理原理的强度,例如代理偏好.
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