通过静态和上下文语言模型预测人类推理处理的可分离的神经机制
Takahisa Uchida1, Nicolas Lair2,3, Hiroshi Ishiguro1
1Ishiguro Lab, Graduate School of Engineering Science, Osaka University, Osaka, Japan.
Neurobiology of language (Cambridge, Mass.)
|April 22, 2024
概括
静态和上下文语言模型 (LMs) 在事件推理方面表现出不同的能力. 像BERT这样的上下文LM擅长复杂的推断,反映人类的战略处理,而静态LM则执行更简单的任务.
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:
- 语言模型 (LMs) 展示了与人类语言处理和神经生理学的复杂关系.
- 最近的研究表明,LM嵌入可以为事件推理创建语音表示.
- 了解不同LM类如何编码事件知识对于绘制到人类推理至关重要.
研究的目的:
- 在不同的LM类 (静态与上下文) 中调查事件知识编码.
- 将LM事件推断能力与人类实验协议进行比较 (Metusalem等人,2012年;McKoon和Ratcliff,1986年).
- 探索不同LM中的语义处理和事件推理之间的关系.
主要方法:
- 利用了两个已建立的人体实验协议来推断事件.
- 静态LM (word2vec,GloVe) 和上下文LM (BERT,RoBERTa) 的推断性能进行比较.
- 评估了简单的语义处理协议与推理任务一起.
主要成果:
- 静态LM显示了两个任务的语义和推理之间的分离.
- 语境LM显示了两个任务的语义和推理处理之间的相关性.
- 静态模型执行了梅图塞勒推理;上下文模型成功地执行了麦考恩推理.
结论:
- 推理任务 (Metusalem与McKoon) 依赖于可分离的过程.
- 语境LM在McKoon推理中的成功与人类战略推理一致.
- 研究结果预测了自动与战略推理处理的独特神经生理学标记.
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