导航语义空间:使用不同的计算语言模型解开精神病中的意义结构
Rui He1, Claudio Palominos1, Han Zhang1
1Department of Translation & Language Sciences, Universitat Pompeu Fabra, Carrer Roc Boronat, 138, Barcelona, 08018, Spain.
研究人员使用先进的语言模型分析精神病中的言语,以了解认知机制. 结果揭示了语义空间的双重变化,突出了患者思想内容和组织的明显变化.
科学领域:
- 认知神经科学 认知神经科学
- 计算语言学 计算语言学
- 精神病学是一个精神病学.
背景情况:
- 精神病中的言语变化通常被描述为"放松关联".
- 了解这些语义变化的认知基础对于诊断和治疗至关重要.
- 以前的研究还没有完全区分精神病中的静态和上下文语义处理.
研究的目的:
- 研究精神病中"放松关联"背后的认知机制.
- 用各种计算语言模型分析语音中的语义变化.
- 在精神病患者中区分静态和上下文语义处理.
主要方法:
- 分析了94名受试者的图片描述 (健康对照,临床高风险,第一发精神病,慢性精神分裂症).
- 使用了五种不同架构的语言模型 (FastText,BERT,Infersent,SBERT,CLIP).
- 量化语义距离和困惑,以评估语音中的意义和意想不到.
主要成果:
- 静态语言模型 (FastText,Infersent,SBERT) 显示患者的语义相似性增加.
- 语境语言模型 (CLIP,BERT) 表明语义相似性下降.
- 在第一发精神病患者中观察到更高的语义困惑.
- 静态指标与思想贫困相关,而参考指标与无组织相关.
结论:
- 精神病涉及到语义空间的双重变化:静态意义的缩小和参考意义的扩大.
- 区分静态和上下文语义处理对于理解精神病至关重要.
- 这些发现为精神病中语义变化的认知机制提供了新的见解.
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