精神分裂症中词汇概念之间的语义距离收缩，并与句子层级的困惑度增加同时发生。
*引用: He, R., , Palominos, C., Zhang, H., Alonso-Sánchez, M. F., Palaniyappan, L., & Hinzen, W. (2024). Navigating the semantic space: Unraveling the structure of meaning in psychosis using different computational language models. Psychiatry research, 333, 115752. Advance online publication. https://doi.org/10.1016/j.psychres.2024.115752
Abstract
Speech in psychosis has long been ascribed as involving ‘loosening of associations’. We pursued the aim to elucidate its underlying cognitive mechanisms by analysing picture descriptions from 94 subjects (29 healthy controls, 18 participants at clinical high risk, 29 with first-episode psychosis, and 18 with chronic schizophrenia), using five language models with different computational architectures: FastText, which represents meaning non-contextually/statically; BERT, which represents contextual meaning sensitive to grammar and context; Infersent and SBERT, which provide sentential representations; and CLIP, which evaluates speech relative to a visual stimulus. These models were used to quantify semantic distances crossed between successive tokens/sentences, and semantic perplexity indicating unexpectedness in continuations. Results showed that, among patients, semantic similarity increased when measured with FastText, Infersent, and SBERT, while it decreased with CLIP and BERT. Higher perplexity was observed in first-episode psychosis. Static semantic measures were associated with clinically measured impoverishment of thought and referential semantic measures with disorganization. These patterns indicate a shrinking conceptual semantic space as represented by static language models, which co-occurs with a widening in the referential semantic space as represented by contextual models. This duality underlines the need to separate these two forms of meaning for understanding mechanisms involved in semantic change in psychosis.
以下为机器翻译摘要：
精神疾病中的言语长期以来被认为涉及“联想的松动”。 我们分析了 94 名受试者（29 名健康对照、18 名临床高危、29 名首发精神病患者和 18 名慢性精神分裂症参与者）的图片描述，使用具有不同计算能力的五种语言模型来阐明其潜在的认知机制，包括：FastText，代表非上下文/静态的含义； BERT，代表对语法和上下文敏感的上下文含义； Infersent 和 SBERT，提供句子表示； CLIP，评估与视觉刺激相关的语音。 这些模型用于量化连续词/句之间交叉的语义距离，以及代表意外情况的语义困惑度。 结果显示，在患者中，使用 FastText、Infersent 和 SBERT 测量时，语义相似性增加，而使用 CLIP 和 BERT 测量时，语义相似性下降。 在首发精神病中观察到更高的困惑。 静态语义指标与临床测量的思维贫乏相关，而指代语义指标则与思维混乱相关。 这些模式表明静态语言模型所代表的概念语义空间发生收缩，而上下文模型所代表的指称语义空间则发生扩大。 这种二元性强调，我们需要将这两种形式的意义分开，以理解精神疾病中语义变化所涉及的机制。