Department of Psychology,Indiana University- Purdue University Indianapolis,Indianapolis, IN,USA.
Department of Psychology,University of California-Riverside,Riverside, CA,USA.
Psychol Med. 2019 Feb;49(3):440-448. doi: 10.1017/S0033291718001046. Epub 2018 Apr 25.
Conveying information cohesively is an essential element of communication that is disrupted in schizophrenia. These disruptions are typically expressed through disorganized symptoms, which have been linked to neurocognitive, social cognitive, and metacognitive deficits. Automated analysis can objectively assess disorganization within sentences, between sentences, and across paragraphs by comparing explicit communication to a large text corpus.
Little work in schizophrenia has tested: (1) links between disorganized symptoms measured via automated analysis and neurocognition, social cognition, or metacognition; and (2) if automated analysis explains incremental variance in cognitive processes beyond clinician-rated scales. Disorganization was measured in schizophrenia (n = 81) with Coh-Metrix 3.0, an automated program that calculates basic and complex language indices. Trained staff also assessed neurocognition, social cognition, metacognition, and clinician-rated disorganization.
Findings showed that all three cognitive processes were significantly associated with at least one automated index of disorganization. When automated analysis was compared with a clinician-rated scale, it accounted for significant variance in neurocognition and metacognition beyond the clinician-rated measure. When combined, these two methods explained 28-31% of the variance in neurocognition, social cognition, and metacognition.
This study illustrated how automated analysis can highlight the specific role of disorganization in neurocognition, social cognition, and metacognition. Generally, those with poor cognition also displayed more disorganization in their speech-making it difficult for listeners to process essential information needed to tie the speaker's ideas together. Our findings showcase how implementing a mixed-methods approach in schizophrenia can explain substantial variance in cognitive processes.
连贯地传达信息是交流的一个基本要素，而在精神分裂症中，这种交流往往会被打乱。这些紊乱通常通过紊乱的症状表现出来，这些症状与神经认知、社会认知和元认知缺陷有关。自动化分析可以通过将明确的交流与大型文本语料库进行比较，客观地评估句子内、句子间和段落间的不连贯性。
在精神分裂症中，很少有研究测试过：（1）通过自动化分析测量的紊乱症状与神经认知、社会认知或元认知之间的联系；（2）自动化分析是否能在认知过程中解释临床评定量表之外的增量方差。使用 Coh-Metrix 3.0 对精神分裂症患者（n=81）的紊乱程度进行了测量，这是一种自动程序，可计算基本和复杂的语言指标。经过培训的工作人员还评估了神经认知、社会认知、元认知和临床评定的紊乱程度。
研究结果表明，所有三种认知过程都与至少一个自动化的紊乱指标显著相关。当将自动化分析与临床评定量表进行比较时，它可以在神经认知和元认知方面解释临床评定量表之外的显著方差。当这两种方法结合使用时，它们可以解释神经认知、社会认知和元认知的 28-31%的方差。
本研究说明了自动化分析如何突出紊乱在神经认知、社会认知和元认知中的具体作用。一般来说，认知能力较差的人在说话时也会表现出更多的紊乱，这使得听众难以处理说话者的思想中需要联系在一起的关键信息。我们的研究结果展示了在精神分裂症中实施混合方法可以解释认知过程中的大量方差。
