The Predictive Coding Account of Schizophrenia: Dysfunctional Interaction across Linguistic Levels?

精神分裂症的特点是在语言层次的多个层次上出现明显的语言功能障碍，从较低水平的听觉感知到较高水平的语义处理。迄今为止，精神分裂症中语言缺陷的神经病理学仍未得到解决。在这里，我们的目标是在预测编码的框架内为这些语言缺陷提供一个统一的电生理解释，这将精神分裂症中的语言功能障碍解释为预测和传入感官输入之间的不平衡。具体而言，根据现有的临床神经科学研究，研究了在压缩语言水平内的语言功能障碍，我们假设患者在较高抽象语言水平的预测和较低听觉水平的预测误差之间的相互作用中受损，并且这些损伤的研究可能为幻听背后的功能缺陷提供现象学解释。为此，我们将使用脑电图（EEG）来比较三组参与者之间的在线语音感知和语言处理：有和没有幻听的精神分裂症患者，以及匹配的健康对照，使用三个工作包（wp）。在WP1中，我们用经典的古怪范式和基于句子的范式考察了音节遗漏形式的听觉感知，以检验统计（古怪）/语义（句子）预测与听觉感知之间的相互作用是否在精神分裂症中受损。在WP2中，将采用一种自然主义范式，通过最先进的计算建模获得逐词的语义预测指数和音素级预测误差。在WP3中，我们用静息状态脑电图检测兴奋和抑制（im）平衡。我们假设功能障碍语言预测的脑电图标记（事件相关电位，窄带振荡）在患者和对照组之间，以及在有幻听和没有幻听的患者之间是可分离的。此外，WP1-3的EEG标记将在WP4中使用现代机器学习方法进行进一步分析，以测试电生理解释是否支持基于EEG的分类和聚类。总而言之，作为第一个用脑电图解决精神分裂症跨语言水平的语言预测障碍和预测错误的项目，该项目不仅将提供对精神分裂症语言缺陷的神经生物学理解，而且将提供有利于临床实践的翻译知识。

Schizophrenia is characterized by marked language dysfunctions occurring at multiple levels of the linguistic hierarchy, ranging from lower-level auditory perception to higher-level semantic processing. To date, the neuropathology of language deficits in schizophrenia remains unresolved. Here, we aim at providing a unitary electrophysiological account of these linguistic deficits within the framework of predictive coding, which explains language dysfunctions in schizophrenia as an imbalance between prediction and incoming sensory inputs. Specifically, stepping from extant clinical neuroscience studies that investigate language dysfunctions within encapsulated linguistic levels, we hypothesize that patients are impaired in the interaction between prediction from higher abstract linguistic levels and prediction error from lower auditory sensory levels, and that the study of these impairments may provide a phenomenological explanation of the functional deficit underlying auditory hallucinations. To this end, we will employ electroencephalography (EEG) to compare online speech perception and language processing between three groups of participants: patients with schizophrenia with and without auditory hallucinations, and matched healthy controls, with three work packages (WPs). In WP1, we examine auditory perception in the form of syllable omission with a classic oddball paradigm and a sentence-based paradigm, to test if interaction between statistic (oddball) / semantic (sentence) prediction and auditory perception is impaired in schizophrenia. In WP2, a naturalistic paradigm will be employed in which word-by-word indices of semantic prediction and phoneme-level prediction error are obtained through state-of-the-art computational modelling. In WP3, we examine excitation and inhibition (im)balance with resting-state EEG. We hypothesize that EEG markers of dysfunctional linguistic prediction (event related potentials, narrow-band oscillations) are dissociable between patients and controls, and between patients with and without auditory hallucinations. Additionally, the EEG markers from WP1–3 will be further analyzed in WP4 with modern machine learning methods, to test if the electrophysiological account could support EEG-based classification and clustering. To summarize, being the first to address impaired linguistic prediction and prediction error across linguistic levels in schizophrenia with EEG, the project will not only provide a sharpened neurobiological understanding of language deficits in schizophrenia, but also will provide translational knowledge that benefits clinical practice.