Psychiatric research is undergoing significant advances in an emerging subspeciality of computational psychiatry, building upon cognitive neuroscience research by expanding to neurocomputational modeling. Here, we illustrate some research trends in this domain using work on proactive cognitive control deficits in schizophrenia as an example. We provide a selective review of formal modeling approaches to understanding cognitive control deficits in psychopathology, focusing primarily on biologically plausible connectionist-level models as well as mathematical models that generate parameter estimates of putatively dissociable psychological or neural processes. We illustrate some of the advantages of these models in terms of understanding both cognitive control deficits in schizophrenia and the potential roles of effort and motivation. Further, we highlight critical future directions for this work, including a focus on establishing psychometric properties, additional work modeling psychotic symptoms and their interaction with cognitive control, and the need to expand both behavioral and neural modeling to samples that include individuals with different mental health conditions, allowing for the examination of dissociable neural or psychological substrates for seemingly similar cognitive impairments across disorders.

中文翻译：

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精神分裂症的认知控制：计算方法的进展。


精神病学研究在认知神经科学研究的基础上扩展到神经计算模型，在计算精神病学的新兴分支领域取得了重大进展。在这里，我们以精神分裂症主动认知控制缺陷的研究为例，说明该领域的一些研究趋势。我们对理解精神病理学中认知控制缺陷的正式建模方法进行了选择性回顾，主要关注生物学上合理的联结主义水平模型以及生成假定可分离心理或神经过程参数估计的数学模型。我们阐述了这些模型在理解精神分裂症认知控制缺陷以及努力和动机的潜在作用方面的一些优势。此外，我们强调了这项工作未来的关键方向，包括重点建立心理测量特性、对精神病症状及其与认知控制的相互作用进行建模的额外工作，以及将行为和神经模型扩展到包括具有不同心理健康状况的个体的样本的需要条件，允许检查可分离的神经或心理基础，以发现跨疾病的看似相似的认知障碍。