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Computational Cognitive Modeling for the Diagnosis of Specific Language Impairment

机译:诊断特定语言障碍的计算认知建模

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Specific Language Impairment (SLI), as many other cognitive deficits, is difficult to diagnose given its heterogeneous profile and its overlap with other impairments. Existing techniques are based on different criteria using behavioral variables on different tasks. In this paper we propose a methodology for the diagnosis of SLI that uses computational cognitive modeling in order to capture the internal mechanisms of the normal and impaired brain. We show that machine learning techniques that use the information of these models perform better than those that only use behavioral variables.
机译:与其他许多认知缺陷一样,由于特定语言障碍(SLI)的异质性以及与其他障碍的重叠,因此很难诊断。现有技术基于在不同任务上使用行为变量的不同标准。在本文中,我们提出了一种使用计算认知建模来诊断SLI的方法,以捕获正常和受损大脑的内部机制。我们表明,使用这些模型的信息的机器学习技术的性能要优于仅使用行为变量的机器学习技术。

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