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Understanding the cognitive mechanisms underlying autistic behavior: a recurrent neural network study

机译:了解自闭症行为的潜在认知机制:递归神经网络研究

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People with autism spectrum disorder are suggested to exhibit atypical perception and differences in cognitive processing. In behavioral studies, however, such differences are often difficult to verify. Apparently, differences in cognitive processing do not always cause an impairment of behavior. To investigate how such a mismatch between cognitive and behavioral level could be explained, we model and evaluate the process of learning to imitate using recurrent neural networks. We systematically adjust learning parameters of the network which are linked to the precision of learning, a factor that might differ between individuals with autism and typically developed individuals. We evaluate the trained networks in terms of task performance (behavioral level) as well as in terms of the structure of the internal representation that emerges during learning (cognitive level). Our findings demonstrate that comparable behavioral network output can be caused by different internal network representations. A less well structured internal representation does not necessarily result in a decline in performance, but can also be associated with good imitation performance. Additionally, we find evidence that well structured internal representations in our setting emerge with an appropriate integration of top-down predictions and bottom-up information processing, a finding which integrates well with theories from developmental psychology.
机译:建议患有自闭症谱系障碍的人表现出非典型的知觉和认知过程的差异。然而,在行为研究中,此类差异通常难以验证。显然,认知过程的差异并不总是会导致行为受损。为了研究如何解释认知水平与行为水平之间的不匹配,我们对使用递归神经网络进行模仿的学习过程进行了建模和评估。我们系统地调整网络的学习参数,这些参数与学习的准确性有关,这是自闭症患者和通常发育的个体之间可能会有所不同的因素。我们根据任务绩效(行为水平)以及学习过程中出现的内部表示的结构(认知水平)评估受过训练的网络。我们的发现表明,可比的行为网络输出可能是由不同的内部网络表示形式引起的。结构性较差的内部表示形式不一定会导致性能下降,但也可能与良好的模仿性能相关。此外,我们发现证据表明,通过适当整合自上而下的预测和自下而上的信息处理,可以形成结构良好的内部表示形式,这一发现与发展心理学的理论很好地融合在一起。

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