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

机译:了解自闭症行为的认知机制:经常性神经网络研究

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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:建议具有自闭症谱系障碍的人们表现出非典型感知和认知处理的差异。然而,在行为研究中,这种差异通常难以验证。显然,认知处理的差异并不总是造成行为的损害。为了调查可以解释认知和行为水平之间的这种不匹配,我们模拟并评估使用经常性神经网络模拟学习的过程。我们系统地调整网络的学习参数,这些参数与学习的精度相关联,这是具有自闭症的个人之间可能不同的因素,并且通常开发个人。我们在任务性能(行为级别)方面评估培训的网络以及在学习期间出现的内部表示的结构(认知水平)的结构。我们的研究结果表明,不同的内部网络表示可能导致类似的行为网络输出。较少的结构性内容不一定导致性能下降,但也可以与良好的仿形表现相关。此外,我们发现我们的设置中结构性内容良好的内部陈述出现了自上而下的预测和自下而上的信息处理的适当整合,这一发现与发育心理学的理论相结合。

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