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首页> 外文会议>International Conference on Computing for Geospatial Research and Application >Adaptive and Interactive Design Based on Big Data Computational Model for Treating Autism
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Adaptive and Interactive Design Based on Big Data Computational Model for Treating Autism

机译：基于大数据计算模型的自闭症自适应交互式设计

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We propose an affordable and adaptive computer interaction design to treat autism, where the progress monitoring can eliminate the danger of addiction. The suggested plan is based on the concept of the Big Data computational model that selects only a small part of information to operate explicitly while the rest of information contributes to this selection just implicitly. Understanding the information-processing model of the brain is vital in order to treat mental disorders such as autism. Evolutionary biology asserts that the unconsciousness is vital for the functionality of the brain. The human brain consists of billions of neurons and trillions of synapses. Its active memory is just 1 gigabit (Fig. 1). Yet, it is able to processes and retrieve information faster than a computer. Given the small active memory but efficient processing, we hypothesize that the brain's information processing is influenced by the unconsciousness or background information. Mental disorders occur due to distortions in this background information. Fundamental distortions are (Fig. 2): (1) presence of excessive and unwanted information in the background, and (2) absence of vital information. Depression is an example for the first (e.g. traumatic events stored in background). Autism is an example of the later. Our suggested approach (Fig. 3) will have an Oracle machine -- a qualified psychiatrist or supervisor computer program -- will provide specific suggestions to the individual based on their type of mental disorder and supervise their selections. The Oracle machine will show individual the ideal choice in case they have selected a wrong choice. Given the information- processing model of the brain, these suggested selections and ideal choices among the selections would presumably affect the unconsciousness of the brain thus inserting new information in the contextual background of the brain. After a certain period of time, we may expect to see the improvements in the behavior of the - ndividual with autism.
机译：我们提出了一种负担得起的自适应计算机交互设计来治疗自闭症，其中的进度监控可以消除成瘾的危险。建议的计划基于大数据计算模型的概念，该模型仅选择一小部分信息来进行显式操作，而其余信息只是隐式地促成此选择。为了治疗自闭症等精神疾病，了解大脑的信息处理模型至关重要。进化生物学断言，无意识对于大脑的功能至关重要。人脑由数十亿个神经元和数万亿个突触组成。它的活动内存仅为1 GB（图1）。但是，它能够比计算机更快地处理和检索信息。鉴于活动记忆少但处理效率高，我们假设大脑的信息处理受意识或背景信息的影响。由于此背景信息的扭曲而导致精神障碍。基本失真是（图2）：（1）背景中存在过多和不需要的信息，以及（2）不存在重要信息。情绪低落是头一个例子（例如，背景中存储的创伤事件）。自闭症是后者的一个例子。我们建议的方法（图3）将配备一台Oracle机器-合格的精神病医生或主管计算机程序-将根据个人的精神障碍类型向其提供具体建议并监督他们的选择。如果Oracle计算机选择了错误的选项，它将向他们显示理想的选择。在给定大脑的信息处理模型的情况下，这些建议的选择和选择中的理想选择可能会影响大脑的无意识，从而在大脑的背景环境中插入新的信息。一段时间后，我们可能会期望看到自闭症患者的行为得到改善。

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