Adaptive Response Technology for Autism Spectrum Disorders Intervention

描述(由申请人提供)：据估计，美国每110名儿童中就有1人受到自闭症谱系障碍(ASD)的影响。ASD的识别和有效治疗通常被描述为一种公共卫生紧急情况，每年造成350亿至900亿美元的成本。旨在解决较高水平的社会和适应性障碍的传统干预措施已被证明对患有自闭症的学龄儿童和青少年最低限度有效，并被认为是由于传统方法未能以适当的高剂量系统地将干预策略与自然环境内和跨自然环境的特定技能缺陷相匹配。在这项拨款提案中，为了解决上述问题，制定了一项研究计划，将智能适应技术作为一种基于虚拟现实(VR)的干预模式来治疗a)核心社会缺陷和b)应用适应行为技能治疗患有自闭症的儿童和青少年。这项技术将应用于具有可控难度的能力，可以根据参与者在参与任务时的行为参与预测，将其适应VR环境，实时适应社交和适应性行为干预场景。行为投入是通过情感和注意力状态来运作的，因此，干预技术将对个体儿童投入的变化如何预测任务表现非常敏感。受规则控制的适应将被纳入虚拟现实互动中，并明确审查此类修改如何提高性能。简单地说，这项技术将被应用于根据个人的独特概况自动调整任务特征，希望通过应用单独调制和脚手架加固模式，损害核心领域内的任务表现将得到极大的支持。本研究的具体目标是：1)提炼作者在试点工作中开发的智能适应反应技术，并设计虚拟现实环境中的适应行为和社交任务；2)开发基于生理的个性化情感模型和基于眼睛注视信息的注意力推理机制，并设计基于规则的监督器，以允许对行为参与敏感的自适应强化策略；以及3)应用和检验预测模型和适应技术在自闭症儿童中的有效性，以改善基于虚拟现实的现实社会和适应任务相关的具体学习。这种适应性反应技术在ASD干预中的成功将为系统地探索和应用旨在将个体缺陷与靶向干预相匹配的适应性干预范例铺平道路。

DESCRIPTION (provided by applicant): It is estimated that 1 in 110 children in the United States are affected by Autism Spectrum Disorders (ASD). The identification and effective treatment of ASD is often characterized as a public health emergency incurring a $35-90 billion annual cost. Traditional interventions designed to address higher level social and adaptive impairments have been demonstrated to be minimally effective for school-aged children and adolescents with ASD and are thought to be a result of a failure of traditional methodologies to systematically match intervention strategies to specific skill deficits within and across naturalistic settings in appropriately intensive dosages. In this grant proposal, in an attempt to address the above concerns, a research plan is developed to apply intelligent adaptive technology as a Virtual Reality (VR) based intervention modality for treatment of a) core social deficits and b) applied adaptive behavioral skills for children and adolescents with ASD. The technology will be applied with capacities for controllable levels of difficulty that can be adapted with the VR environment into both social and adaptive behavioral intervention scenarios in real-time based on predictions of behavioral engagement of participants while engaged with the tasks. Behavioral engagement is operationalized by affective and attentive states and as such, the intervention technology will be sensitive to how variations in an individual child's engagement predict task performance. Rule-governed adaptation will be incorporated into VR interactions and examined explicitly in terms of how such modifications improve performance. Simply, this technology will be applied to automatically adjust task characteristics based on an individual's unique profile in hopes that task performance within core domains of impairment will be greatly bolstered by application of individually modulated and scaffolded reinforcement modalities. The specific aims of this research are: 1) to refine the intelligent adaptive response technology that the authors have already developed in their pilot work, and design both adaptive behavior and social tasks in a VR environment; 2) to develop physiology-based individualized affective models and attention inference mechanisms from eye gaze information, and to design a rule-based supervisor to allow adaptive reinforcement strategies sensitive to behavioral engagement; and 3) to apply and examine the efficacy of prediction models and the adaptive technology in children with ASD relative to improving specific learning related to VR-based and realistic social and adaptive tasks. The success of such an adaptive response technology in ASD intervention will pave the way for systematic exploration and application of adaptive intervention paradigms aimed at matching individual deficit with targeted intervention.