Task sensitive attention tracking

本研究的目标是开发确定用户任务的技术，并利用这些信息跟踪和/或预测注意力资源的分配。现有的追踪人们对电视、网页或杂志等媒体注意力的方法有很多，一般都是基于对显性注意力的追踪，也就是对眼球运动的追踪。这些方法适用于受试者执行需要将目光集中在感兴趣的物体上的任务，比如阅读。然而，当一个人从事需要外围警戒的任务时（例如在许多运动或玩视频游戏中），这些方法几乎没有用处。相反，我们需要的是隐蔽的注意力追踪，或者知道人们的注意力在哪里偏离了他们的中心视觉。“从他们的眼角往外看”)。隐蔽注意力跟踪通常使用图像显著性模型来完成，这是对哪些视觉特征最吸引注意力的规范。尽管图像显著性模型已经得到了广泛的研究，并且相当发达，但它们最多只能占人类实际注意力分配的75%左右。我们认为，这一缺点是由于模型中缺乏任务依赖性。因此，我们建议开发基于机器学习的技术来确定一个主体正在执行的任务，以调整任务依赖的注意力模型。

The goal of the research is to develop techniques for determining user task and using this information in tracking and/or predicting the allocation of attention resources. There are many existing methods for tracking attention to media such as television, web-pages or magazines, generally based on tracking of overt attention, that is, the tracking of eye movements. These methods are appropriate when the subject is carrying out a task that requires gaze to be centred on the objects of interest, such as in reading. However, these methods are of little use when the person is engaged in tasks requiring peripheral vigilance (such as in many sports or playing of video games). Instead, what is needed is covert attention tracking, or knowing where people are paying attention away from their central vision (i.e. "looking out of the corner of their eyes"). Covert attention tracking is typically done using a model of image salience, which is the specification of which visual features are most attractive to attention. Although image salience models have been extensively researched and are quite well-developed, they can, at best, only account for about 75% of the actual attention allocations by humans. It is our view that this shortcoming is due to a lack of task-dependence in the models. Thus, we propose to develop machine-learning based techniques for determining the task being carried out by a subject, in order to tune task-dependent attention models.