Task sensitive attention tracking

这项研究的目标是开发确定用户任务并使用这些信息来跟踪和/或预测注意力资源分配的技术。有许多现有的方法用于跟踪对诸如电视、网页或杂志之类的媒体的注意力，通常基于对公开注意力的跟踪，即对眼睛运动的跟踪。这些方法适用于受试者在执行一项需要将目光集中在感兴趣的对象上的任务时，例如在阅读中。然而，当人从事需要外围警觉的任务时(如在许多运动或玩视频游戏中)，这些方法几乎没有用。相反，我们需要的是隐蔽的注意力跟踪，也就是知道人们的注意力从中心视觉转移到了哪里(即“从眼角向外看”)。隐蔽注意力跟踪通常是使用图像显著模型来完成的，图像显著模型是视觉特征最吸引注意力的规范。虽然图像显著模型已经得到了广泛的研究和相当完善的开发，但它们充其量只能占人类实际注意力分配的75%左右。我们认为，这一缺陷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由受试者执行的任务，以便调整任务相关的注意模型。

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.