Task sensitive attention tracking

研究的目标是开发技术，确定用户的任务，并使用这些信息在跟踪和/或预测的注意力资源的分配。存在许多现有的用于跟踪对诸如电视、网页或杂志的媒体的注意力的方法，这些方法通常基于对明显注意力的跟踪，即，对眼睛运动的跟踪。当受试者正在执行需要注视集中在感兴趣的对象上的任务时，例如在阅读中，这些方法是合适的。然而，当人从事需要外围警惕的任务时（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，我们需要的是内隐注意力跟踪，或者知道人们在远离中央视觉的地方注意力集中（即“从眼角看出去”）。内隐注意跟踪通常使用图像显著性模型来完成，这是视觉特征最吸引注意力的规范。虽然图像显著性模型已经得到了广泛的研究，并且发展得相当成熟，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，用于确定受试者正在执行的任务，以调整任务相关的注意力模型。

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.