论文标题
视觉关注的感知增强是任务依赖于自然主义环境
The perceptual boost of visual attention is task-dependent in naturalistic settings
论文作者
论文摘要
自上而下的注意力使人们可以专注于与任务相关的视觉信息。由此产生的感知是否可以提高任务依赖于自然主义环境？我们的目标是通过大规模的计算实验来回答这一点。首先，我们设计了一个视觉任务的集合，每个任务都包括从所选任务集（Imagenet类别的子集）分类的图像。任务的性质是确定任务集中包含哪些类别的。其次，在每个任务上，我们都会训练一个引起注意的神经网络，然后将其准确性与基线网络的准确性进行比较。我们表明，随着任务集难度的增加，注意力的感知提升更加强大，随着任务集大小的增加而弱弱，并且在任务集中的感知相似性越来越弱。
Top-down attention allows people to focus on task-relevant visual information. Is the resulting perceptual boost task-dependent in naturalistic settings? We aim to answer this with a large-scale computational experiment. First, we design a collection of visual tasks, each consisting of classifying images from a chosen task set (subset of ImageNet categories). The nature of a task is determined by which categories are included in the task set. Second, on each task we train an attention-augmented neural network and then compare its accuracy to that of a baseline network. We show that the perceptual boost of attention is stronger with increasing task-set difficulty, weaker with increasing task-set size and weaker with increasing perceptual similarity within a task set.
