Human visual attention is susceptible to social influences. In education,
peer effects impact student learning, but their precise role in modulating
attention remains unclear. Our experiment (N=311) demonstrates that displaying
peer visual attention regions when students watch online course videos enhances
their focus and engagement. However, students retain adaptability in following
peer attention cues. Overall, guided peer attention improves learning
experiences and outcomes. These findings elucidate how peer visual attention
shapes students' gaze patterns, deepening understanding of peer influence on
learning. They also offer insights into designing adaptive online learning
interventions leveraging peer attention modelling to optimize student
attentiveness and success.

通过实验证明展示同伴的视觉注意区域可以提高学生在线课程视频观看的专注度和参与度，但是学生仍然保留按照同伴注意引导的适应性。综合来看，由同伴引导的注意力可以改善学习体验和结果，这些研究结果增进了我们对同伴对学习的影响以及如何利用同伴注意建模设计自适应在线学习干预措施来优化学生的专注度和成功的理解。

同儕關注提升學生學習

Peer attention enhances student learning

Image representations are commonly learned from class labels, which are a
simplistic approximation of human image understanding. In this paper we
demonstrate that transferable representations of images can be learned without
manual annotations by modeling human visual attention. The basis of our
analyses is a unique gaze tracking dataset of sonographers performing routine
clinical fetal anomaly screenings. Models of sonographer visual attention are
learned by training a convolutional neural network (CNN) to predict gaze on
ultrasound video frames through visual saliency prediction or gaze-point
regression. We evaluate the transferability of the learned representations to
the task of ultrasound standard plane detection in two contexts. Firstly, we
perform transfer learning by fine-tuning the CNN with a limited number of
labeled standard plane images. We find that fine-tuning the saliency predictor
is superior to training from random initialization, with an average F1-score
improvement of 9.6% overall and 15.3% for the cardiac planes. Secondly, we
train a simple softmax regression on the feature activations of each CNN layer
in order to evaluate the representations independently of transfer learning
hyper-parameters. We find that the attention models derive strong
representations, approaching the precision of a fully-supervised baseline model
for all but the last layer.

本文研究通过建立基于人类视觉注意的模型来学习图像的可转移表示，以取代直接学习类标签所造成的图像理解的简化。通过在超声视频中预测视觉显著性或注视点回归，利用卷积神经网络从注视数据集中学习视觉注意模型，并评估该模型在超声标准平面检测任务中的可迁移性。实验表明，在有限标记的场景下，微调视觉显著性预测器优于随机初始化，平均 F1 分数整体提高 9.6％，对于心脏平面提高了 15.3％。另外，该学习方法生成的表示发现是可靠的，在近乎监督的基准模型的精度上接近。

通过模拟超声医师的视觉注意力进行超声图像表示学习

Ultrasound Image Representation Learning by Modeling Sonographer Visual  Attention

Understanding and predicting the human visual attentional mechanism is an
active area of research in the fields of neuroscience and computer vision. In
this work, we propose DeepFix, a first-of-its-kind fully convolutional neural
network for accurate saliency prediction. Unlike classical works which
characterize the saliency map using various hand-crafted features, our model
automatically learns features in a hierarchical fashion and predicts saliency
map in an end-to-end manner. DeepFix is designed to capture semantics at
multiple scales while taking global context into account using network layers
with very large receptive fields. Generally, fully convolutional nets are
spatially invariant which prevents them from modeling location dependent
patterns (e.g. centre-bias). Our network overcomes this limitation by
incorporating a novel Location Biased Convolutional layer. We evaluate our
model on two challenging eye fixation datasets -- MIT300, CAT2000 and show that
it outperforms other recent approaches by a significant margin.

DeepFix 是一种全新的卷积神经网络，可用于准确地预测显著性图，并集成了多尺度语义和全局上下文，并通过引入 Location Biased 卷积层来解决空间不变性问题，其表现优越。