论文标题
内在动机在用户界面设计中的作用，以增强亚马逊MTURK的工人绩效
Role of Intrinsic Motivation in User Interface Design to Enhance Worker Performance in Amazon MTurk
论文作者
论文摘要
多年来，生物学家和科学家一直在解决海洋生物监测和鱼类种群估计的问题。现在，通过利用专门设计的水下机器人来收集海洋人口的图像，可以指示努力朝着非侵入性方法迈进。培训机器学习算法在收集的图像上，我们现在可以估算人口。反过来，这有助于强加法规来控制过度捕捞。但是，要训练这些模型，我们需要带注释的图像。十年来收集的大量图像的注释非常具有挑战性。因此，我们求助于众包平台亚马逊机械土耳其人（MTURK），以完成图像注释任务。尽管在MTURK完成工作是很快的，但是所获得的工作通常质量很差。这项工作旨在从自决理论的角度理解设计人类智能任务（命中）的人为因素。应用理论中的要素，我们设计了一个打击，以增加工人的能力和动力。在我们的实验框架内，我们发现新界面可显着提高工人绩效的准确性。
Biologists and scientists have been tackling the problem of marine life monitoring and fish stock estimation for many years now. Efforts are now directed to move towards non-intrusive methods, by utilizing specially designed underwater robots to collect images of the marine population. Training machine learning algorithms on the images collected, we can now estimate the population. This in turn helps to impose regulations to control overfishing. To train these models, however, we need annotated images. Annotation of large sets of images collected over a decade is quite challenging. Hence, we resort to Amazon Mechanical Turk (MTurk), a crowdsourcing platform, for the image annotation task. Although it is fast to get work done in MTurk, the work obtained is often of poor quality. This work aims to understand the human factors in designing Human Intelligence Tasks (HITs), from the perspective of the Self-Determination Theory. Applying elements from the theory, we design an HIT to increase the competence and motivation of the workers. Within our experimental framework, we find that the new interface significantly improves the accuracy of worker performance.
