Driven by Curiosity: Interaction Techniques and Incentive Mechanisms for Crowdsourcing Scientific Tasks

科学越来越数据密集;然而，许多涉及数据收集，注释和分析的研究任务尚未完全由计算机自动化。公民科学的理念是通过网络吸引大量的人来贡献和处理科学数据，以解决数据的规模，传播和复杂性超出算法分析能力或小团队带宽的问题。 这些公民科学平台的成功部分是由于现有的业余爱好者群体（例如，观鸟者，业余天文学家），他们已经感兴趣并渴望提供帮助。不幸的是，相反的情况实际上更常见--科学家通常拥有大多数人不熟悉的研究数据和问题，并且很难将劳动密集型数据处理任务转化为本质上激励参与者去做的事情。缺乏激励和维持参与的机制是阻碍公民科学系统广泛采用的主要障碍。 在这项拟议的研究中，我们将通过调查好奇心作为参与的驱动力来解决公民科学参与的问题。特别是，我们将开发新的互动技术和激励机制，以支持基于好奇心的心理模型-洛温斯坦的信息差距理论-的“好奇心诱导设计”。该理论认为，当人们意识到知识中的差距时，他们会感到好奇，并进行探索性的信息寻求行为，以缩小信息差距并解决不确定性。 我们的研究有三个目标。 目标1旨在将好奇心的概念操作化到基于信息鸿沟理论的界面中，并研究这些好奇心诱导界面如何影响公民和专业科学家与公民科学系统互动的方式。目标2是说明这些干预措施如何影响不同类型的人群，例如，非专家与专家志愿者，K-12和大学生，以及科学家。目标3是提高好奇心诱导界面的通用性，使其适用于多种公民科学项目，并扩展其功能，不仅诱导，而且维持，好奇心。这项研究将使用一个名为Curio的测试平台进行，这是一个通用的公民科学平台，使来自不同领域的研究人员能够创建和管理科学众包项目，这些研究人员是领域专家，但不一定精通技术或熟悉众包。 除了公民科学，这项研究的预期成果是一个新颖的，良好的特点，彻底评估的设计框架，激励参与，广泛适用于志愿者为基础的众包。

Science is increasingly data-intensive; yet, many research tasks involving the collection, annotation and analysis of data are not yet fully automated by computers. The idea of citizen science is to engage massive number of people over the Web to contribute and process scientific data, to tackle problems where the scale, spread and complexity of the data is beyond the analytic capability of algorithms or the bandwidth of small teams.  The success of these citizen science platforms is in part due to an existing population of hobbyists (e.g., birders, amateur astronomers) who are already interested and eager to help. Unfortunately, the reverse scenario is actually more common - often scientists have research data and problems that are unfamiliar to most people, and struggle to transform labor intensive data processing tasks into something that is intrinsically motivating for participants to do. The lack of mechanisms for incentivizing and sustaining participation presents a major roadblock preventing citizen science systems from widespread adoption. In this proposed research, we will tackle the issue of engagement in citizen science by investigating curiosity as a driver for participation. In particular, we will develop new interaction techniques and incentive mechanisms to support "curiosity-inducing designs" based on a well-established psychological model of curiosity - Loewenstein's information gap theory - which says that people are curious when they are made aware of the gap in their knowledge, and engage in exploratory, information-seeking behavior in order to close this information gap and resolve uncertainty.  Our research is guided by three objectives.  Objective 1 aims to operationalize the concept of curiosity into interfaces based on information gap theory, and study how these curiosity-inducing interfaces affect the way citizen and professional scientists interact with citizen science systems. Objective 2 is to characterize how these interventions affect different types of crowds, e.g., non-expert vs expert volunteers, K-12 and college students, and scientists. Objective 3 is to improve the generalizability of curiosity-inducing interfaces, making them applicable to many kinds of citizen science projects and extending their capabilities to not only induce, but sustain, curiosity. This research will be conducted using a testbed called Curio, a general purpose citizen science platform that enables researchers from diverse fields, who are domain experts but not necessarily technically savvy or familiar with crowdsourcing, to create and manage scientific crowdsourcing projects.  Beyond citizen science, the expected outcome of this research is a novel, well-characterized, thoroughly evaluated design framework for motivating participation that is broadly applicable to volunteer-based crowdsourcing.