You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
How to take multiple factors into account when evaluating a Game with a Purpose? How is player behaviour or participation influenced by different incentives? How does player engagement impact their accuracy in solving tasks? In this paper, we present a detailed investigation of multiple factors affecting the evaluation of a GWAP and we show how they impact on the achieved results. We inform our study with the experimental assessment of a GWAP designed to solve a multinomial classification task.

Cited Abstract(s):
[1]: Image classification is a classical task heavily studied in computer vision and widely required in many concrete scientific and industrial scenarios. Is it better to rely on human eyes, thus asking people to classify pictures, or to train a machine learning system to automatically solve the task? The answer largely depends on the specific case and the required accuracy: humans may be more reliable - especially if they are domain experts - but automatic processing can be cheaper, even if less capable to demonstrate an "intelligent" behaviour. In this paper, we present an experimental comparison of different Human Computation and Machine Learning approaches to solve the same image classification task on a set of pictures used in light pollution research. We illustrate the adopted methods and the obtained results and we compare and contrast them in order to come up with a long term combined strategy to address the specific issue at scale: while it is hard to ensure a long-term engagement of users to exclusively rely on the Human Computation approach, the human classification is indispensable to overcome the "cold start" problem of automated data modelling.

[2]: Paid microtask crowdsourcing has traditionally been approached as an individual activity, with units of work created and completed independently by the members of the crowd. Other forms of crowdsourcing have, however, embraced more varied models, which allow for a greater level of participant interaction and collaboration. This article studies the feasibility and uptake of such an approach in the context of paid microtasks. Specifically, we compare engagement, task output, and task accuracy in a paired-worker model with the traditional, single-worker version. Our experiments indicate that collaboration leads to better accuracy and more output, which, in turn, translates into lower costs. We then explore the role of the social flow and social pressure generated by collaborating partners as sources of incentives for improved performance. We utilise a Bayesian method in conjunction with interface interaction behaviours to detect when one of the workers in a pair tries to exit the task. Upon this realisation, the other worker is presented with the opportunity to contact the exiting partner to stay: either for personal financial reasons (i.e., they have not completed enough tasks to qualify for a payment) or for fun (i.e., they are enjoying the task). The findings reveal that: (1) these socially motivated incentives can act as furtherance mechanisms to help workers attain and exceed their task requirements and produce better results than baseline collaborations; (2) microtask crowd workers are empathic (as opposed to selfish) agents, willing to go the extra mile to help their partners get paid; and, (3) social furtherance incentives create a win-win scenario for the requester and for the workers by helping more workers get paid by re-engaging them before they drop out.

[3]: Crowdsourcing via paid microtasks has been successfully applied in a plethora of domains and tasks. Previous efforts for making such crowdsourcing more effective have considered aspects as diverse as task and workflow design, spam detection, quality control, and pricing models. Our work expands upon such efforts by examining the potential of adding gamification to microtask interfaces as a means of improving both worker engagement and effectiveness. We run a series of experiments in image labeling, one of the most common use cases for microtask crowdsourcing, and analyse worker behavior in terms of number of images completed, quality of annotations compared against a gold standard, and response to financial and game-specific rewards. Each experiment studies these parameters in two settings: one based on a state-of-the-art, non-gamified task on CrowdFlower and another one using an alternative interface incorporating several game elements. Our findings show that gamification leads to better accuracy and lower costs than conventional approaches that use only monetary incentives. In addition, it seems to make paid microtask work more rewarding and engaging, especially when sociality features are introduced. Following these initial insights, we define a predictive model for estimating the most appropriate incentives for individual workers, based on their previous contributions. This allows us to build a personalised game experience, with gains seen on the volume and quality of work completed.

[4]: Intrinsic and extrinsic types of motivation have been widely studied, and the distinction between them has shed important light on both developmental and educational practices. In this review we revisit the classic definitions of intrinsic and extrinsic motivation in light of contemporary research and theory. Intrinsic motivation remains an important construct, reflecting the natural human propensity to learn and assimilate. However, extrinsic motivation is argued to vary considerably in its relative autonomy and thus can either reflect external control or true self-regulation. The relations of both classes of motives to basic human needs for autonomy, competence and relatedness are discussed. ≈ 2000 Academic Press To be motivated means to be moved to do something. A person who feels no impetus or inspiration to act is thus characterized as unmotivated, whereas someone who is energized or activated toward an end is considered motivated. Most everyone who works or plays with others is, accordingly, concerned with motivation, facing the question of how much motivation those others, or oneself, has for a task, and practitioners of all types face the perennial task of fostering more versus less motivation in those around them. Most theories of motivation reflect these concerns by viewing motivation as a unitary phenomenon, one that varies from very little motivation to act to a great deal of it. Yet, even brief reflection suggests that motivation is hardly a unitary phenomenon. People have not only different amounts, but also different kinds of motivation. That is, they vary not only in level of motivation (i.e., how much motivation), but also in the orientation of that motivation (i.e., what type of motivation). Orientation of motivation concerns the underlying attitudes and goals that give rise to action—that is, it concerns the why of actions. As an example, a student can be highly motivated to do homework out of curiosity and interest or, alternatively, because he or she wants to procure the approval of a teacher or parent. A student could be motivated

[5]: Virtual citizen science (VCS) projects enable new forms of scientific research using crowdsourcing and human computation to gather and analyse large-scale datasets. To attract and sustain the number of participants and levels of participation necessary to achieve research aims, some VCS projects have introduced game elements such as competitions to tasks. However, we still know very little about how some game elements, particularly competitions, influence participation rates. To investigate the impact of game elements on player engagement, we conducted a two-part mixed-methods study of EyeWire, a VCS game. First, we interviewed EyeWire designers to understand their rationale for introducing competitions. Guided by their answers, we analysed two datasets of EyeWire user task contributions and chat logs to assess the effectiveness of competitions in achieving designers' goals. Our findings contribute to the growing understanding of how competitions influence participant activity in human computation initiatives and socio-technical systems such as VCS.

[6]: Human computation can address complex computational problems by tapping into large resource pools for relatively little cost. Two prominent human-computation techniques - games with a purpose (GWAP) and microtask crowdsourcing - can help resolve semantic-technology-related tasks, including knowledge representation, ontology alignment, and semantic annotation. To evaluate which approach is better with respect to costs and benefits, the authors employ categorization challenges in Wikipedia to ultimately create a large, general-purpose ontology. They first use the OntoPronto GWAP, then replicate its problem-solving setting in Amazon Mechanical Turk, using a similar task-design structure, evaluation mechanisms, and input data.

[7]: 

[8]:
Participation incentives are usually classified as intrinsic or extrinsic motivation [4] . Some comparative analysis of incentives exists for GWAPs [8] , especially in contrast to different methods like micro-working [6] [3] [2] or machine learning [1] . The effect of competition and tangible rewards on participation and quality of results has also been explored, both in the context of GWAPs [7] and online citizen science campaigns [5] , revealing the pros and cons of designing different motivation mechanisms.