Abstract
This paper presents methods for improving the attention span of workers in tasks that heavily rely on their attention to the occurrence of rare events. The underlying idea in our approach is to dynamically augment the task with some dummy (artificial) events at different times throughout the task, rewarding the worker upon identifying and reporting them. The proposed approach is an alternative to the traditional approach of exclusively relying on rewarding the worker for successfully identifying the event of interest itself. We propose three methods for timing the dummy events throughout the task. Two of these methods are static and determine the timing of the dummy events at random or uniformly throughout the task. The third method is dynamic and uses the identification (or misidentification) of dummy events as a signal for the worker's attention to the task, adjusting the rate of dummy events generation accordingly.
Highlights
The past decade has seen significant growth in crowdsourcing applications
We focus on simple tasks that require moderate, yet continuous, attention on the workers side, with a very low cognitive load. Examples for such tasks include watching suitcases passing through an X-ray machine with the aim of detecting sharp objects or explosives, and watching streams arriving from Closed Circuit Televisions (CCTVs) with the aim of identifying crime
In this paper we present a method for overcoming the degradation in workers’ attention span over time in monitoring tasks
Summary
The past decade has seen significant growth in crowdsourcing applications. The crowdsourcing model encapsulates a key question that has captured the attention of researchers for a long time– How to motivate workers so they perform higher quality work. Some works examine the use of different financial compensation mechanisms which are based on the workers’ performance [Yin and Chen, 2015; Mason and Watts, 2010; Gao et al, 2012; Feng et al, 2014]. Another line of work [Law et al, 2016; Kaufmann et al, 2011] suggests the use of intrinsic behavioral factors to motivate workers. We focus on simple tasks that require moderate, yet continuous, attention on the workers side, with a very low cognitive load. Common to all the above examples, that the work is highly monotonous and normally workers’ attention degrade with time [Rahman, 2012]
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