Target Acquisition and the Crowd Actor

Abstract

Work in human-computer interaction has generally assumed either a single user or a group of users working together in a shared virtual space. Recent crowd-powered systems use a different model in which a dynamic group of individuals (the crowd) collectively form a single actor that responds to real-time performance tasks, e.g., controlling an on-screen character, driving a robot, or operating an existing desktop interface. In this paper, we introduce the idea of the crowd actor as a way to model coordination strategies and resulting collective performance, and discuss how the crowd actor is influenced not only by the domain on which it is asked to operate but also by the personality endowed to it by algorithms used to combine the inputs of constituent participants. Nowhere is the focus on the individual performer more finely resolved than in the study of the human psychomotor system, a mainstay topic in psychology that, largely owing to Fitts' law, also has a legacy in HCI. Therefore, we explored our notion of a crowd actor by modeling the crowd as a individual motor system performing pointing tasks. We combined the input of 200 participants in a controlled offline experiment to demonstrate the inherent trade-offs between speed and errors based on personality, the number of constituent individuals, and the mechanism used to distribute work across the group. Finally, 10 workers participated in a synchronous experiment to explore how the crowd actor responds in a real online setting. This work contributes to the beginning of a predictive science for the general crowd actor model.