Publications
カンファレンス (国際) Better Definition and Calculation of Throughput and Effective Parameters for Steering to Account for Subjective Speed-accuracy Tradeoffs
Nobuhito Kasahara (Meiji University), Yosuke Oba (Meiji University), Shota Yamanaka, Anil Ufuk Batmaz (Concordia University), Wolfgang Stuerzlinger (Simon Fraser University), Homei Miyashita (Meiji University)
The ACM CHI conference on Human Factors in Computing Systems (CHI 2024)
2024.5.11
In Fitts' law studies to investigate pointing, throughput is used to characterize the performance of input devices and users, which is claimed to be independent of task difficulty or the user's subjective speed-accuracy bias. While throughput has been recognized as a useful metric for target-pointing tasks, the corresponding formulation for path-steering tasks and its evaluation have not been thoroughly examined in the past. In this paper, we conducted three experiments using linear, circular, and sine-wave path shapes to propose and investigate a novel formulation for the effective parameters and the throughput of steering tasks. Our results show that the effective width substantially improves the fit to data with mixed speed-accuracy biases for all task shapes. Effective width also smoothed out the throughput across all biases, while the usefulness of the effective amplitude depended on the task shape. Our study thus advances the understanding of user performance in trajectory-based tasks.
Paper :
Better Definition and Calculation of Throughput and Effective Parameters for Steering to Account for Subjective Speed-accuracy Tradeoffs
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