Bivariate Effective Width Method to Improve the Normalization Capability for Subjective Speed-accuracy Biases in Rectangular-target Pointing

Shota Yamanaka, Hiroki Usuba, Homei Miyashita

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The effective width method of Fitts' law can normalize speed-accuracy biases in 1D target pointing tasks. However, in graphical user interfaces, more meaningful target shapes are rectangular. To empirically determine the best way to normalize the subjective biases, we ran remote and crowdsourced user experiments with three speed-accuracy instructions. We propose to normalize the speed-accuracy biases by applying the effective sizes to existing Fitts' law formulations including width W and height H. We call this target-size adjustment the bivariate effective width method. We found that, overall, Accot and Zhai's weighted Euclidean model using the effective width and height independently showed the best fit to the data in which the three instruction conditions were mixed (i.e., the time data measured in all instructions were analyzed with a single regression expression). Our approach enables researchers to fairly compare two or more conditions (e.g., devices, input techniques, user groups) with the normalized throughputs.

Original languageEnglish
Title of host publicationCHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450391573
DOIs
Publication statusPublished - 29 Apr 2022
Event2022 CHI Conference on Human Factors in Computing Systems, CHI 2022 - Virtual, Online, United States
Duration: 30 Apr 20225 May 2022

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2022 CHI Conference on Human Factors in Computing Systems, CHI 2022
Country/TerritoryUnited States
CityVirtual, Online
Period30/04/225/05/22

Keywords

  • Fitts' law
  • crowdsourcing
  • graphical user interface
  • human motor performance
  • pointing

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