Refining Fitts' law models for bivariate pointing

  • Authors:
  • Johnny Accot;Shumin Zhai

  • Affiliations:
  • IBM Almaden Research Center, San Jose, CA;IBM Almaden Research Center, San Jose, CA

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2003

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Abstract

We investigate bivariate pointing in light of the recent progress in the modeling of univariate pointing. Unlike previous studies, we focus on the effect of target shape (width and height ratio) on pointing performance, particularly when such a ratio is between 1 and 2. Results showed unequal impact of amplitude and directional constraints, with the former dominating the latter. Investigating models based on the notion of weighted Lp norm, we found that our empirical findings were best captured by an Euclidean model with one free weight. This model significantly outperforms the best model to date.