Acquisition of expanding targets

ABSTRACT

There exist several user interface widgets that dynamically grow in size in response to the user's focus of attention. Some of these, such as icons in toolbars, expand to facilitate their selection - allowing for a reduced initial size in an attempt to optimize screen space use. However, selection performance may be degraded by this decreased initial widget size. We describe an experiment which explores the effects of varying parameters of expansion techniques in a selection task. Our results suggest that Fitts' law can model and predict performance in such tasks. They also indicate that performance is governed by the target's final size, not its initial one. Further, performance is dependent on the target's final size even when the target only begins expanding as late as after 90% of the movement towards the target has already been completed. These results indicate that expanding widgets can be used without sacrificing performance

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Knowledge acquisition

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.2 Design Tools and Techniques
          Subjects: User interfaces

  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.4 Systems and Software
          Subjects: Performance evaluation (efficiency and effectiveness)

General Terms:
Design, Human Factors, Performance, Theory

Keywords:
Fitts' law, empirical evaluation, expanding targets, interaction design, interaction modeling