Age-specific predictive models of human performance

  • Authors:
  • Shari Trewin;Bonnie John;John Richards;David Sloan;Vicki Hanson;Rachel Bellamy;John Thomas;Calvin Swart

  • Affiliations:
  • IBM T.J. Watson Research Center, Yorktown Heights, New York, USA;IBM T. J. Watson Research Center, Yorktown Heights, New York, USA;IBM T.J. Watson Research Center & University of Dundee, Yorktown Heights, New York, USA;University of Dundee, Dundee, UK;University of Dundee, Dundee, UK;IBM T. J. Watson Research Center, Yorktown Heights, New York, USA;IBM T. J. Watson Research, Yorktown Heights, New York, USA;IBM T. J. Watson Research Center, Yorktown Heights, New York, USA

  • Venue:
  • CHI '12 Extended Abstracts on Human Factors in Computing Systems
  • Year:
  • 2012

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Abstract

Designers often struggle to create interfaces that are optimal for both younger and older adults, as they may interact differently with the same interface. Human-performance models have been used to aid designers in evaluating the efficiency of user interfaces. Can we create age-specific models to help designers create interfaces that are efficient for all age groups? We modeled a target acquisition task using published younger and older person parameters. While the younger model's mean prediction matches younger human data well (within 3.2%), the older model overestimates older users' mean task times by 34.6%. Further work should explore the influence of device type and the role of error-avoidance on parameter values for models of older adult interactions with technology.