Investigating the effectiveness of mental workload as a predictor of opportune moments for interruption

Shamsi T. Iqbal, Brian P. Bailey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This work investigates the use of workload-aligned task models for predicting opportune moments for interruption. From models for several tasks, we selected boundaries with the lowest (Best) and highest (Worst) mental workload. We compared effects of interrupting primary tasks at these and Random moments on resumption lag, annoyance, and social attribution. Results show that interrupting tasks at predicted Best moments consistently caused less resumption lag and annoyance, and fostered more social attribution. Thus, the use of workload-aligned task models offers a systematic method for predicting opportune moments for interruption.

Original languageEnglish (US)
Title of host publicationCHI'05 Extended Abstracts on Human Factors in Computing Systems, CHI EA'05
Pages1489-1492
Number of pages4
DOIs
StatePublished - 2005
EventConference on Human Factors in Computing Systems, CHI EA 2005 - Portland, OR, United States
Duration: Apr 2 2005Apr 7 2005

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Other

OtherConference on Human Factors in Computing Systems, CHI EA 2005
Country/TerritoryUnited States
CityPortland, OR
Period4/2/054/7/05

Keywords

  • Attention
  • Interruption
  • Mental Workload
  • Task Models

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

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