title: Predicting dual-task performance with the Multiple Resources Questionnaire (MRQ).
abstract: OBJECTIVE
  The objective was to assess the validity of the Multiple Resources Questionnaire (MRQ) in predicting dual-task interference.
  
  
  BACKGROUND
  Subjective workload measures such as the Subjective Workload Assessment Technique (SWAT) and NASA Task Load Index are sensitive to single-task parameters and dual-task loads but have not attempted to measure workload in particular mental processes. An alternative is the MRQ.
  
  
  METHODS
  In Experiment 1, participants completed simple laboratory tasks and the MRQ after each. Interference between tasks was then correlated to three different task similarity metrics: profile similarity, based on r(2) between ratings; overlap similarity, based on summed minima; and overall demand, based on summed ratings. Experiment 2 used similar methods but more complex computer-based games.
  
  
  RESULTS
  In Experiment 1 the MRQ moderately predicted interference (r = +.37), with no significant difference between metrics. In Experiment 2 the metric effect was significant, with overlap similarity excelling in predicting interference (r = +.83). Mean ratings showed high diagnosticity in identifying specific mental processing bottlenecks.
  
  
  CONCLUSIONS
  The MRQ shows considerable promise as a cognitive-process-sensitive workload measure.
  
  
  CONCLUSIONS
  Potential applications of the MRQ include the identification of dual-processing bottlenecks as well as process overloads in single tasks, preparatory to redesign in areas such as air traffic management, advanced flight displays, and medical imaging.
authors.affiliation: Department of Psychology, P.O. Box 870348, University of Alabama, Tuscaloosa, AL 35487, USA. dboles@bama.ua.edu
authors.forename: David B
authors.initials: DB
authors.lastname: Boles
authors.forename: Jonathan H
authors.initials: JH
authors.lastname: Bursk
authors.forename: Jeffrey B
authors.initials: JB
authors.lastname: Phillips
authors.forename: Jason R
authors.initials: JR
authors.lastname: Perdelwitz
mesh_terms: D000704:Analysis of Variance; D003071:Cognition; D005163:Factor Analysis, Statistical; D006801:Humans; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012647:Self-Assessment; D011795:Surveys and Questionnaires; D013647:Task Performance and Analysis; D016526:Workload