title: Automation usage decisions: controlling intent and appraisal errors in a target detection task.
abstract: BACKGROUND
  It was proposed that misuse and disuse often occur because operators (a) cannot determine if automation or a nonautomated alternative maximizes the likelihood of task success (appraisal errors) or (b) know the utilities of the options but disregard this information when deciding to use automation (intent errors).
  
  
  OBJECTIVE
  This investigation assessed the effectiveness of performance feedback, a procedure developed to attenuate appraisal errors, and scenario training, an intervention designed to decrease intent errors.
  
  
  METHODS
  Operators given feedback were told how many errors they and an automated device made on a target detection task. Scenario training took operators through the thought processes of optimal decision makers after the utilities of the automated and nonautomated alternatives had been determined. Following 200 training trials, participants chose between relying on their observations or an automated device.
  
  
  RESULTS
  There was little misuse, but disuse rates were high (84%) among operators receiving neither feedback nor scenario training. Operators paired with a more accurate machine and given feedback made approximately twice as many errors as the automated device. Nevertheless, intent errors were commonplace; 55% of the operators who received feedback without scenario training did not rely on automation. Feedback effectiveness was enhanced when used in conjunction with scenario training; the disuse rate decreased to 29%.
  
  
  CONCLUSIONS
  A combination of feedback and scenario training was more effective in mitigating disuse than either intervention used in isolation.
  
  
  CONCLUSIONS
  An important application of these findings is that operator training programs should incorporate techniques to control intent and appraisal errors.
authors.affiliation: Psychology Department, Appalachian State University, Boone, NC 28608, USA. beckhp@appstate.edu
authors.forename: Hall R
authors.initials: HR
authors.lastname: Beck
authors.forename: Mary T
authors.initials: MT
authors.lastname: Dzindolet
authors.forename: Linda G
authors.initials: LG
authors.lastname: Pierce
mesh_terms: D001331:Automation; D003657:Decision Making; D005260:Female; D006801:Humans; D033182:Intention; D007600:Judgment; D007858:Learning; D008297:Male; D008328:Man-Machine Systems; D011340:Problem Solving; D013647:Task Performance and Analysis