title: Do Tele-Operators Learn to Better Judge Whether a Robot Can Pass Through an Aperture?
abstract: OBJECTIVE
  This experiment examined whether tele-operators learn to better judge a robot's ability to pass through an aperture, hereafter referred to as pass-ability judgments, and detailed the nature of such learning.
  
  
  BACKGROUND
  Jones, Johnson, and Schmidlin reported that tele-operators' pass-ability judgments did not improve over the course of their experiment, which was surprising.
  
  
  METHODS
  In each of seven blocks, tele-operators made pass-ability judgments about 10 apertures whose width varied. During each trial, participants drove the robot toward the aperture, answered yes or no to whether it could pass through that aperture, and then attempted to drive the robot through the aperture. Pass-ability judgments were analyzed in terms of percentage correct and absolute thresholds; the latter mimicked how Jones et al. analyzed their data.
  
  
  RESULTS
  Learning was revealed when judgments were analyzed in terms of percentage correct and not when analyzed in terms of absolute thresholds. Further analyses revealed that tele-operators only improved their pass-ability judgments for impassable apertures, and tele-operators' perceptual sensitivity and response bias changed over the course of the experiment.
  
  
  CONCLUSIONS
  The percentage correct-based analyses revealed that tele-operators learned to make better pass-ability judgments. Jones et al.'s decision to analyze their data in terms of absolute thresholds obscured learning.
  
  
  CONCLUSIONS
  The present results suggested that researchers should employ percentage correct when studying learning in this domain, training protocols should focus on improving tele-operators' abilities to judge the pass-ability of impassable apertures, and tele-operators truly learned to better discriminate passable and impassable apertures.
authors.affiliation: Sony Computer Entertainment America, San Mateo, California.
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Schmidlin
authors.affiliation: Texas Tech University, Lubbock, Texas keith.s.jones@ttu.edu.
authors.forename: Keith S
authors.initials: KS
authors.lastname: Jones
mesh_terms: D000293:Adolescent; D000328:Adult; D005260:Female; D006801:Humans; D007858:Learning; D008297:Male; D008328:Man-Machine Systems; D015588:Observer Variation; D012107:Research Design / Q000592:standards*; D013647:Task Performance and Analysis; D013685:Telecommunications; D055815:Young Adult
keywords: human-robot interaction; perception-action; perceptual-motor performance; tele-operation; vision