Predicting Misuse and Disuse of Combat Identification Systems

Two combat identification systems have been designed to reduce fratricide by providing soldiers with the ability to “interrogate” a potential target by sending a microwave or laser signal that, if returned, identifies the target as a “friend.” Ideally, gunners will appropriately rely on these automated aids, which will reduce fratricide rates. However, past research has found that human operators underutilize (disuse) and overly rely on (misuse) automated systems (cf. Parasuraman & Riley, 1997). The purpose of this laboratory study was to simultaneously examine misuse and disuse of an automated decision-making aid at varying levels of reliability. With or without the aid of an automated system that is correct about 90%, 75%, or 60% of the time, 91 college students viewed 226 slides of Fort Sill terrain and indicated the presence or absence of camouflaged soldiers. Regardless of the reliability of the automated aid, misuse was more prevalent than disuse, F(1, 65) = 31.43, p < .01; p = .27 for misuse, p = .13 for disuse. Results are interpreted within a general framework of automation use (Dzindolet, Beck, Pierce, & Dawe, 2001).

[1]  J. Shepperd Productivity loss in performance groups: A motivation analysis. , 1993 .

[2]  John D. Lee,et al.  Trust, self-confidence, and operators' adaptation to automation , 1994, Int. J. Hum. Comput. Stud..

[3]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[4]  Hall P. Beck,et al.  A Framework of Automation Use , 2001 .

[5]  N. Kerr,et al.  Dispensability of member effort and group motivation losses: Free-rider effects , 1983 .

[6]  A. Simpson,et al.  What is the best index of detectability? , 1973, Psychological Bulletin.

[7]  Raja Parasuraman,et al.  Automation- Induced "Complacency": Development of the Complacency-Potential Rating Scale , 1993 .

[8]  Donald D. Dorfman,et al.  Estimation of signal detection theory parameters from rating-method data: A comparison of the method of scoring and direct search* , 1973 .

[9]  N Moray,et al.  Trust, control strategies and allocation of function in human-machine systems. , 1992, Ergonomics.

[10]  Raja Parasuraman,et al.  Performance Consequences of Automation-Induced 'Complacency' , 1993 .

[11]  Larry Doton Integrating Technology to Reduce Fratricide , 1996 .

[12]  D. Dorfman,et al.  Maximum-likelihood estimation of parameters of signal-detection theory and determination of confidence intervals—Rating-method data , 1969 .

[13]  Neil A. Macmillan,et al.  Detection Theory: A User's Guide , 1991 .

[14]  Mustapha Mouloua,et al.  Automation and Human Performance : Theory and Applications , 1996 .

[15]  Arthur D. Fisk,et al.  Supporting Perception in the Service of Dynamic Decision Making , 1996, Hum. Factors.

[16]  Raja Parasuraman,et al.  Trust in Decision Aids: a Model and Its Training Implications , 1998 .

[17]  Kathleen L. Mosier,et al.  Automation Use and Automation Bias , 1999 .

[18]  N. Moray,et al.  Adaptive automation, trust, and self-confidence in fault management of time-critical tasks. , 2000, Journal of experimental psychology. Applied.

[19]  Douglas L. Hintzman,et al.  Simpson's paradox and the analysis of memory retrieval. , 1980 .

[20]  N. Macmillan,et al.  Response bias : characteristics of detection theory, threshold theory, and nonparametric indexes , 1990 .

[21]  K. Mosier,et al.  Human Decision Makers and Automated Decision Aids: Made for Each Other? , 1996 .

[22]  Raja Parasuraman,et al.  Humans and Automation: Use, Misuse, Disuse, Abuse , 1997, Hum. Factors.

[23]  J. Bliss,et al.  Reversal of the Cry-Wolf Effect: An Investigation of Two Methods to Increase Alarm Response Rates , 1994 .

[24]  David D. Woods,et al.  Systems with Human Monitors: A Signal Detection Analysis , 1985, Hum. Comput. Interact..

[25]  Raja Parasuraman,et al.  Automation-induced monitoring inefficiency: role of display location , 1997, Int. J. Hum. Comput. Stud..