Reducing False Alarms in Automated Target Recognition by Lowering the Level of Automation

Abstract

Automated target recognition (ATR) technologies are designed to help operators detect and identify potential threats. However, ATRs can generate a high number of false alarms (FA), resulting in low operator trust and potential automation disuse. In this study, we examined whether lowering the level of automation (LOA) of ATR could reduce FAs and support automation compliance and reliance. Participants performed a visual search for a target with or without the help of ATR. The ATR operated at three LOAs and two levels of reliability. The results suggested that the lowest LOA resulted in greater automation compliance and reliance. Operators also performed better in one measure of diagnostic accuracy under the low automation condition. The findings suggest that operating ATR at lower LOAs provide an effective method for improving ATR use and for detecting potential threats.

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References

Bliss

J.P.

(2003). Investigation of alarm-related accidents and incidents in aviation. The International Journal of Aviation Psychology, 13(3), 249-268.

Breznitz

(1983). Cry-wolf: The psychology of false alarms. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

Chapple

(2008). Automated detection and classification in high-resolution sonar imagery for autonomous underwater vehicle operations. (DSTO-GD-0537) Edinburgh, South Australia. Maritime Operations Division -DSTO.

Davies

D. R.

Parasuraman

(1982). The psychology of vigilance. London, UK: Academic Press.

Dixon

S.R.

Wickens

C.D.

(2006). Automation reliability in unmanned aerial vehicle control: A reliance-compliance model of automation dependence in high workload. Human Factors, 48(3), 474-486.

Green

D.M.

Swets

J.A.

(1966) Signal Detection Theory and Psychophysics. New York: Wiley.

Hart

S. G.

Staveland

L. E.

(1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Human Mental Workload. P. A. Hancock and N. Meshkati. Amsterdam, North Holland Press: 139-178.

Pavlovic

N.J.

Arrabito

G.R.

Myers

(2012). Increasing trust in automated target recognition for AUV minehunting. Presented at the 120th Annual Convention of the American Psychological Association, Orlando, FL. 2-5 Aug, 2012.

Jian

J.Y.

Bisantz

A. M.

Drury

C.G.

(2000). “Foundations for an empirically determined scale of trust in automated systems.” International Journal of Cognitive Ergonomics 4(1): 53-71.

10.

Kessel

R.T.

(2005). Apparent reliability: Conditions for reliance on supervised automation (TM 2005-155). Dartmouth, NS: Defence R&D Canada, Atlantic.

11.

Meyer

(2004). Conceptual issues in the study of dynamic hazard warnings. Human Factors, 46(2), 196-204.

12.

Parasuraman

Hancock

P.A.

(1999). Using signal detection theory and Bayesian analysis to design parameters for automated warning systems. In Scerbo

M.W.

Mouloua

(Eds.), Automation technology and human performance: Current research and trends (pp. 63-67). Mahwah, NJ: Lawrence Erlbaum Associates Inc.

13.

Parasuraman

Sheridan

T.B.

Wickens

C.D.

(2000). A model for types and levels of human interaction with automation. IEEE Transaction on Systems, Man, and Cybernetics- Part A: Systems and Humans, 30(3), 286-297.

14.

Szalma

J.L.

Hancock

P.A.

Warm

J.S.

Dember

W.N.

Parsons

K.S.

(2006). Training for vigilance: Using predictive power to evaluate feedback effectiveness. Human Factors, 48(4), 682-692.

15.

Rice

(2009). Examining single- and multiple-process theories of trust in automation. The Journal of General Psychology, 136(3), 303-319.