Sage Journals: Discover world-class research

Abstract

A long-standing question in cognitive engineering is the effect of computer aids on work skills. We examined the performance of human operators in a discrete process control environment. During an extended training period, humans controlled production under one of four different conditions (manual, hybrid, supervisory, and automatic) that varied the degree of in-the-loop control. After a training period all subjects transferred to a supervisory condition with two scheduling logic errors to compensate for. Training results showed that production was best overall with supervisory control, intermediate with hybrid control, and worst with manual control. Supervisory performance did not differ across the two scheduling aids. When scheduling logic errors were introduced, subjects who had trained in the automatic condition (simply observing) took longer to respond and let production drop most. Overall, performance depends on an interplay between (1) ability to identify goal-relevant cues and (2) ability to connect cues to action. Moreover, whether out-of-the-loop unfamiliarity is seen depends on how tight the “windows of opportunity” for action are in the domain.

Get full access to this article

View all access options for this article.

References

Brunswik

(1943). Organismic achievement and environmental probability. Psychological Review, 50, 255–272

Curry

Ephrath

(1977). Monitoring and control of unreliable systems. In Sheridan

T.B.

Johannsen

(Eds.), Monitoring behavior and supervisory control. New York: Plenum

Dawes

R.M.

(1975). The mind, the model, and the task. In Restle

Shiffrin

R.M.

Castellan

N.J.

Lindman

H.R.

Pisoni

D.P.

(Eds.), Cognitive Theory, 1. Hillsdale, NJ: LEA

Dunkler

Mitchell

Govindaraj

Ammons

(1988). The effectiveness of supervisory control strategies in scheduling flexible manufacturing systems. IEEE Transactions on Systems, Man, and Cybernetics, SMC-18, 223–237

Endsley

Kiris

(1995). The out-of-the-loop performance problem and level of control in automation. Human Factors, 37, 381–394

Hughes

(1989) Glass cockpit study reveals human factors problems. Aviation Week and Space Technology, August 7, 32–36

Hwang

S.-L.

Salvendy

(1988). Operator performance and subjective response in control of flexible manufacturing systems. Work and Stress, 2, 27–39

Kessell

Wickens

C.D.

(1982). The transfer of failure detection skills between monitoring and controlling dynamic systems. Human Factors, 24, 49–60

Kirlik

Miller

R.A.

Jagacinski

R.J.

(1993). Supervisory control in a dynamic and uncertain environment: A process model of skilled human-environment interaction. IEEE Transactions on Systems, Man, & CyberneticsI, I23I, 929

10.

Moray

(1986). Monitoring behavior and supervisory control In Boff

(Ed.), Handbook of perception and human performance. New York: Wiley

11.

Sanderson

P. M.

(1989). The human planning and scheduling role in advanced manufacturing systems: An emerging human factors domain. Human Factors, 31, 635–664

12.

Sharit

(1985). Supervisory control of a flexible manufacturimg system. Human Factors, 27, 47–59

13.

Sheridan

Ferrell

(1974). Man-machine systems Cambridge, MA: MIT Press

14.

Sheridan

T.B.

(1987). Supervisory control. In Salvendy

(Ed.), Handbook of Human Factors. New York: Wiley

15.

Shiff

(1983). An experimental study of the human-computer interface in process control. Unpublished thesis, University of Toronto, Department of Industrial Engineering, Toronto, Canada

16.

Shneiderman

(1988). Designing the user interface. New York: Addison Wesley

17.

Wickens

C.D.

(1992). Engineering psychology and human performance. New York: Harper-Collins

18.

Wiener

(1988). Cockpit automation. In Eiener

E.L.

Nagel

D.C.

(Eds.), Human factors in aviation. San Diego: Academic Press

In and Out of the Loop: Strategic Skill and Windows of Opportunity in Discrete Process Control

Abstract

Get full access to this article

References