A connectionist model of complacency and adaptive recovery under automation.
Journal of Experimental Psychology Learning Memory and CognitionPublished 1 January 2000
Simon Farrell, Stephan Lewandowsky
Citations43
SJR quartileQ1
SJR score1.23
SNIP1.24
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TL;DR
A computational model of human performance under automation based on connectionist principles is presented and the model is shown to explain several benchmark findings, among them the basic complacency effect; the effect of the variability of automation reliability oncomplacency; the effects of task complexity; and theeffect of intermittently returning control to the operator.
Abstract
Automation is intended to reduce the demands on operators in complex environments, thereby enhancing overall system performance. Although automation usually reduces workload, it is often accompanied by a decline in monitoring performance, an effect known as complacency. The circumstances under which complacency occurs and how it can be prevented, for example by intermittently returning control to the operator, are empirically well understood. To date, that empirical knowledge has not been accompanied by strong psychological theory. This article presents a computational model of human performance under automation based on connectionist principles. The model is shown to explain several benchmark findings, among them the basic complacency effect; the effect of the variability of automation reliability on complacency; the effect of task complexity; and the effect of intermittently returning control to the operator.
Keywords
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