Title

What To Automate: Addressing The Multidimensionality Of Cognitive Resources Through System Design

Keywords

adaptive automation; automation; human automation interaction; human system integration; information processing; level of automation; stress; teleoperation; type of automation; workload

Abstract

The implementation of automation relies on the assumption that automation will reduce the operator's cognitive demand and improve performance. However, accepted models demonstrate the multidimensionality of cognitive resources, suggesting that automation must support an appropriate resource dimension to have an appreciable effect. To evaluate this theory, the present study examined the impact of various types of automation on an unmanned ground vehicle (UGV) operator's performance, workload, and stress. The use of a visually demanding task allowed for comparison between an auditory alert (supporting the heavily burdened visual dimension) and a driving aid (supporting action execution, a relatively unburdened cognitive dimension). Static and adaptive (fluctuating based on task demand) levels were implemented for each automation type. Those receiving auditory alerts exhibited better performance and reduced Worry, but also increased Temporal Demand and Effort relative to those receiving driving automation. Adaptive automation reduced workload for those receiving the auditory alerts, and increased workload for those receiving the driving automation. The results from this research demonstrate the need to consider the multidimensionality of the operator's cognitive resources when implementing automation into a system. System designers should consider the type of automation necessary to support the specific cognitive resources burdened by the task. © 2013, Human Factors and Ergonomics Society.

Publication Date

12-1-2013

Publication Title

Journal of Cognitive Engineering and Decision Making

Volume

7

Issue

4

Number of Pages

311-329

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1177/1555343413495396

Socpus ID

84887948971 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84887948971

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