Citation

Loew A, Forster Y, Naujoks F, Biebl B, Keinath A, Bengler K. Transp. Res. F Traffic Psychol. Behav. 2022; 91: 1-16.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.trf.2022.09.020

PMID

unavailable

Abstract

Speech is considered a promising modality for human-machine interaction while driving, especially in reducing visual and manual distraction. However, speech-based user interfaces themselves have shown to increase cognitive distraction. There remains a lack of standardized and unambiguous methods for measuring the impact of speech-based assistants on cognitive distraction while driving. This work aims to investigate whether the combination of the box task and the detection response task (DRT) is a suitable method for assessing the cognitive distraction caused by speech-based assistants. For this purpose, participants (N = 39) engaged in artificial (n-back tasks) and natural speech-based secondary tasks (interaction with Android's Google Assistant and Apple's Siri) differing in predefined levels of cognitive workload while performing the box task and the DRT. The results showed that DRT performance differed between the 0-back and 1-back task but not between the different cognitive workload levels of the speech-based assistants. No clear effects emerged for the box task parameters. Thus, the combination of the box task and DRT is well-suited for measuring cognitive distraction caused by artificial secondary tasks but not by natural interactions with speech-based assistants.

Language: en

Keywords

Cognitive workload; Driver distraction; Evaluation methods; Speech-based assistants