%0 Conference Paper %1 conf/automotiveUI/ChuangGK17 %A Chuang, Lewis L. %A Glatz, Christiane %A Krupenia, Stas S. %B Proceedings of the International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) %D 2017 %E Boll, Susanne %E Pfleging, Bastian %E Donmez, Birsen %E Politis, Ioannis %E Large, David R. %I ACM %K 2017 C06 sfbtrr161 %P 123-133 %R 10.1145/3122986.3123017 %T Using EEG to Understand why Behavior to Auditory In-vehicle Notifications Differs Across Test Environments %U https://doi.org/10.1145/3122986.3123017 %X In this study, we employ EEG methods to clarify why auditory notifications, which were designed for task management in highly automated trucks, resulted in different performance behavior, when deployed in two different test settings: (a) student volunteers in a lab environment, (b) professional truck drivers in a realistic vehicle simulator. Behavioral data showed that professional drivers were slower and less sensitive in identifying notifications compared to their counterparts. Such differences can be difficult to interpret and frustrates the deployment of implementations from the laboratory to more realistic settings. Our EEG recordings of brain activity reveal that these differences were not due to differences in the detection and recognition of the notifications. Instead, it was due to differences in EEG activity associated with response generation. Thus, we show how measuring brain activity can deliver insights into how notifications are processed, at a finer granularity than can be afforded by behavior alone. %@ 978-1-4503-5150-8

@inproceedings{conf/automotiveUI/ChuangGK17, abstract = {In this study, we employ EEG methods to clarify why auditory notifications, which were designed for task management in highly automated trucks, resulted in different performance behavior, when deployed in two different test settings: (a) student volunteers in a lab environment, (b) professional truck drivers in a realistic vehicle simulator. Behavioral data showed that professional drivers were slower and less sensitive in identifying notifications compared to their counterparts. Such differences can be difficult to interpret and frustrates the deployment of implementations from the laboratory to more realistic settings. Our EEG recordings of brain activity reveal that these differences were not due to differences in the detection and recognition of the notifications. Instead, it was due to differences in EEG activity associated with response generation. Thus, we show how measuring brain activity can deliver insights into how notifications are processed, at a finer granularity than can be afforded by behavior alone.}, added-at = {2020-03-06T13:30:07.000+0100}, author = {Chuang, Lewis L. and Glatz, Christiane and Krupenia, Stas S.}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2d1faed8c0daf305c407a9a517b3ee583/leonkokkoliadis}, booktitle = {Proceedings of the International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI)}, doi = {10.1145/3122986.3123017}, editor = {Boll, Susanne and Pfleging, Bastian and Donmez, Birsen and Politis, Ioannis and Large, David R.}, ee = {https://doi.org/10.1145/3122986.3123017}, interhash = {cffb6faa1564c8d472bca30815a7c633}, intrahash = {d1faed8c0daf305c407a9a517b3ee583}, isbn = {978-1-4503-5150-8}, keywords = {2017 C06 sfbtrr161}, pages = {123-133}, publisher = {ACM}, timestamp = {2020-03-06T12:30:07.000+0100}, title = {Using EEG to Understand why Behavior to Auditory In-vehicle Notifications Differs Across Test Environments}, url = {https://doi.org/10.1145/3122986.3123017}, year = 2017 }