Using EEG to understand why behavior to auditory in-vehicle notifications 
differs across test environments

Chuang, LL; Glatz, C; Krupenia, S

doi:10.1145/3122986.3123017

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Using EEG to understand why behavior to auditory in-vehicle notifications differs across test environments

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Chuang, LL
Project group: Cognition & Control in Human-Machine Systems, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Glatz, C
Project group: Motion Perception & Simulation, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Project group: Cognition & Control in Human-Machine Systems, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Chuang, L., Glatz, C., & Krupenia, S. (2017). Using EEG to understand why behavior to auditory in-vehicle notifications differs across test environments. In S. Boll, B. Pfleging, B. Donmez, I. Politis, & D. Large (Eds.), 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '17) (pp. 123-133). New York, NY, USA: ACM Press.

Cite as: https://hdl.handle.net/21.11116/0000-0000-C391-F

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.