TY - GEN
T1 - EEG-Based Neural Correlates of Trust in Human-Autonomy Interaction
AU - Wang, Min
AU - Hussein, Aya
AU - Rojas, Raul Fernandez
AU - Shafi, Kamran
AU - Abbass, Hussein A.
N1 - Funding Information:
The study was conducted after approval from UNSW Human Research Ethics Compliance Committee Protocol HC17434. This work was funded by the Australian Research Council Discovery Grant number DP160102037.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - This paper aims at identifying the neural correlates of human trust in autonomous systems using Electroencephalography (EEG) signals. Quantifying the relationship between trust and brain activities allows for real-time assessment of human trust in automation. This line of effort contributes to the design of trusted autonomous systems, and more generally, modeling the interaction in human-autonomy interaction. To study the correlates of trust, we use an investment game in which artificial agents with different levels of trustworthiness are employed. We collected EEG signals from 10 human subjects while they are playing the game; then computed three types of features from these signals considering the signal time-dependency, complexity and power spectrum using an autoregressive model (AR), sample entropy and Fourier analysis, respectively. Results of a mixed model analysis showed significant correlation ( p < 0.05) between human trust and EEG features from certain electrodes. The frontal and the occipital area are identified as the predominant brain areas correlated with trust.
AB - This paper aims at identifying the neural correlates of human trust in autonomous systems using Electroencephalography (EEG) signals. Quantifying the relationship between trust and brain activities allows for real-time assessment of human trust in automation. This line of effort contributes to the design of trusted autonomous systems, and more generally, modeling the interaction in human-autonomy interaction. To study the correlates of trust, we use an investment game in which artificial agents with different levels of trustworthiness are employed. We collected EEG signals from 10 human subjects while they are playing the game; then computed three types of features from these signals considering the signal time-dependency, complexity and power spectrum using an autoregressive model (AR), sample entropy and Fourier analysis, respectively. Results of a mixed model analysis showed significant correlation ( p < 0.05) between human trust and EEG features from certain electrodes. The frontal and the occipital area are identified as the predominant brain areas correlated with trust.
KW - EEG
KW - Human factor
KW - Human-autonomy interaction
KW - Mixed model analysis
KW - Trust
UR - https://www.mendeley.com/catalogue/e073f671-f91e-3950-bbfa-2a6047e5d56f/
UR - http://www.ieee-ssci2018.org/index.html
UR - http://www.scopus.com/inward/record.url?scp=85062789874&partnerID=8YFLogxK
U2 - 10.1109/SSCI.2018.8628649
DO - 10.1109/SSCI.2018.8628649
M3 - Conference contribution
SN - 9781538692776
T3 - Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
SP - 350
EP - 357
BT - Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
A2 - Pal, Nikhil R
A2 - Sundaram, Suresh
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - United States
T2 - 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
Y2 - 18 November 2018 through 21 November 2018
ER -