Neurological Measurement of Human Trust in Automation Using Electroencephalogram
الموضوعات :
seeung oh
1
,
Younho Seong
2
,
Sun Yi
3
,
Sangsung Park
4
1 - Industrial and Systems Engineering, North Carolina A&T state University, USA.
2 - Industrial and Systems Engineering, North Carolina A&T state University, USA.
3 - Mechanical Engineering, North Carolina A&T state University, USA.
4 - Department of Big Data and Statistics, Cheongju University, Chungbuk, South Korea
الکلمات المفتاحية: Trust, Automation, mistrust, electroencephalogram (EEG), power spectrum,
ملخص المقالة :
In modern society, automation is complex enough to perform advanced tasks. The role of the human operator to control and monitor complex automations is crucial to avoid failure, to reduce risk, and to prevent unpredictable situations. Measuring the human operators’ level of trust is crucial in predicting their acceptance and reliance on the automation. This research used an electroencephalogram (EEG) as a neurological measure to identify specific brainwaves in situations of trust and mistrust in automation. Power spectrum analysis was used for brainwave analysis. The results indicate that the power of alpha and beta waves was stronger for the trust situation; whereas, the power of gamma waves was stronger for the mistrust situation. When the level of human trust in automation increases, the use of an automatic control increases. Therefore, the findings of this research will contribute to defining how trust in automation affects the human operator’s monitoring, decision-making, and overall performance.
[1] Arana-Llanes, JY., González-Serna, G., Pineda-Tapia, R., Olivares-Peregrino, V., Ricarte-Trives, JJ. and Latorre-Postigo, JM., 2018, EEG lecture on recommended activities for the induction of attention and concentration mental states on e-learning students. Journal of Intelligent & Fuzzy Systems, Jan 1,34(5), 3359-71.
[2] Barber, B., 1983, The Logic and Limits of Trust. New Brunswick, Rutgers University Press.
[3] Bhattacharya, R., Devinney, M. and Pillutla, M., 1998, A formal model of trust based on outcomes. Academy of management review, 23(3),459-472.
[4] [Bisantz, A. and Seong, Y., 2001, Assessment of operator trust in and utilization of automated decision-aids under different framing conditions. International Journal of Industrial Ergonomics, 28(2), 85-97.
[5] Bisantz, A., Llinas, J., Seong, Y., Finger, R. and Jian, Y., 2000, Empirical Investigations of Trust-Related Systems Vulnerabilities in Aided, Adversarial Decision Making. STATE UNIV OF NEW YORK AT BUFFALO CENTER OF MULTISOURCE INFORMATION FUSION.
[6] Casado-Aranda, LA., Dimoka, A. and Sánchez-Fernández, J., 2019, Consumer processing of online trust signals: a neuroimaging study. Journal of Interactive Marketing, 1(47), 159-80.
[7] Cohen, X. and Donner, H., 2013, Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior. Journal of Neurophysiology, 110(12), 2752-2763.
[8] Cohen, X., Elger, E. and Fell, J., 2008, Oscillatory activity and phase–amplitude coupling in the human medial frontal cortex during decision making. Journal of cognitive neuroscience, 21(2), 390-402.
[9] Deutsch, M., 1997, The resolution of conflict: Constructive and destructive processes. Yale University Press.
[10] Dimoka, A., 2011, Brain mapping of psychological processes with psychometric scales: An fMRI method for social neuroscience. NeuroImage, 54, S263-S271.
[11] Dong, Y.,Kim, K., Lee, K. and Lee, Y., 2015, A preliminary study on human trust measurements by EEG for human-machine interactions. In Proceedings of the 3rd International Conference on Human-Agent Interaction, 265-268.
[12] Ferrez, W. and Millán, D., 2005, You Are Wrong!---Automatic Detection of Interaction Errors from Brain Waves. In Proceedings of the 19th international joint conference on Artificial intelligence.
[13] Gianotti, R., Knoch, D., Faber, L., Lehmann, D., Pascual-Marqui, D., Diezi, C. ... and Fehr, E., 2009, Tonic activity level in the right prefrontal cortex predicts individuals' risk taking. Psychological science, 20(1), 33-38.
[14] Hirshfield, M., Hirshfield, H., Hincks, S., Russell, M., Ward, R. and Williams, T., 2011, Trust in human-computer interactions as measured by frustration, surprise, and workload. In International Conference on Foundations of Augmented Cognition, 507-516.
[15] Huang, CH. and Lin, PJ., 2019, Effects of Symbol Component on the Identifying of Graphic Symbols from EEG for Young Children with and without Developmental Delays. Applied Sciences, Jan 9(6), 1260.
[16] Jabbari, M.M. & Tohidi, H. (2012). Role of human aspects in project management, Procedia - Social and Behavioral Sciences, 31, 837-840, [17]Jabbari, M.M. & Tohidi, H. (2012). The Necessity of Using CRM, Procedia Technology, 1, 514-516. https://doi.org/10.1016/j.protcy.2012.02.110.
[17] Jian, Y., Bisantz, M. and Drury, G., 2000, Foundations for an Empirically Determined Scale of Trust in Automated Systems. International journal of cognitive ergonomics, 4(1), 53-71.
[18] Jobdas, Rebakah., Ashish, Jani. and Rupesh, Vyas., 2018, BCI in classroom, system for assessing dynamic state of cognitive performance using EEG signals. International Journal of Research in Engineering and Innovation, 2(2), 195-200.
[19] Juan, Roselia., 2019, Electrical Brain Activity and The Examinee's Level of Effort During Performance Validity Tasks. Stephen F. Austin State University.
[20] Klem, H., Luders, O., Jasper, H. and Elger, C., 1999, The ten–twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology, 52, 3–6.
[21] Kumar, A., Gao, L., Pirogova, E. and Fang, Q., 2019, A Review of Error-Related Potential-Based Brain–Computer Interfaces for Motor Impaired People. IEEE Access, Sep 26(7), 142451-66.
[22] Larzelere, E. and Huston, L., 1980, The dyadic trust scale: Toward understanding interpersonal trust in close relationships/ Journal of Marriage and the Family, 595-604.
[23] Lee, D. and Moray, N., 1994, Trust, self-confidence, and operators' adaptation to automation. International Journal of Human-Computer Studies, 40, 153-184.
[24] Lee, D. and See, A., 2004, Trust in technology: Designing for appropriate reliance. Human Factors, 46, 50–80.
[25] Light, G. A., Williams, L. E., Minow, F., Sprock, J., Rissling, A., Sharp, R. … and Braff, D. L., 2010, Electroencephalography (EEG) and event-related potentials (ERPs) with human participants, Current protocols in neuroscience, Chapter 6, Unit–6.25.24.
[26] Liu, M., Zhang, J., Jia, W., Chang, Q., Shan, S., Hu, Y. and Wang, D., 2019, Enhanced Executive Attention Efficiency after Adaptive Force Control Training: Behavioural and Physiological Results. Behavioural brain research, Mar 18.
[27] Masalonis, J. and Parasuraman, R., 1999, Trust as a construct for evaluation of automated aids: Past and future theory and research. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 43(3), 184-187.
[28] Mayer, C., Davis, H. and Schoorman, D., 1995, An integrative model of organizational trust, Academy of Management Review, 20, 709–734.
[29] Minguillon, J., Lopez-Gordo, A. and Pelayo, F., 2016, Stress assessment by prefrontal relative gamma, Frontiers in computational neuroscience, 10, 101.
[30] Muir, M., 1987, Trust between humans and machines, and the design of decision aids, International Journal of Man-Machine Studies, 27(5-6), 527-539.
[31] Nomura, T., Higuchi, K., Yu, H., SASAKI, I., Kimura, S. and Itoh, H., 2006, Slow‐wave photic stimulation relieves patient discomfort during esophagogastroduodenoscopy. Journal of gastroenterology and hepatology, 21(1), 54-58.
[32] Ota, K., Shinya, M. and Kudo, K., 2019, Transcranial direct current stimulation over dorsolateral prefrontal cortex modulates risk-attitude in motor decision-making. Frontiers in human neuroscience, 13, 297.
[33] Posada-Quintero, HF., Reljin, N., Bolkhovsky, J., Orjuela-Cañón, AD. and Chon, K., 2019, Brain Activity Correlates With Cognitive Performance Deterioration During Sleep Deprivation. Frontiers in neuroscience, 13, 1001.
[34] Prestel, M., Riedl, R., Stark, R. and Ott, U., 2019, Enhancing Mindfulness by Combining Neurofeedback with Meditation. Journal of Consciousness Studies, Jan 1-26(7-8), 268-93.
[35] Rajendran, A., Sudeshraj, R. and Sureshkumar, S., 2019, Phytonutrients: Stress and relaxation dietary health food supplements. The Pharma Innovation Journal, 8(5), 799-802.
[36] Rempel, K., Holmes, G. and Zanna, P., 1985, Trust in close relationships. Journal of Personality and Social Psychology, 49, 95-112.
[37] Sacré, P., Kerr, MS., Subramanian, S., Fitzgerald, Z., Kahn, K., Johnson, MA., Niebur, E., Eden, UT., González-Martínez, JA., Gale, JT. and Sarma, SV., 2019, Risk-taking bias in human decision-making is encoded via a right–left brain push–pull system, Proceedings of the National Academy of Sciences, Jan 22-116(4), 1404-13.
[38] Tang, YY., Tang, R., Rothbart, MK. and Posner, MI., 2019, Frontal theta activity and white matter plasticity following mindfulness meditation. Current opinion in psychology, Apr 18.
[39] Teplan, M., Krakovska, A. and Štolc, S., 2006, EEG responses to long-term audio–visual stimulation. International journal of psychophysiology, 59(2), 81-90.
[40] Tohidi, H., Jabbari, M.M., (2012). “Decision role in management to increase effectiveness of an organization”. Procedia-social and behavioral sciences, 32: 825-828.
[41] Wang, TC., Tsai, CL., Tang, TW., Wang WL. and Lee, KT., 2019, The Effect of Cycling Through a Projection-Based Virtual Environment System on Generalized Anxiety Disorder, Journal of clinical medicine, Jul, 8(7), 973.
[42] Wang, Y., Zhang, Z., Jing, Y., Valadez, A. and Simons, F., 2016, How do we trust strangers? The neural correlates of decision making and outcome evaluation of generalized trust. Social cognitive and affective neuroscience, 11(10), 1666–1676.
[43] Williams, J., Ramaswamy, D. and Oulhaj, A., 2006, 10 Hz flicker improves recognition memory in older people. BMC Neurosci, 7(21).
[44] Winston, S., Strange, A., O'Doherty, J. and Dolan, J., 2002, Automatic and intentional brain responses during evaluation of trustworthiness of faces. Nature neuroscience, 5(3), 277.
