Neural Correlates of Trust During an Automated System Monitoring Task: Preliminary Results of an Effective Connectivity Study - Sanders, Nathan; Choo, Sanghyun; Kim, Nayoung; Nam, Chang S.; Fitts, Edward P. (Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2019)
As autonomous systems become more prevalent and their inner workings become more opaque, we increasingly rely on trust to guide our interactions with them especially in complex or rapidly evolving situations. When our expectations of what automation is capable of do not match reality, the consequences can be sub-optimal to say the least. The degree to which our trust reflects actual capability is known as trust calibration. One of the approaches to studying this is neuroergonomics. By understanding the neural mechanisms involved in human-machine trust, we can design systems which promote trust calibration and possibly measure trust in real time. Our study used the Multi Attribute Task Battery to investigate neural correlates of trust in automation. We used EEG to record brain activity of participants as they watched four algorithms of varying reliability perform the SYSMON subtask on the MATB. Subjects reported their subjective trust level after each round. We subsequently conducted an effective connectivity analysis and identified the cingulate cortex as a node, and its asymmetry ratio and incoming information flow as possible indices of trust calibration. We hope our study will inform future work involving decision-making and real-time cognitive state detection.