Neural Correspondence to Environmental Uncertainty in Multiple Probability Judgment Decision Support System

With artificial intelligence (AI) technology development, decision support systems (DSS) supporting human judgment are applied to multi-agent systems (MAS) as an interactive automated system. Despite the development of automation in systems, human judgment is important to prevent system failures due to impaired situational awareness under uncertainty. In human judgment, uncertainty can affect neural correspondence which induces cognitive bias, resulting in distorted judgment. Therefore, neural correspondences under uncertainty for appropriate judgments should be considered. This paper suggests understanding neural correspondence with multiple information judgments under uncertainty. For uncertainty, we used expected and unexpected uncertainty concepts influencing trustworthiness for the system performance. We used electroencephalogram (EEG) to measure neural correspondence in the multiple information judgment system. Based on the analysis of neural correspondence, we found that the cognitive process is based on linguistic and mathematical processing. With having trust in the system, humans can experience a strong incongruent situation by observing poor system performance. This study will give insights into cognitive process improvements of the multiple probability information judgment system through understanding neural correspondence under uncertainty.