Judgment analysis for real-time decision support using the cognitive shadow policy-capturing system

Abstract

The present work introduces a prototype intelligent cognitive assistant that continuously learns the decision pattern of the user online, instantly recognizes deviations from that pattern as potential errors and then alerts the user accordingly. We investigated the potential of this prototype system using a human-in-the-loop experiment designed to assess impacts on decision making performance, workload and trust in the decision support capability. Study participants interacted with a naval air-defence testbed to classify radar contacts as friendly, uncertain or hostile based on track parameters displayed on screen. The between-group experimental design included a control condition and two decision support conditions (with system reliability provided either offline or online). Results showed that both decision support conditions significantly improved task accuracy compared to the control condition. The advisory system was successful at improving human performance without burdening the user with excessive additional workload, even when providing reliability information in real time.