Summaries - Office of Research & Innovation
Research Summaries
Back Contingent Attention Management (CAM) Experimentation
Fiscal Year | 2020 |
Division | Graduate School of Operational & Information Sciences |
Department | Computer Science |
Investigator(s) | Balogh, Imre L. |
Sponsor | Naval Research Laboratory (Navy) |
Summary | Research conducted at NRL has demonstrated the ability of attention management strategies to significantly improve operator performance in highly multitasking environments such as Combat Information Centers (CICs). Viewing human attention as a limited resource, this research attempts to manage an operator's attention through serialized guidance thus freeing the operator to completely attend to one task at a time. NRL has already shown the merit of mediated attention management for concurrent radio communications monitoring in CICs through an extended series of human performance studies. Measures of attention to data, comprehension, and effort improve dramatically when competing communications circuits are buffered and played sequentially at accelerated rates of speech to ensure that transmissions on each circuit are fully presented. An attention management strategy, however, must include a task prioritization model that can inform serialized guidance so that operators address the most critical amongst competing primary tasks. Additionally, this model must provide for operator-centered control that enables the operators to affect how tasks are prioritized based on their current operational needs. Finally, contingencies of an operator's workflow must be accounted for with strategies for rapid recovery of task-level SA when an operator leaves and reenters attention management. The objective of this research is to design and evaluate operator-centric prioritization algorithms for chat and voice communications. Operator centric prioritization means that operators are able to specify topics of interest explicitly, and provide the system with feedback on how well the prioritization scheme reflects their informational priorities. The proposed approach will utilize three criteria for assessing the relative importance of a chat or voice message: relevance, urgency and precedence. Given these three measures, the Analytic Hierarchy Process (AHP) model will be utilized to derive a final priority for an incoming chat or voice message. |
Keywords | Analytic Hierarchy Process Artificial Intelligence Attention Decision Support machine learning |
Publications | Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal |
Data | Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal |