- Dr. Matthew Bolton (Chairperson) - Associate Professor, Systems and Information Engineering Department, UVA
- Dr. Sara Lu Riggs (Advisor) - Associate Professor, Systems and Information Engineering Department, UVA
- Dr. William T. Scherer - Professor, Systems and Information Engineering Department, UVA
- Dr. Seongkook Heo - Assistant Professor, Computer Science, UVA
- Dr. Nadine Marie Moacdieh - Assistant Professor, Computer Science, Carleton University
Note: Please email SIE Student Services for zoom information.
Title: Cross-Modal Attentional Interactions and Their Implications on Multimodal Interface Design for Multitasking Environments
In multitasking occupations like air-traffic control and emergency response, attentional failures can cause disastrous incidents. These environments pose a strain on attention, a crucial component of human information processing. While its definition has evolved since its initial conception, attention is currently conceptualized as both a mental filter and resource; it filters and selects from the incoming sensory information, and it is also a constrain on the number of simultaneous mental processes that can take place. Attention is parameterized by two main limitations: processing speed (i.e., how fast we can process information) and capacity (i.e., how much information we can simultaneously process). Partitioning this limited attentional capacity across different tasks inevitably leads to attentional breakdowns and performance deterioration, especially during high workload periods. Multimodal interfaces are a promising approach of addressing such failures; they distribute the information load across different sensory modalities i.e., vision, audition, touch, potentially enhancing information processing. However, it is still not clear how to properly incorporate multimodal interfaces into multitasking environments. To this extent, the ongoing research studies whether multimodal interfaces can be effective in mitigating these attentional breakdowns and enhancing operator multitasking performance under high workloads.