Name
Abstract | ||
|---|---|---|
In this paper, we used signal detection theory (SDT) as a tool to evaluate human performance in a multitasking environment. The primary objective of using SDT is to assess an operator's sensitivity (d') and bias (beta). In addition, NASA-TLX was used to measure participants' workload under different complexity scenarios. During the experiment, participants were asked to detect abnormal and alarm signals on a gauge monitoring display. They also needed to perform multi-attribute task battery (MATB) tasks at the same time. The gauge-monitoring screen contains total 52 gauges (flow, level, temperature, and pressure). The MATB consists of system monitoring, target tracking, and dynamic resource management. The results of this study demonstrate that participants showed various levels of sensitivity (d') in the gauge-monitoring task based on the degree of task complexity. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-40030-3_2 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Signal detection theory,Human-in-the-loop simulation,Mental workload | Resource management,Detection theory,ALARM,Workload,Computer science,System monitoring,Real-time computing,Operator (computer programming),Human multitasking,Battery (electricity) | Conference |
Volume | ISSN | Citations |
9736 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Monika Putri | 1 | 0 | 0.34 |
| Xiaonan Yang | 2 | 0 | 0.68 |
| Jung Hyup Kim | 3 | 10 | 4.85 |