ABSTRACT
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1222 Neurotechnologies for Assessing User State . . . . . . 1222
Electrodermal Activity (EDA)/GSR-based Arousal and Cognitive Workload Gauge . . . . . . . . . . . . . . . . . 1223 EKG (ECG)-Based Arousal and Cognitive Workload Gauges . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1223 Body Position/Posture Tracking . . . . . . . . . . . . . . . . . 1223 Stress Gauge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1226 Arousal Meter Gauge . . . . . . . . . . . . . . . . . . . . . . . . . 1226 eXecutive Load Index Gauge . . . . . . . . . . . . . . . . . . . 1226 P300 Novelty Detector Gauge . . . . . . . . . . . . . . . . . . 1227 Engagement Index Gauge . . . . . . . . . . . . . . . . . . . . . 1227 New Workload Assessment Monitor (NuWAM; Combined EEG, ECG and EOG Sensors) . . . . . . . . . . 1227
Dry Electrodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1227 Functional Near Infrared (fNIR) . . . . . . . . . . . . . . . 1227 Boeing’s fNIR Approach . . . . . . . . . . . . . . . . . . . . . 1227 LM-ATL’s fNIR Approach . . . . . . . . . . . . . . . . . . . . . 1228
Pupillometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1228 EEG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1229
LM-ATL’s EEG-Based Technology . . . . . . . . . . . . . . 1229 DaimlerChrysler’s EEG-Based Technology . . . . . . . 1230
Event Related Optical Signal (EROS) . . . . . . . . . . . . . 1230 Cognitive Monitor (CogMon) . . . . . . . . . . . . . . . . . . . 1230
Application of Neurotechnologies . . . . . . . . . . . . . . 1230 Closed-Loop Systems . . . . . . . . . . . . . . . . . . . . . . . . . 1231
Honeywell’s AugCog Closed-Loop Efforts . . . . . . . 1231 DaimlerChrysler’s AugCog Closed-Loop Efforts . . . 1231 LM-ATL’s AugCog Closed-Loop Efforts . . . . . . . . . . 1232
Boeing’s AugCog Closed-Loop Efforts . . . . . . . . . . 1232 QinetiQ’s AugCog Closed-Loop Efforts . . . . . . . . . . 1232
Improving HCI System Design and Evaluation Capabilities . . . . . . . . . . . . . . . . . . . . . . . . 1233 Training Systems and Operator Selection . . . . . . . . . 1233
CPE Initiative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1233 Assessing training progression . . . . . . . . . . . . . . 1234 Assessing individual potential . . . . . . . . . . . . . . . 1234
Lessons Learned and Future Directions . . . . . . . . . 1235 The need to understand the user . . . . . . . . . . . . 1235 Integrate early, integrate often . . . . . . . . . . . . . . . 1235 Development strategy . . . . . . . . . . . . . . . . . . . . . 1235 One sensor/gauge does not fit all . . . . . . . . . . . . 1235 Cognitive bottlenecks may be task specific . . . . . 1235 Artifact detection and correction . . . . . . . . . . . . . 1235 Perceived stability/trust . . . . . . . . . . . . . . . . . . . . 1235 Timing of mitigation strategies . . . . . . . . . . . . . . 1236 Mitigation strategy principles . . . . . . . . . . . . . . . 1236 Hardware integration . . . . . . . . . . . . . . . . . . . . . . 1236 Context gauges . . . . . . . . . . . . . . . . . . . . . . . . . . 1236 Individual differences . . . . . . . . . . . . . . . . . . . . . 1236 Proactive vice reactive augmented cognition . . . 1236 Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1236
Cognitive State Sensors . . . . . . . . . . . . . . . . . . . . . . . 1237 Mitigation Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . 1237 Robust Controllers . . . . . . . . . . . . . . . . . . . . . . . . . . . 1237 Roles of National and Supra-National Institutions . . . 1237
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1238 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1238
In 1960, J. C. R. Licklider had a vision for a “Man-Computer Symbiosis” in which the human and computer, while dissimilar from one another, would live together in an intimate association producing increased handling and new ways of processing information (Licklider, 1960). Over the past few decades, several attempts to realize this vision have been made by interactive system developers, but each time, it has eluded them. This was likely due to the insufficiency of technology and computational power, but also to the need to mature several fields of basic science necessary to understand how human-machine symbiosis might be produced. A more thorough understanding of human brain functioning and what guides behavior during human-computer interaction has been a continuing missing requirement in the ability to enable true human-machine symbiosis.
