Conference proceeding
Human-automated judgment learning: applying interpersonal learning to investigate human interaction with alerting systems
Proceedings. The 21st Digital Avionics Systems Conference, v 2, 7A3
2002
Abstract
Alerting systems are a prevalent and integral part of modem cockpits. When alerting systems serve their intended roles in the cockpit, they can increase safety through monitoring for, and directing pilot attention to, developing hazards. However, numerous studies have identified several problematic types of interactions between pilot and alerting systems. Preventing problematic interactions between pilots and alerting systems requires a methodology that can capture and clarify the extent to which pilots will innately agree with and ultimately rely upon alerting systems. This paper details the development of Human-Automated Judgment Learning (HAJL), a new methodology attempting to provide these capabilities. The first section provides the background with regard to modeling and measuring pilot interaction with alerting systems. The next section presents the HAJL methodology. Then an initial experiment substantiating the HAJL methodology is described.
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Details
- Title
- Human-automated judgment learning: applying interpersonal learning to investigate human interaction with alerting systems
- Creators
- E.J Bass - University of VirginiaA.R Pritchett - Space Engineering University
- Publication Details
- Proceedings. The 21st Digital Avionics Systems Conference, v 2, 7A3
- Conference
- The 21st Digital Avionics Systems Conference, 21st
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Other Identifier
- 991019292135104721