The purpose of this study is to extend previous findings of Mouloua, Parasuraman, and Molloy (1993), Parasuraman, Mouloua, and Molloy (1996), Hilburn, Parasuraman, and Mouloua (1996), and Oakley, Mouloua, and Hancock (2003) by: 1) examining the effect of repeated adaptive function allocation to manual control of minimal length (5 minutes) to reduce of human error and minimize workload; 2) explore the placement or timing of adaptive function allocation intervals (approximately 20 minutes of automation control to reduce the human operators' monitoring decrement between intervals, maintain adaptive recovery performance levels, and improve response times); 3) examine different levels of automation reliability (30%, 60%, and 90% reliable); 4) explore factors that may be manipulated to reduce automation-induced monitoring inefficiency, increase detection of automation malfunctions, improve situation awareness, reduce response/reaction times, and reduce workload in a simulated complex aviation system. The study was a 2 (non-adaptive control vs. adaptive group) x 3 (30%, 60%, and 90% automation reliability condition) x 4 (repeated 25 minute session) mixed factorial design. Fifty-four undergraduate participants' (i.e., 27 participants per group; 9 participants per condition; at least 18 yrs. of age) percentage of detected malfunctions, response times, and subjective workload were gathered from the Multi-Attribute Task Battery and the NASA TLX. Results indicated a significant improvement in detection of malfunctions and response times during adaptive-function allocation to manual control but without adaptive recovery. There was a significant effect for workload found between baseline measures and experimental sessions by group in the first session but not across experimental sessions. Theoretical and practical implications, limitations and future research are discussed.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-4216 |
| Date | 01 January 2007 |
| Creators | Jones, Lauriann Maria |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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