The Role of Implicit and Explicit Systems Feedback in Return-to-Manual Performance
2018-11
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The Role of Implicit and Explicit Systems Feedback in Return-to-Manual Performance
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2018-11
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Differences in display and automation types were examined to assess their influence on the development of novice-level participants’ explicit and implicit understanding of a dynamic system. Participants operated a highly simplified nuclear power plant simulation for three simulation rounds; the first two rounds with the assistance of automated support and the third return-to-manual (RTM) round in which automated support was reduced. The combination of three Display Types (separable, configural, semantic-spatial) and two Automation Types (decision automation, no decision automation) resulted in a total of six unique conditions, with multiple performance, understanding, and workload measurements being employed. Results indicated the availability of decision automation improved performance and understanding, and reduced workload, but resulted in greater negative impacts associated with the loss of automated support in the return-to-manual round. Higher rates of errors occurred when attempting to address system damage in the RTM round for participants who previously operated the system with the assistance of decision automation and likely resulted from the availability of decision automation reducing participant experience operating the system in a damaged state. Examination of the influence of Display Types found the explicit feedback available within the semantic-spatial display improved the efficiency of meeting energy demand and reduced frustration when compared to the separable display but did not improve participants’ understanding of the system. Decision automation appeared to negate differences between individual display type conditions, whereas distinct differences were found between no-decision automation conditions. Analysis of the predictive value of workload and understanding measurements found higher levels of implicit errors, better explicit understanding, and lower workload in simulation rounds one and two predicted smaller impacts on RTM performance. These and additional findings corroborate and extend previous research pertaining to relationships between automation, displays, understanding, and return-to-manual performance for novice participants.
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University of Minnesota Ph.D. dissertation. 2018. Major: Human Factors/Ergonomics. Advisor: Kathleen Harder. 1 computer file (PDF); 164 pages.
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Fitch, Brian. (2018). The Role of Implicit and Explicit Systems Feedback in Return-to-Manual Performance. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/202203.
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