Modeling Dual-Task Concurrency and Effort in QN-ACTR and IMPRINT.

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dc.contributor.author Best, Christopher Jason en_US
dc.date.accessioned 2014-01-16T20:41:02Z
dc.date.available NO_RESTRICTION en_US
dc.date.available 2014-01-16T20:41:02Z
dc.date.issued 2013 en_US
dc.date.submitted 2013 en_US
dc.identifier.uri http://hdl.handle.net/2027.42/102330
dc.description.abstract Computational cognitive models have wide ranging applications from reducing the time and cost of task and interface analyses to the discovery of new human cognitive phenomena. We investigate the use and limitations of IMPRINT, a task network simulation tool, and develop an extension to improve the modeling of task component execution limits in multi-task performance under high workload. The extension is implemented as a Soar agent that moderates task execution akin to executive processes in EPIC. We show that an IMPRINT model of a UAV operation task with the extension exhibits qualitatively distinct workload management strategies also observed in human performance of the same task. Next, we develop QN-ACTR models of a concurrent addition and targeting task and collect empirical data of human performance on the tasks to validate the models' predictions of execution time and a time sharing concurrency metric. We also use the empirical data to validate an IMPRINT model of the addition and targeting tasks. Both QN-ACTR and IMPRINT models capture the primary effects of variable task difficulty parameters on execution time and concurrency. Model inaccuracy at the subtask level provides evidence for the use of visual spatial memory during complex addition. In a second experiment with similar tasks, we introduce an incentive to examine the effects of effort on execution time and concurrency in dual task performance. Incentive induced effort is found to increase performance on the rewarded dimension without an increase in the time sharing concurrency metric, suggesting that the performance improvements are not derived from an increase in task scheduling efficiency or resource sharing but from the same improvements found in single task conditions. The QN-ACTR task models are modified to account for the increased effort by adjusting base level parameters and are validated with the empirical data. en_US
dc.language.iso en_US en_US
dc.subject Cognitive Modeling en_US
dc.subject Multitasking en_US
dc.subject Concurrency en_US
dc.subject Effort en_US
dc.title Modeling Dual-Task Concurrency and Effort in QN-ACTR and IMPRINT. en_US
dc.type Thesis en_US
dc.description.thesisdegreename PhD en_US
dc.description.thesisdegreediscipline Industrial & Operations Engineering en_US
dc.description.thesisdegreegrantor University of Michigan, Horace H. Rackham School of Graduate Studies en_US
dc.contributor.committeemember Liu, Yili en_US
dc.contributor.committeemember Booth, Victoria en_US
dc.contributor.committeemember Sarter, Nadine B. en_US
dc.contributor.committeemember Locket Iii, John F. en_US
dc.subject.hlbsecondlevel Industrial and Operations Engineering en_US
dc.subject.hlbtoplevel Engineering en_US
dc.description.bitstreamurl http://deepblue.lib.umich.edu/bitstream/2027.42/102330/1/cjbest_1.pdf
dc.owningcollname Dissertations and Theses (Ph.D. and Master's)
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