Abstract

As the global landscape changes and powers rise and fall, the Contested, Degraded, and Operationally Limited (CDO) environment is likely to be the new normal going forward. Uncertainty variables, such as missing, false, or extra information characterize the CDO environment. A key focus of this dissertation is optimizing training for recognizing these uncertainty variables when training time is limited. This was investigated by either exposing participants to multiple uncertainty variables at a time with low doses of each (whole-task training), by exposing singular variables at a time with high doses (part-task training) or using no variables throughout training (control). A key motivator behind this research was Cognitive Load Theory, as mindful abstraction can only occur if there are cognitive resources to spare. Dependent variables, such as time to complete, number correct, task workload, and uncertainty variables identified, were collected. The results revealed that on the transfer task, the part-task condition recorded a significantly lower workload score than the whole-task (and control) condition, while the condition's workload scores were consistent across all training and transfer tasks. In contrast, the control and whole-task condition experienced significant increases in workload during the transfer task. Additionally, the part-task condition participants were able to significantly identify more uncertainty variables on the final task than the whole-task condition and control condition. The part-task condition found the transfer task to be the "easiest" in terms of workload, and as there is more opportunity for mindful abstraction if there are more cognitive resources available, it can be stated that based on these results, the part-task training schedule facilitated mindful abstraction more than the other two training schedules. As this was a limited, abstracted, and laboratory experiment, future work should apply the same methodology to applied tasks in a controlled environment to gauge further usefulness of this research.

Notes

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Graduation Date

2018

Semester

Fall

Advisor

Martin, Glenn

Degree

Doctor of Philosophy (Ph.D.)

College

College of Sciences

Degree Program

Modeling and Simulation

Format

application/pdf

Identifier

CFE0007339

URL

http://purl.fcla.edu/fcla/etd/CFE0007339

Language

English

Release Date

December 2018

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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