Theoretical Throughput Capacity: Capabilities of Human Information Processing during Multitasking
Subhashini Ganapathy (Committee Member), David Kender (Committee Member), Andy Mckinley (Committee Member), Chandler Phillips (Advisor), David Reynolds (Committee Member), Dana Rogers (Committee Member)
Doctor of Philosophy (PhD)
Technological advancements in automation have allowed humans to collect data at exorbitant rates. As a result, human multitasking as become an integral part of many government, industrial, and routine activities. However, multitasking can be difficult to study, primarily due to the lack of objective metrics. The Human Operator Informatic Model (HOIM) is an information-theory based model that has been recently developed to combat these difficulties. The HOIM is based on Shannon information theory and can provide an objective, meaningful measure to describe system complexity and overall multitasking performance. The main goal of this work was to validate the HOIM as a reliable model and test the effect of multisensorial feedback on operator strategy and performance. Results showed that as input information rate increases, operator output also increases, but not at a proportional rate. As a result, overall information throughput declines with in- creasing input information rate. Further, multisensorial feedback during multitasking was shown to increase performance of the two tasks with feedback. However, this came at a proportional cost in the performance of the two tasks that did not have feedback. Overall performance was not significantly affected by the presence of multisensorial feedback. This work proposes that human operators have a throughput capacity: a maximum, finite capacity to process information during multitasking.
Department or Program
Ph.D. in Engineering
Year Degree Awarded
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