Engagement and not workload is implicated in automation-induced learning deficiencies for unmanned aerial system trainees
| dc.contributor.author | Blitch, John G., author | |
| dc.contributor.author | Clegg, Benjamin, advisor | |
| dc.contributor.author | Delosh, Edward, committee member | |
| dc.contributor.author | Kraiger, Kurt, committee member | |
| dc.contributor.author | Robinson, Daniel, committee member | |
| dc.date.accessioned | 2007-01-03T06:39:05Z | |
| dc.date.available | 2007-01-03T06:39:05Z | |
| dc.date.issued | 2014 | |
| dc.description.abstract | Automation has been known to provide both costs and benefits to experienced humans engaged in a wide variety of operational endeavors. Its influence on skill acquisition for novice trainees, however, is poorly understood. Some previous research has identified impoverished learning as a potential cost of employing automation in training. One prospective mechanism for any such deficits can be identified from related literature that highlights automation's role in reducing cognitive workload in the form of perceived task difficulty and mental effort. However three experiments using a combination of subjective self-report and EEG based neurophysiological instruments to measure mental workload failed to find any evidence that link the presence of automation to workload or to performance deficits resulting from its previous use. Rather the results in this study implicate engagement as an underlying basis for the inadequate mental models associated with automation-induced training deficits. The conclusion from examining these various states of cognition is that automation-induced training deficits observed in novice unmanned systems operators are primarily associated with distraction and disengagement effects, not an undesirable reduction in difficulty as previous research might suggest. These findings are consistent with automation's potential to push humans too far "out of the loop" in training. The implications of these findings are discussed. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | Blitch_colostate_0053A_12214.pdf | |
| dc.identifier.uri | http://hdl.handle.net/10217/82506 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | automation | |
| dc.subject | cognition | |
| dc.subject | learning | |
| dc.subject | neuroscience | |
| dc.subject | robot | |
| dc.subject | training | |
| dc.title | Engagement and not workload is implicated in automation-induced learning deficiencies for unmanned aerial system trainees | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Psychology | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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