Developing a Cognitive Battery for Top-Down Workload Assessment

Abstract

Event Abstract Back to Event Developing a Cognitive Battery for Top-Down Workload Assessment Amanda E. Kraft1*, Matthias D. Ziegler1, Sophia Mayne-DeLuca1, Trevor Sands1, Alison M. Perez1, Jesse Mark2, Adrian Curtin2, Amanda Sargent2, Hasan Ayaz2, 3, 4 and William Casebeer1 1 Lockheed Martin (United States), United States 2 School of Biomedical Engineering, Science and Health Systems, Drexel University, United States 3 Department of Family and Community Health, School of Nursing, University of Pennsylvania, United States 4 General Pediatrics, Children's Hospital of Philadelphia, United States Computational models of workload are often specialized to specific types of tasks and do not transfer between tasks. Part of this is due to the reliance of building models based on similar training tasks that enable detection of comparable neural indicators. As such, many workload prediction systems currently rely on tasks of similar design and interaction targeting specific cognitive functions. To address this, we developed personalized models in our study from a battery of standard cognitive tasks and mapped them to a dynamic operational environment, requiring simultaneous use of a combination of these cognitive functions. We first discuss the design of the baseline battery of cognitive tasks and how the components translate to a more dynamic two-person mission. We selected and modified the following six standard cognitive workload tasks to comprise a 60-minute baseline battery assessment: Stop Signal Task, Conjunctive Continuous Performance Task (CCPT), Spatial Span Working Memory Task, Trail Making Task (TMT), Situation Awareness Global Assessment Technique (SAGAT), and Balloon Analogue Risk Task (BART). All tasks were adapted in at least two ways: (1) timing and number of trials to support use of functional neuroimaging with block design and enable 60-minute max across all 6 tasks; and (2) visually to reflect icons and styles expected in the final task. We then evaluate the effectiveness of the workload-based performance predictions for each cognitive function in guiding task reassignment in a two-person mission, as compared to reassignment based on performance alone. The two-person mission is composed of multiple interrelated subtasks with the overall goal of finding and identifying as many targets as possible in the time allowed. Each of the subtasks will require use of one or more cognitive functions, mapping to those tested in the baseline battery. By including workload indicators across the cognitive functions, we anticipate enabling reassignment prior to performance reduction, allowing for overall increase in performance while maintaining sustainable workload across the teammates. Acknowledgements This research was supported by the Air Force Research laboratory’s Human Performance Sensing BAA call 002. Keywords: baseline battery, baseline battery design, Workload Assessment, Cognitive Modeling, Performance assessment Conference: 2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018. Presentation Type: Oral Presentation Topic: Neuroergonomics Citation: Kraft AE, Ziegler MD, Mayne-DeLuca S, Sands T, Perez AM, Mark J, Curtin A, Sargent A, Ayaz H and Casebeer W (2019). Developing a Cognitive Battery for Top-Down Workload Assessment. Conference Abstract: 2nd International Neuroergonomics Conference. doi: 10.3389/conf.fnhum.2018.227.00028 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 02 Apr 2018; Published Online: 27 Sep 2019. * Correspondence: Ms. Amanda E Kraft, Lockheed Martin (United States), Bethesda, United States, amanda.e.kraft@lmco.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Amanda E Kraft Matthias D Ziegler Sophia Mayne-DeLuca Trevor Sands Alison M Perez Jesse Mark Adrian Curtin Amanda Sargent Hasan Ayaz William Casebeer Google Amanda E Kraft Matthias D Ziegler Sophia Mayne-DeLuca Trevor Sands Alison M Perez Jesse Mark Adrian Curtin Amanda Sargent Hasan Ayaz William Casebeer Google Scholar Amanda E Kraft Matthias D Ziegler Sophia Mayne-DeLuca Trevor Sands Alison M Perez Jesse Mark Adrian Curtin Amanda Sargent Hasan Ayaz William Casebeer PubMed Amanda E Kraft Matthias D Ziegler Sophia Mayne-DeLuca Trevor Sands Alison M Perez Jesse Mark Adrian Curtin Amanda Sargent Hasan Ayaz William Casebeer Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.