Predicting Cognitive Load and Operational Performance in a Simulated Marksmanship Task
Modern operational environments can place significant demands on a service member's cognitive resources, increasing the risk of errors or mishaps due to overburden. The ability to monitor cognitive burden and associated performance within operational environments is critical to improving missio...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
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Frontiers Media SA
2021
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| Online Access: | https://hdl.handle.net/1721.1/132644 |
| _version_ | 1826192434346852352 |
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| author | Rao, Hrishikesh M. Smalt, Christopher J. Rodriguez, Aaron Wright, Hannah M. Mehta, Daryush D. Brattain, Laura J Edwards, Harvey Lammert, Adam Heaton, Kristin J. Quatieri, Thomas F. |
| author2 | Lincoln Laboratory |
| author_facet | Lincoln Laboratory Rao, Hrishikesh M. Smalt, Christopher J. Rodriguez, Aaron Wright, Hannah M. Mehta, Daryush D. Brattain, Laura J Edwards, Harvey Lammert, Adam Heaton, Kristin J. Quatieri, Thomas F. |
| author_sort | Rao, Hrishikesh M. |
| collection | MIT |
| description | Modern operational environments can place significant demands on a service member's cognitive resources, increasing the risk of errors or mishaps due to overburden. The ability to monitor cognitive burden and associated performance within operational environments is critical to improving mission readiness. As a key step toward a field-ready system, we developed a simulated marksmanship scenario with an embedded working memory task in an immersive virtual reality environment. As participants performed the marksmanship task, they were instructed to remember numbered targets and recall the sequence of those targets at the end of the trial. Low and high cognitive load conditions were defined as the recall of three- and six-digit strings, respectively. Physiological and behavioral signals recorded included speech, heart rate, breathing rate, and body movement. These features were input into a random forest classifier that significantly discriminated between the low- and high-cognitive load conditions (AUC = 0.94). Behavioral features of gait were the most informative, followed by features of speech. We also showed the capability to predict performance on the digit recall (AUC = 0.71) and marksmanship (AUC = 0.58) tasks. The experimental framework can be leveraged in future studies to quantify the interaction of other types of stressors and their impact on operational cognitive and physical performance. |
| first_indexed | 2024-09-23T09:12:47Z |
| format | Article |
| id | mit-1721.1/132644 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T09:12:47Z |
| publishDate | 2021 |
| publisher | Frontiers Media SA |
| record_format | dspace |
| spelling | mit-1721.1/1326442022-09-30T14:05:10Z Predicting Cognitive Load and Operational Performance in a Simulated Marksmanship Task Rao, Hrishikesh M. Smalt, Christopher J. Rodriguez, Aaron Wright, Hannah M. Mehta, Daryush D. Brattain, Laura J Edwards, Harvey Lammert, Adam Heaton, Kristin J. Quatieri, Thomas F. Lincoln Laboratory Modern operational environments can place significant demands on a service member's cognitive resources, increasing the risk of errors or mishaps due to overburden. The ability to monitor cognitive burden and associated performance within operational environments is critical to improving mission readiness. As a key step toward a field-ready system, we developed a simulated marksmanship scenario with an embedded working memory task in an immersive virtual reality environment. As participants performed the marksmanship task, they were instructed to remember numbered targets and recall the sequence of those targets at the end of the trial. Low and high cognitive load conditions were defined as the recall of three- and six-digit strings, respectively. Physiological and behavioral signals recorded included speech, heart rate, breathing rate, and body movement. These features were input into a random forest classifier that significantly discriminated between the low- and high-cognitive load conditions (AUC = 0.94). Behavioral features of gait were the most informative, followed by features of speech. We also showed the capability to predict performance on the digit recall (AUC = 0.71) and marksmanship (AUC = 0.58) tasks. The experimental framework can be leveraged in future studies to quantify the interaction of other types of stressors and their impact on operational cognitive and physical performance. 2021-09-27T15:39:03Z 2021-09-27T15:39:03Z 2020-07 2020-03 Article http://purl.org/eprint/type/JournalArticle 1662-5161 https://hdl.handle.net/1721.1/132644 Rao, Hrishikesh M. et al. "Predicting Cognitive Load and Operational Performance in a Simulated Marksmanship Task." Frontiers in Human Neuroscience 14 (July 2020): 222. © 2020 Massachusetts Institute of Technology https://doi.org/10.3389/fnhum.2020.00222 Frontiers in Human Neuroscience Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Frontiers Media SA Frontiers |
| spellingShingle | Rao, Hrishikesh M. Smalt, Christopher J. Rodriguez, Aaron Wright, Hannah M. Mehta, Daryush D. Brattain, Laura J Edwards, Harvey Lammert, Adam Heaton, Kristin J. Quatieri, Thomas F. Predicting Cognitive Load and Operational Performance in a Simulated Marksmanship Task |
| title | Predicting Cognitive Load and Operational Performance in a Simulated Marksmanship Task |
| title_full | Predicting Cognitive Load and Operational Performance in a Simulated Marksmanship Task |
| title_fullStr | Predicting Cognitive Load and Operational Performance in a Simulated Marksmanship Task |
| title_full_unstemmed | Predicting Cognitive Load and Operational Performance in a Simulated Marksmanship Task |
| title_short | Predicting Cognitive Load and Operational Performance in a Simulated Marksmanship Task |
| title_sort | predicting cognitive load and operational performance in a simulated marksmanship task |
| url | https://hdl.handle.net/1721.1/132644 |
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