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Predicting task performance for intelligent human-machine interactions
Human-machine teams are deployed in a diverse range of task environments and paradigms that may have high failure costs (e.g., nuclear power plants). It is critical that the machine team member can interact with the human effectively without reducing task performance. These interactions may be used...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513063/ https://www.ncbi.nlm.nih.gov/pubmed/36176571 http://dx.doi.org/10.3389/fnbot.2022.973967 |
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author | Heard, Jamison Baskaran, Prakash Adams, Julie A. |
author_facet | Heard, Jamison Baskaran, Prakash Adams, Julie A. |
author_sort | Heard, Jamison |
collection | PubMed |
description | Human-machine teams are deployed in a diverse range of task environments and paradigms that may have high failure costs (e.g., nuclear power plants). It is critical that the machine team member can interact with the human effectively without reducing task performance. These interactions may be used to manage the human's workload state intelligently, as the overall workload is related to task performance. Intelligent human-machine teaming systems rely on a facet of the human's state to determine how interaction occurs, but typically only consider the human's state at the current time step. Future task performance predictions may be leveraged to determine if adaptations need to occur in order to prevent future performance degradation. An individualized task performance prediction algorithm that relies on a multi-faceted human workload estimate is shown to predict a supervisor's task performance accurately. The analysis varies the prediction time frame (from 0 to 300 s) and compares results to a generalized algorithm. |
format | Online Article Text |
id | pubmed-9513063 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95130632022-09-28 Predicting task performance for intelligent human-machine interactions Heard, Jamison Baskaran, Prakash Adams, Julie A. Front Neurorobot Neuroscience Human-machine teams are deployed in a diverse range of task environments and paradigms that may have high failure costs (e.g., nuclear power plants). It is critical that the machine team member can interact with the human effectively without reducing task performance. These interactions may be used to manage the human's workload state intelligently, as the overall workload is related to task performance. Intelligent human-machine teaming systems rely on a facet of the human's state to determine how interaction occurs, but typically only consider the human's state at the current time step. Future task performance predictions may be leveraged to determine if adaptations need to occur in order to prevent future performance degradation. An individualized task performance prediction algorithm that relies on a multi-faceted human workload estimate is shown to predict a supervisor's task performance accurately. The analysis varies the prediction time frame (from 0 to 300 s) and compares results to a generalized algorithm. Frontiers Media S.A. 2022-09-13 /pmc/articles/PMC9513063/ /pubmed/36176571 http://dx.doi.org/10.3389/fnbot.2022.973967 Text en Copyright © 2022 Heard, Baskaran and Adams. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Heard, Jamison Baskaran, Prakash Adams, Julie A. Predicting task performance for intelligent human-machine interactions |
title | Predicting task performance for intelligent human-machine interactions |
title_full | Predicting task performance for intelligent human-machine interactions |
title_fullStr | Predicting task performance for intelligent human-machine interactions |
title_full_unstemmed | Predicting task performance for intelligent human-machine interactions |
title_short | Predicting task performance for intelligent human-machine interactions |
title_sort | predicting task performance for intelligent human-machine interactions |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513063/ https://www.ncbi.nlm.nih.gov/pubmed/36176571 http://dx.doi.org/10.3389/fnbot.2022.973967 |
work_keys_str_mv | AT heardjamison predictingtaskperformanceforintelligenthumanmachineinteractions AT baskaranprakash predictingtaskperformanceforintelligenthumanmachineinteractions AT adamsjuliea predictingtaskperformanceforintelligenthumanmachineinteractions |