Development of a Human Performance Baseline of Lay Error in Targeting
This study pursued two key objectives: first, to create an advanced model that accurately represents a human’s precision in aligning sight crosshairs with the center of a target, otherwise known as lay error; second, to investigate how various engagement conditions (such as target shape,...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11008800/ |
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| author | Thirimachos Bourlai Parker Ensing Alexia Toma Victor Philippe Jennifer Forsythe Cody L. Lundberg Nicholas R. Gans |
| author_facet | Thirimachos Bourlai Parker Ensing Alexia Toma Victor Philippe Jennifer Forsythe Cody L. Lundberg Nicholas R. Gans |
| author_sort | Thirimachos Bourlai |
| collection | DOAJ |
| description | This study pursued two key objectives: first, to create an advanced model that accurately represents a human’s precision in aligning sight crosshairs with the center of a target, otherwise known as lay error; second, to investigate how various engagement conditions (such as target shape, size, range, and motion) influence lay error and determine whether a single error model could be reliably applied across different targets. To address these objectives, a photo-realistic simulation environment using Unreal Engine was developed featuring four different targets, four motion configurations, four levels of zoom, four ranges, three levels of contrast, and four levels of obstructions. After prototyping and evaluation, the simulation environment was used to collect lay error metrics as 110 IRB-approved subjects aligned crosshairs on targets under various conditions. Subjects fired a total of 11,088 shots throughout a set of three different data collection sessions that were completed over 10 months. After data collection, statistical analyses of lay error were conducted including fitting non-Gaussian distribution functions and applying Fitts’ Law to the targeting analysis. Experimental results show that (a) the lay error changes with target type and is greater on closer targets and lower on farther targets for the majority of the subjects who participated in the study; (b) The firing time for most shots is below 10 seconds for most subjects, independent of the test conditions, distance to target, or target type; and (c) although the subjects’ shots were mostly accurate, subjects occasionally had significant errors (outliers) that merit deeper consideration. |
| format | Article |
| id | doaj-art-6a51aced73e64c6fa554d68c4989276c |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-6a51aced73e64c6fa554d68c4989276c2025-08-20T03:12:36ZengIEEEIEEE Access2169-35362025-01-0113911829119910.1109/ACCESS.2025.357159111008800Development of a Human Performance Baseline of Lay Error in TargetingThirimachos Bourlai0https://orcid.org/0000-0001-8751-0836Parker Ensing1Alexia Toma2Victor Philippe3https://orcid.org/0009-0009-2715-9664Jennifer Forsythe4https://orcid.org/0009-0001-9819-3175Cody L. Lundberg5https://orcid.org/0009-0000-9451-3074Nicholas R. Gans6https://orcid.org/0000-0003-3462-4199University of Georgia, Athens, GA, USAUniversity of Georgia, Athens, GA, USAUniversity of Georgia, Athens, GA, USAUniversity of Georgia, Athens, GA, USAU.S. Army DEVCOM Analysis Center, Aberdeen Proving Ground, MD, USAThe University of Texas at Arlington, Arlington, TX, USAThe University of Texas at Arlington, Arlington, TX, USAThis study pursued two key objectives: first, to create an advanced model that accurately represents a human’s precision in aligning sight crosshairs with the center of a target, otherwise known as lay error; second, to investigate how various engagement conditions (such as target shape, size, range, and motion) influence lay error and determine whether a single error model could be reliably applied across different targets. To address these objectives, a photo-realistic simulation environment using Unreal Engine was developed featuring four different targets, four motion configurations, four levels of zoom, four ranges, three levels of contrast, and four levels of obstructions. After prototyping and evaluation, the simulation environment was used to collect lay error metrics as 110 IRB-approved subjects aligned crosshairs on targets under various conditions. Subjects fired a total of 11,088 shots throughout a set of three different data collection sessions that were completed over 10 months. After data collection, statistical analyses of lay error were conducted including fitting non-Gaussian distribution functions and applying Fitts’ Law to the targeting analysis. Experimental results show that (a) the lay error changes with target type and is greater on closer targets and lower on farther targets for the majority of the subjects who participated in the study; (b) The firing time for most shots is below 10 seconds for most subjects, independent of the test conditions, distance to target, or target type; and (c) although the subjects’ shots were mostly accurate, subjects occasionally had significant errors (outliers) that merit deeper consideration.https://ieeexplore.ieee.org/document/11008800/Lay errorsimulation environmentdata collectionstatistical analysis |
| spellingShingle | Thirimachos Bourlai Parker Ensing Alexia Toma Victor Philippe Jennifer Forsythe Cody L. Lundberg Nicholas R. Gans Development of a Human Performance Baseline of Lay Error in Targeting IEEE Access Lay error simulation environment data collection statistical analysis |
| title | Development of a Human Performance Baseline of Lay Error in Targeting |
| title_full | Development of a Human Performance Baseline of Lay Error in Targeting |
| title_fullStr | Development of a Human Performance Baseline of Lay Error in Targeting |
| title_full_unstemmed | Development of a Human Performance Baseline of Lay Error in Targeting |
| title_short | Development of a Human Performance Baseline of Lay Error in Targeting |
| title_sort | development of a human performance baseline of lay error in targeting |
| topic | Lay error simulation environment data collection statistical analysis |
| url | https://ieeexplore.ieee.org/document/11008800/ |
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