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, 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.