Citation

Mensen JM, Holland SB, Helton WS, Shaw TH, Peterson MS. Hum. Factors 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, Human Factors and Ergonomics Society, Publisher SAGE Publishing)

DOI

10.1177/00187208221127945

PMID

36124873

Abstract

OBJECTIVE: Expand research on the Sustained Attention to Response Task (SART) to a more applied agricultural target detection/selection task and examine the utility of various performance metrics, including composite measures of speed and accuracy, in a High-Go/Low-No-Go stimuli task.

BACKGROUND: Modified SARTs have been utilized to investigate mechanisms, such as failures of response inhibition, occurring in friendly fire and collateral damage incidents. Researchers have demonstrated that composite measures of speed and accuracy are useful for Low Go/High No-Go stimuli tasks, but this has not been demonstrated for High-Go/Low-No-Go tasks, such as the SART.

METHOD: Participants performed a modified SART, where they selected ("sprayed") images of weeds (Go stimuli) that appeared on a computer screen, while withholding to rarer soybean plant images (No-Go stimuli).

RESULTS: Response time was a function of distance from a central starting point. Participants committed commission errors (sprayed the soybeans) at a significantly higher rate when the stimuli appeared under the cursor centered on the screen for each trial. Participant's omission errors (failure to spray a weed) increased significantly as a function of distance. The composite measures examined were primarily influenced by response time and omission errors limiting their utility when commission errors are of particular interest.

CONCLUSION: Participants are far more accurate in their decision making when required to execute a longer duration motor task in High-Go/Low-No-Go experiments.

APPLICATION: Demonstrates a serious human factors liability of target detection and snap-to-target systems.

Language: en

Keywords

reaction time; cognition; displays and controls; graphical user interfaces; human performance modeling; methods and skills; motor behavior; signal detection theory