Detection of error-related negativity in complex visual stimuli: a new neuroergonomic arrow in the practitioner's quiver. Issue 2 (1st February 2017)

• Record Type: Journal Article
• Authors: Sawyer, Ben D.; Karwowski, Waldemar; Xanthopoulos, Petros; Hancock, P. A.
• Is Part Of: Ergonomics. Volume 60:Issue 2(2017)
• Page Start: 234
• Page End: 240
• Publication Date: 2017-02-01
• Subjects: Error negativity -- applied electroencephalography -- visual search -- human-machine systems -- motorcycle conspicuity; Human engineering -- Periodicals; Cybernetics -- Periodicals; Industrial management -- Periodicals; Ergonomie -- Périodiques; Cybernétique -- Périodiques; Gestion d'entreprise -- Périodiques; 620.8205
• DOI: 10.1080/00140139.2015.1124928
• Languages: English
• ISSNs: 0014-0139

Abstract

Abstract: Brain processes responsible for the error-related negativity (ERN) evoked response potential (ERP) have historically been studied in highly controlled laboratory experiments through presentation of simple visual stimuli. The present work describes the first time the ERN has been evoked and successfully detected in visual search of complex stimuli. A letter flanker task and a motorcycle conspicuity task were presented to participants during electroencephalographic (EEG) recording. Direct visual inspection and subsequent statistical analysis of the resultant time-locked ERP data clearly indicated that the ERN was detectable in both groups. Further, the ERN pattern did not differ between groups. Such results show that the ERN can be successfully elicited and detected in visual search of complex static images, opening the door to applied neuroergonomic use. Harnessing the brain's error detection system presents significant opportunities and complex challenges, and implication of such are discussed in the context of human-machine systems. Practitioner Summary: For the first time, error-related negativity (ERN) has been successfully elicited and detected in a visually complex applied search task. Brain-process-based error detection in human-machine systems presents unique challenges, but promises broad neuroergonomic applications.