Recognition Mechanism of Dangerous Goods Marks: Evidence from an Event-Related Potential Study

Author:

Wei Qiang1,Du Xinyu12,Lin Yixin3,Hou Guanhua4,Liu Siyuan1,Fang Hao56,Jin Ming7

Affiliation:

1. School of Education, Jianghan University, Wuhan 430056, China

2. School of Arts and Communication, China University of Geoscience, Wuhan 430074, China

3. School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

4. Pan Tianshou College of Architecture, Art and Design, Ningbo University, Ningbo 315211, China

5. School of Art and Design, Wuhan Institute of Technology, Wuhan 430205, China

6. Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Putian 351100, China

7. Lancaster Medical School, Lancaster University, Lancaster LA1 4YW, UK

Abstract

Dangerous goods marks are the most effective means of alerting individuals to the potential dangers associated with the transport of dangerous goods. In order to gain a better understanding of how dangerous goods marks convey risk information, the cognitive processing of dangerous goods marks was examined by measuring event-related potentials (ERPs). We recruited 23 participants, and their ERP data were recorded. We discovered that the dangerous goods marks elicited a larger P200 amplitude and a smaller N300 amplitude, indicating that, compared to other marks, the dangerous goods marks exhibited stronger warning information and drew more attention from the subjects. Simultaneously, dangerous goods marks elicited insufficient emotional arousal in individuals. Therefore, these findings suggest that the designs of dangerous goods marks need to be improved, such as improving the graphic consistency. Changes in ERP patterns can be used to measure the risk perception level of dangerous goods marks, which can be used as an accurate indicator of the effectiveness of warning sign design. In addition, this study provides a theoretical foundation for the cognitive understanding mechanism of dangerous goods marks.

Funder

2021 Hubei Provincial Teaching Reform Research Project

Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University Open Topics General Projects

Publisher

MDPI AG

Subject

Health, Toxicology and Mutagenesis,Public Health, Environmental and Occupational Health

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