Relativity modeling of work motivation and human error probability based on neural network

Jialei Chen; Xing Pan

doi:10.1109/PHM.2016.7819917

Relativity modeling of work motivation and human error probability based on neural network

Jialei Chen
, Xing Pan

School of Reliability and Systems Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Human error behavior is jointly determined by human and environment, specially, the effect of work motivation has been emphasized to be an important factor on human performance in psychology and behavioral science. However, current Human Reliability Analysis (HRA) pay little attention to this aspect, which creates difficulties in finding the mechanisms of error behavior that could arise in the cognitive process. To fill the gap mentioned above, this paper considered work motivation factors into HRA, the hypothesis of the relationship between work motivation and Human Error Probability (HEP) in task context was given, with reference to the relationship between arousal and performance described by Yerkes-Dodson Law in psychology, then a relativity model of work motivation and HEP on different task difficulty was proposed based on neural network, and simulation experiment was carried out. The experimental results showed the work motivation-HEP relationship is of a U-shaped curve and the optimal work motivation for a difficult task is lower than an easy task.

Original language	English
Title of host publication	Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016
Editors	Qiang Miao, Zhaojun Li, Ming J. Zuo, Liudong Xing, Zhigang Tian
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509027781
DOIs	https://doi.org/10.1109/PHM.2016.7819917
State	Published - 16 Jan 2017
Event	7th IEEE Prognostics and System Health Management Conference, PHM-Chengdu 2016 - Chengdu, Sichuan, China Duration: 19 Oct 2016 → 21 Oct 2016

Publication series

Name	Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016

Conference

Conference	7th IEEE Prognostics and System Health Management Conference, PHM-Chengdu 2016
Country/Territory	China
City	Chengdu, Sichuan
Period	19/10/16 → 21/10/16

Keywords

Human error probability
Human reliability analysis
Neural network
Work motivation
Yerkesdodson law

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10.1109/PHM.2016.7819917

Cite this

Chen, J., & Pan, X. (2017). Relativity modeling of work motivation and human error probability based on neural network. In Q. Miao, Z. Li, M. J. Zuo, L. Xing, & Z. Tian (Eds.), Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016 Article 7819917 (Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PHM.2016.7819917

Chen, Jialei ; Pan, Xing. / Relativity modeling of work motivation and human error probability based on neural network. Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016. editor / Qiang Miao ; Zhaojun Li ; Ming J. Zuo ; Liudong Xing ; Zhigang Tian. Institute of Electrical and Electronics Engineers Inc., 2017. (Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016).

@inproceedings{8b60df7b621d4d3db6d2d27404a19951,

title = "Relativity modeling of work motivation and human error probability based on neural network",

abstract = "Human error behavior is jointly determined by human and environment, specially, the effect of work motivation has been emphasized to be an important factor on human performance in psychology and behavioral science. However, current Human Reliability Analysis (HRA) pay little attention to this aspect, which creates difficulties in finding the mechanisms of error behavior that could arise in the cognitive process. To fill the gap mentioned above, this paper considered work motivation factors into HRA, the hypothesis of the relationship between work motivation and Human Error Probability (HEP) in task context was given, with reference to the relationship between arousal and performance described by Yerkes-Dodson Law in psychology, then a relativity model of work motivation and HEP on different task difficulty was proposed based on neural network, and simulation experiment was carried out. The experimental results showed the work motivation-HEP relationship is of a U-shaped curve and the optimal work motivation for a difficult task is lower than an easy task.",

keywords = "Human error probability, Human reliability analysis, Neural network, Work motivation, Yerkesdodson law",

author = "Jialei Chen and Xing Pan",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 7th IEEE Prognostics and System Health Management Conference, PHM-Chengdu 2016 ; Conference date: 19-10-2016 Through 21-10-2016",

year = "2017",

month = jan,

day = "16",

doi = "10.1109/PHM.2016.7819917",

language = "英语",

series = "Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Qiang Miao and Zhaojun Li and Zuo, \{Ming J.\} and Liudong Xing and Zhigang Tian",

booktitle = "Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016",

address = "美国",

}

Chen, J & Pan, X 2017, Relativity modeling of work motivation and human error probability based on neural network. in Q Miao, Z Li, MJ Zuo, L Xing & Z Tian (eds), Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016., 7819917, Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016, Institute of Electrical and Electronics Engineers Inc., 7th IEEE Prognostics and System Health Management Conference, PHM-Chengdu 2016, Chengdu, Sichuan, China, 19/10/16. https://doi.org/10.1109/PHM.2016.7819917

Relativity modeling of work motivation and human error probability based on neural network. / Chen, Jialei; Pan, Xing.
Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016. ed. / Qiang Miao; Zhaojun Li; Ming J. Zuo; Liudong Xing; Zhigang Tian. Institute of Electrical and Electronics Engineers Inc., 2017. 7819917 (Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Relativity modeling of work motivation and human error probability based on neural network

AU - Chen, Jialei

AU - Pan, Xing

PY - 2017/1/16

Y1 - 2017/1/16

N2 - Human error behavior is jointly determined by human and environment, specially, the effect of work motivation has been emphasized to be an important factor on human performance in psychology and behavioral science. However, current Human Reliability Analysis (HRA) pay little attention to this aspect, which creates difficulties in finding the mechanisms of error behavior that could arise in the cognitive process. To fill the gap mentioned above, this paper considered work motivation factors into HRA, the hypothesis of the relationship between work motivation and Human Error Probability (HEP) in task context was given, with reference to the relationship between arousal and performance described by Yerkes-Dodson Law in psychology, then a relativity model of work motivation and HEP on different task difficulty was proposed based on neural network, and simulation experiment was carried out. The experimental results showed the work motivation-HEP relationship is of a U-shaped curve and the optimal work motivation for a difficult task is lower than an easy task.

AB - Human error behavior is jointly determined by human and environment, specially, the effect of work motivation has been emphasized to be an important factor on human performance in psychology and behavioral science. However, current Human Reliability Analysis (HRA) pay little attention to this aspect, which creates difficulties in finding the mechanisms of error behavior that could arise in the cognitive process. To fill the gap mentioned above, this paper considered work motivation factors into HRA, the hypothesis of the relationship between work motivation and Human Error Probability (HEP) in task context was given, with reference to the relationship between arousal and performance described by Yerkes-Dodson Law in psychology, then a relativity model of work motivation and HEP on different task difficulty was proposed based on neural network, and simulation experiment was carried out. The experimental results showed the work motivation-HEP relationship is of a U-shaped curve and the optimal work motivation for a difficult task is lower than an easy task.

KW - Human error probability

KW - Human reliability analysis

KW - Neural network

KW - Work motivation

KW - Yerkesdodson law

UR - https://www.scopus.com/pages/publications/85015613608

U2 - 10.1109/PHM.2016.7819917

DO - 10.1109/PHM.2016.7819917

M3 - 会议稿件

AN - SCOPUS:85015613608

T3 - Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016

BT - Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016

A2 - Miao, Qiang

A2 - Li, Zhaojun

A2 - Zuo, Ming J.

A2 - Xing, Liudong

A2 - Tian, Zhigang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th IEEE Prognostics and System Health Management Conference, PHM-Chengdu 2016

Y2 - 19 October 2016 through 21 October 2016

ER -

Chen J, Pan X. Relativity modeling of work motivation and human error probability based on neural network. In Miao Q, Li Z, Zuo MJ, Xing L, Tian Z, editors, Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016. Institute of Electrical and Electronics Engineers Inc. 2017. 7819917. (Proceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016). doi: 10.1109/PHM.2016.7819917

Relativity modeling of work motivation and human error probability based on neural network

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