Predicting honest behavior based on Eysenck personality traits and gender: an explainable machine learning study using SHAP analysis
IntroductionThis study bridges a critical gap in aviation safety research by examining how Eysenck personality traits (Neuroticism, Psychoticism, Extraversion) and gender predict dishonest behavior in high-risk aviation contexts. While prior studies have focused on the Big Five and HEXACO models in...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1525606/full |
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| Summary: | IntroductionThis study bridges a critical gap in aviation safety research by examining how Eysenck personality traits (Neuroticism, Psychoticism, Extraversion) and gender predict dishonest behavior in high-risk aviation contexts. While prior studies have focused on the Big Five and HEXACO models in ethical decision-making, empirical applications of the Eysenck framework to honesty prediction remain scarce-particularly in aviation, where dishonest acts (e.g., underreporting safety incidents) carry severe public safety consequences.MethodsWe collected behavioral data from 102 flight and air traffic control cadets using a coin-toss task. Explainable machine learning (XGBoost) was employed to model nonlinear relationships between personality, gender, and honesty. Model performance was evaluated via AUC, with SHAP analysis identifying key predictors.ResultsXGBoost achieved superior predictive accuracy (AUC = 0.802). SHAP analysis revealed: (1) neuroticism as the strongest predictor of dishonesty; (2) significant gender differences (higher dishonesty rates in males); and (3) threshold effects for Psychoticism and Extraversion.DiscussionThis work makes three key contributions: (1) first systematic application of the Eysenck model to aviation honesty prediction; (2) identification of gender as a critical moderating variable; and (3) development of a SHAP-driven interpretable framework that connects machine learning outputs with psychological theory. Practically, these findings enable data-driven screening of cadets' honesty tendencies during recruitment, facilitating targeted interventions for safer aviation operations. |
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| ISSN: | 1664-1078 |