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根据战争游戏中的行为来识别对手的神经障碍

Sihui Ge^1,2, Sihua Lyu^1,2, Yazheng Di^1,2

¹Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.

Behavioral sciences (Basel, Switzerland)

|August 28, 2025

概括

这项研究使用机器学习和行为数据来预测竞争环境中的神经病, 克服传统自我报告问卷的局限性. 开发的方法显示出在现实世界中客观心理评估的前景.

科学领域:

心理学
机器学习
行为科学

背景情况:

传统的神经病评估依赖于自我报告问卷, 在对抗性环境中受限且容易产生偏见.
需要客观和实用的方法来评估神经病症,特别是在竞争环境中.

研究的目的:

利用行为数据开发和验证基于机器学习的方法来预测神经病.
克服主观自我报告措施在评估神经病的局限性.

主要方法:

分析了MiaoSuan战斗平台上的167名参与者的行为记录.
确定与神经病相关的关键行为特征,并开发预测模型.
使用"五大"神经症分量表的8项神经症分量表进行评估.

主要成果:

最有效的模型是LinearSVR, 从行为数据中有效推断神经症水平.
在预测和自我报告的神经病得分之间达到了0.606的相关性.
测试重复测试的可靠性为0.516,表明模型的稳定性.

结论:

行为数据可以有效地用于预测神经病症,为自我报告措施提供可行的替代方案.
开发的方法在竞争环境中的心理评估中具有实际应用.
未来的研究应专注于改进特征选择和扩大基于行为评估的应用场景.

关键词:

游戏行为机器学习神经病症战争游戏

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