自动隐性动机编码至少与人类一样准确,并且99%更快
August Håkan Nilsson1, J Malte Runge1, Adithya V Ganesan2
1Oslo Business School, Oslo Metropolitan University.
Journal of personality and social psychology
|April 10, 2025
概括
新的人工智能模型实现隐性动机评估的自动化,大大减少了研究人员研究人格和动机的编码时间和精力. 这一进步有望带来隐性动机研究的复兴.
科学领域:
- 心理学 心理学 心理学
- 计算语言学 计算语言学
- 人格科学 人格科学 人格科学
背景情况:
- 隐含的动机,影响行为和情绪的无意识需求,是人格研究的核心,但传统上需要资源密集的专家编码.
- 目前的评估方法依赖于叙事响应的手动编码,耗时且限制了研究的可扩展性.
研究的目的:
- 使用机器学习和大型语言模型开发高质量,用户友好的隐性动机模型.
- 自动化对权力,成就和归属需求的评估,使研究人员更容易获得.
主要方法:
- 在大型数据集 (N = 85,028个句子) 上训练机器学习模型来编码隐含的动机.
- 与人类编码器和实验数据相比验证的模型性能,包括因果有效性评估.
- 采用主题和词嵌入分析,以确保特定动机语言的面部有效性.
主要成果:
- 个人级别的评估显示与持久数据有很强的趋同 (ICC{1,1) = .85.89).
- 在85%的案例中,人类编码者同意模型评估,而模型和原始编码者不同意.
- 模型通过复制关于隐性动机唤起的实验研究的结果来证明因果有效性.
结论:
- 开发的AI模型提供了一个可靠和高效的替代方案或补充人类编码隐性动机评估.
- 这些模型大大减少了编码时间 (超过99%) 和认知力度,促进了更广泛的研究应用.
- 提供了一个免费,用户友好的R包框架,以支持研究人员应用这些自动编码模型.
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