动机计算:暗示动机编码的基于变压器的自动化
Joyce S Pang1, Hiram Ring2, Kate Y Huang1
1School of Social Sciences, Nanyang Technological University, Singapore.
Journal of personality assessment
|February 26, 2026
概括
一种新的变压器模型以高精度自动化隐式动机编码,达到超过0.85的相关性. 这种"动机计算"的进步使实时动机检测和研究成为可能.
科学领域:
- 心理学 心理学 心理学
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 隐性动机与行为相关,但很难衡量.
- 以前使用标记词方法的自动化尝试是有限的.
- 机器学习为自动化动机编码提供了一个有希望的途径.
研究的目的:
- 开发和验证一种用于自动隐式动机编码的新型变压器模型.
- 评估模型的性能与既定基准和其他变压器模型相比.
- 为研究人员提供免费可访问的API,让他们可以使用该模型.
主要方法:
- 开发了一种新的变压器模型,用于自动化动机评分.
- 该模型在冬季 (1994) 培训手册的基准数据集上进行了训练和评估.
- 验证包括融合,分歧,因果和标准评估.
主要成果:
- 变压器模型与手工编码的分数 (皮尔森和类内系数) 实现了超过或接近0.85的相关性.
- 对于机器预测的分数,建立了收,分歧,因果和标准有效性的证据.
- 该模型的性能与最近的两个基于变压器的模型相比较好.
结论:
- 开发的变压器模型提供了一个高度准确和验证的方法,用于自动化隐式动机编码.
- 这项工作有助于新兴领域的生物科学领域.
- 动机计算,动机计算,动机计算.
- 促进实时动机分析.
- 自由可用的API将加速隐性动机和计算心理学方面的研究.
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