Implicit motives are key drivers of individual differences but are time-consuming to assess, requiring many hours of work by trained human coders. In this paper we report on the use of machine learning to automate the coding of implicit motives. We assess the performance of three neural network models on three unseen datasets in order to establish baselines for convergent, divergent, causal, and criterion validity. Results suggest that this is a promising direction to pursue in developing an automatic procedure for coding implicit motives.

中文翻译：

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隐式动机的自动编码：一种机器学习方法

内隐动机是个体差异的主要驱动力，但评估耗时，需要受过训练的人类编码人员花费大量时间。在本文中，我们报告了使用机器学习自动执行内隐动机编码的情况。为了建立收敛，发散，因果和标准有效性的基线，我们评估了三个看不见的数据集上三个神经网络模型的性能。结果表明，这是开发自动编码隐式动机程序的有前途的方向。