Deliberate attempts to portray oneself in an unrealistic manner are commonly encountered in the administration of personality questionnaires. The main aim of the present study was to explore whether mouse tracking temporal indicators and machine learning models could improve the detection of subjects implementing a faking-good response style when answering personality inventories with four choice alternatives, with and without time pressure. A total of 120 volunteers were randomly assigned to one of four experimental groups and asked to respond to the Virtuous Responding (VR) validity scale of the PPI-R and the Positive Impression Management (PIM) validity scale of the PAI via a computer mouse. A mixed design was implemented, and predictive models were calculated. The results showed that, on the PIM scale, faking-good participants were significantly slower in responding than honest respondents. Relative to VR items, PIM items are shorter in length and feature no negations. Accordingly, the PIM scale was found to be more sensitive in distinguishing between honest and faking-good respondents, demonstrating high classification accuracy (80–83%).

在人格问卷的管理中经常会遇到故意以不切实际的方式描绘自己的尝试。本研究的主要目的是探索鼠标跟踪时间指标和机器学习模型是否可以在使用四种选择方案（有时间压力和没有时间压力的情况下）回答人格清单时改善对实施虚假反应风格的受试者的检测。共有 120 名志愿者被随机分配到四个实验组之一，并要求通过计算机鼠标对 PPI-R 的良性反应（VR）效度量表和 PAI 的积极印象管理（PIM）效度量表做出反应。实施了混合设计，并计算了预测模型。结果表明，在 PIM 量表上，假装好的参与者的反应速度明显慢于诚实的受访者。相对于 VR 项目，PIM 项目的长度更短，并且没有否定。因此，发现 PIM 量表在区分诚实和虚假的受访者方面更敏感，表现出较高的分类准确度（80-83%）。