Defays Daniel
Faculty of Psychology, Logopedics and Educational Sciences, University of Liège, B4000 Liège, Belgium.
Psychol Belg. 2025 May 22;65(1):114-131. doi: 10.5334/pb.1332. eCollection 2025.
Confidence degrees assigned by respondents to their responses are generally taken at their face value. An experiment where respondents were asked to indicate twice their confidence in their (changed or unchanged) response has, however, showed that those confidences can greatly vary over time at the individual level. I propose a model that takes that variation into account and considers confidence as a latent variable - the level of knowledge - to be estimated through a true score approach. The model is defined in the special case of a scale with a given number of confidence degrees. It assumes that when faced with this type of testing requirements, a person experiences uncertainty in a way that can be represented by a finite set of partial knowledge states. It leans mainly on a conditional independence assumption. As the model is intractable under that sole assumption, additional testable and simple constraints must be imposed on the way confidence errors are distributed. The model was applied to data collected in the experiment. The results show that, under a general (population) overestimation bias, very different individual profiles are hidden with different distributions of errors. The model enables also to make predictions about one single individual by only examining his (her) calibration errors. Some errors patterns observed on the replicated data can indeed be anticipated with the proposed models.
受访者对其回答所赋予的置信度通常按其表面价值来对待。然而，一项要求受访者对其（已改变或未改变的）回答表明两次置信度的实验显示，这些置信度在个体层面上会随时间大幅变化。我提出了一个模型，该模型考虑了这种变化，并将置信度视为一个潜在变量——知识水平——通过真分数方法进行估计。该模型是在具有给定数量置信度等级的量表的特殊情况下定义的。它假定当面对这类测试要求时，一个人经历的不确定性可以用一组有限的部分知识状态来表示。它主要依赖于一个条件独立性假设。由于仅在该假设下模型难以处理，所以必须对置信度误差的分布方式施加额外的可测试且简单的约束。该模型被应用于实验中收集的数据。结果表明，在普遍的（总体）高估偏差下，不同的误差分布隐藏着非常不同的个体特征。该模型还能够仅通过检查一个人的校准误差来对其进行预测。在所提出的模型中，确实可以预测在重复数据上观察到的一些误差模式。
