Katahira Kentaro, Oba Takeyuki, Toyama Asako
Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Central 6, 1-1-1 Higashi, Tsukuba, 305-8566, Ibaraki, Japan.
Department of Cognitive and Psychological Sciences, Graduate School of Informatics, Nagoya University, Nagoya, Japan.
Psychon Bull Rev. 2024 Dec;31(6):2465-2486. doi: 10.3758/s13423-024-02490-8. Epub 2024 May 8.
Computational modeling of behavior is increasingly being adopted as a standard methodology in psychology, cognitive neuroscience, and computational psychiatry. This approach involves estimating parameters in a computational (or cognitive) model that represents the computational processes of the underlying behavior. In this approach, the reliability of the parameter estimates is an important issue. The use of hierarchical (Bayesian) approaches, which place a prior on each model parameter of the individual participants, is thought to improve the reliability of the parameters. However, the characteristics of reliability in parameter estimates, especially when individual-level priors are assumed, as in hierarchical models, have not yet been fully discussed. Furthermore, the suitability of different reliability measures for assessing parameter reliability is not thoroughly understood. In this study, we conduct a systematic examination of these issues through theoretical analysis and numerical simulations, focusing specifically on reinforcement learning models. We note that the heterogeneity in the estimation precision of individual parameters, particularly with priors, can skew reliability measures toward individuals with higher precision. We further note that there are two factors that reduce reliability, namely estimation error and intersession variation in the true parameters, and we discuss how to evaluate these factors separately. Based on the considerations of this study, we present several recommendations and cautions for assessing the reliability of the model parameters.
行为的计算建模越来越多地被用作心理学、认知神经科学和计算精神病学的标准方法。这种方法涉及在表示潜在行为的计算（或认知）模型中估计参数。在这种方法中，参数估计的可靠性是一个重要问题。使用分层（贝叶斯）方法，即在个体参与者的每个模型参数上设置先验，被认为可以提高参数的可靠性。然而，参数估计中可靠性的特征，特别是在假设个体水平先验的情况下，如在分层模型中，尚未得到充分讨论。此外，对于评估参数可靠性的不同可靠性度量的适用性也没有得到透彻理解。在本研究中，我们通过理论分析和数值模拟对这些问题进行了系统研究，特别关注强化学习模型。我们注意到个体参数估计精度的异质性，特别是在先验情况下，会使可靠性度量偏向精度较高的个体。我们进一步注意到有两个降低可靠性的因素，即估计误差和真实参数的会话间变化，并且我们讨论了如何分别评估这些因素。基于本研究的考虑，我们提出了一些评估模型参数可靠性的建议和注意事项。
