Pitt Mark A, Kim Woojae, Myung In Jae
Department of Psychology, Ohio State University, Columbus, Ohio 43210, USA.
Psychon Bull Rev. 2003 Mar;10(1):29-44. doi: 10.3758/bf03196467.
Which quantitative method should be used to choose among competing mathematical models of cognition? Massaro, Cohen, Campbell, and Rodriguez (2001) favor root mean squared deviation (RMSD), choosing the model that provides the best fit to the data. Their simulation results appear to legitimize its use for comparing two models of information integration because it performed just as well as Bayesian model selection (BMS), which had previously been shown by Myung and Pitt (1997) to be a superior alternative selection method because it considers a model's complexity in addition to its fit. In the present study, after contrasting the theoretical approaches to model selection espoused by Massaro et al. and Myung and Pitt, we discuss the cause of the inconsistencies by expanding on the simulations of Massaro et al. Findings demonstrate that the results from model recovery simulations can be misleading if they are not interpreted relative to the data on which they were evaluated, and that BMS is a more robust selection method.
应该使用哪种定量方法在相互竞争的认知数学模型中进行选择呢？马萨罗、科恩、坎贝尔和罗德里格斯（2001年）赞成使用均方根偏差（RMSD），选择对数据拟合最佳的模型。他们的模拟结果似乎使将其用于比较两种信息整合模型变得合理，因为它的表现与贝叶斯模型选择（BMS）一样好，而明和皮特（1997年）此前已表明贝叶斯模型选择是一种更优越的替代选择方法，因为它除了考虑模型的拟合度之外，还考虑了模型的复杂性。在本研究中，在对比了马萨罗等人以及明和皮特所支持的模型选择理论方法之后，我们通过扩展马萨罗等人的模拟来讨论不一致的原因。研究结果表明，如果不相对于其评估所依据的数据来解释，模型恢复模拟的结果可能会产生误导，并且贝叶斯模型选择是一种更稳健的选择方法。
