Pitt Mark A., Myung In Jae
Dept of Psychology, Ohio State University, 1885 Neil Avenue, 43210-1222, Columbus, Ohio, USA
Trends Cogn Sci. 2002 Oct 1;6(10):421-425. doi: 10.1016/s1364-6613(02)01964-2.
How should we select among computational models of cognition? Although it is commonplace to measure how well each model fits the data, this is insufficient. Good fits can be misleading because they can result from properties of the model that have nothing to do with it being a close approximation to the cognitive process of interest (e.g. overfitting). Selection methods are introduced that factor in these properties when measuring fit. Their success in outperforming standard goodness-of-fit measures stems from a focus on measuring the generalizability of a model's data-fitting abilities, which should be the goal of model selection.
我们应该如何在认知计算模型中进行选择？虽然测量每个模型与数据的拟合程度是很常见的做法，但这还不够。良好的拟合可能会产生误导，因为它们可能源于模型的某些属性，而这些属性与它是否是对感兴趣的认知过程的近似无关（例如过拟合）。本文介绍了一些选择方法，这些方法在测量拟合时会考虑这些属性。它们在性能上优于标准拟合优度测量方法，这源于它们专注于测量模型数据拟合能力的可推广性，而这应该是模型选择的目标。
