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Preregistration of Modeling Exercises May Not Be Useful

机译：预先登记的建模练习可能不是有用的

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This is a commentary on Lee et al.'s (2019) article encouraging preregistration of model development, fitting, and evaluation. While we are in general agreement with Lee et al.'s characterization of the modeling process, we disagree on whether preregistration of this process will move the scientific enterprise forward. We emphasize the subjective and exploratory nature of model development, and point out that "under-modeling" of data (relying on black-box approaches applied to data without data exploration) is as big a problem as "over-modeling" (fitting noise, resulting in models that generalize poorly). We also note the potential long-run negative impact of preregistration on future generations of cognitive scientists. It is our opinion that preregistration of model development will lead to less, and to less creative, exploratory analysis (i.e., to more under-modeling), and that Lee at al.'s primary goals can be achieved by requiring publication of raw data and code. We conclude our commentary with suggestions on how to move forward.
机译：这是一个评论李et al。(2019)文章鼓励预先注册模型开发、配件和评估。与李等一般协议。’s建模过程的描述,我们不同意是否预先登记将科学的企业过程前进。探索性模型开发的本质指出,“under-modeling”数据(依赖黑箱方法应用到数据上数据探索)一样大的一个问题(合适的噪声,导致“over-modeling”模型,推广不佳)。潜在的长期的负面影响预先登记的后代认知科学家。预先登记的发展将导致模型少,和更少的创造性、探索性分析(例如,更多under-modeling)和李在基地的主要目标可以通过要求实现出版的原始数据和代码。评论和建议前进。

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