Palmeri Thomas J
Vanderbilt University.
Comput Brain Behav. 2019;2(3-4):193-196. doi: 10.1007/s42113-019-00041-2. Epub 2019 Jul 26.
The target article, "Robust Modeling in Cognitive Science", proposes a number of recommended practices in computational modeling in response to the growing "crisis of confidence" facing many scientific disciplines, including psychology and neuroscience. Those of us who do modeling, write about modeling, teach modeling, and mentor modelers worry deeply about best practices and any new suggestions for making modeling more transparent, trusted, and robust is welcome. Many of the recommendations seem uncontroversial. My commentary focuses on forms of preregistration and postregistration, which constitute three of the four key ideas highlighted as take-home recommendations at the conclusion of the target article. I have chosen to consider these recommendations by reflecting on my own past experiences developing new models and modeling approaches.
目标文章《认知科学中的稳健建模》针对包括心理学和神经科学在内的许多科学学科面临的日益严重的“信心危机”，提出了一些计算建模方面的推荐做法。我们这些从事建模、撰写建模文章、教授建模以及指导建模人员的人，都深切关注最佳实践，任何关于使建模更具透明度、可信度和稳健性的新建议都是受欢迎的。许多建议似乎并无争议。我的评论集中在预注册和后注册的形式上，这是目标文章结尾作为重点推荐的四个关键理念中的三个。我选择通过反思自己过去开发新模型和建模方法的经历来思考这些建议。
