Suppr超能文献

使文献过度适配于一组刺激因素和数据。

Overfitting the Literature to One Set of Stimuli and Data.

作者信息

Grootswagers Tijl, Robinson Amanda K

机构信息

The MARCS Institute for Brain, Behaviour and Development, Sydney, NSW, Australia.

School of Psychology, Western Sydney University, Sydney, NSW, Australia.

出版信息

Front Hum Neurosci. 2021 Jul 8;15:682661. doi: 10.3389/fnhum.2021.682661. eCollection 2021.

Abstract

A large number of papers in Computational Cognitive Neuroscience are developing and testing novel analysis methods using one specific neuroimaging dataset and problematic experimental stimuli. Publication bias and confirmatory exploration will result in overfitting to the limited available data. We highlight the problems with this specific dataset and argue for the need to collect more good quality open neuroimaging data using a variety of experimental stimuli, in order to test the generalisability of current published results, and allow for more robust results in future work.

摘要

计算认知神经科学领域的大量论文正在使用一个特定的神经影像数据集和有问题的实验刺激来开发和测试新的分析方法。发表偏倚和验证性探索将导致对有限可用数据的过度拟合。我们强调了这个特定数据集存在的问题,并主张有必要使用各种实验刺激来收集更多高质量的开放神经影像数据,以便测试当前已发表结果的普遍性,并在未来的工作中获得更可靠的结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b87/8295535/8edf8501f203/fnhum-15-682661-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验