Suppr超能文献

用于启动实验的随机试验序列的自动生成。

Automatic generation of randomized trial sequences for priming experiments.

作者信息

Ihrke Matthias, Behrendt Jörg

机构信息

Department for Nonlinear Dynamics, Max Planck Institute for Dynamics and Self-Organization Göttingen, Germany.

出版信息

Front Psychol. 2011 Sep 19;2:225. doi: 10.3389/fpsyg.2011.00225. eCollection 2011.

Abstract

In most psychological experiments, a randomized presentation of successive displays is crucial for the validity of the results. For some paradigms, this is not a trivial issue because trials are interdependent, e.g., priming paradigms. We present a software that automatically generates optimized trial sequences for (negative-) priming experiments. Our implementation is based on an optimization heuristic known as genetic algorithms that allows for an intuitive interpretation due to its similarity to natural evolution. The program features a graphical user interface that allows the user to generate trial sequences and to interactively improve them. The software is based on freely available software and is released under the GNU General Public License.

摘要

在大多数心理学实验中,连续显示的随机呈现对于结果的有效性至关重要。对于某些范式而言,这并非一个微不足道的问题,因为试验是相互依存的,例如启动范式。我们展示了一款软件,它能自动为(负)启动实验生成优化的试验序列。我们的实现基于一种被称为遗传算法的优化启发式方法,由于它与自然进化相似,所以易于直观理解。该程序具有图形用户界面,允许用户生成试验序列并进行交互式改进。该软件基于免费可用软件,并根据GNU通用公共许可证发布。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/236b/3175588/c96d78585358/fpsyg-02-00225-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