Princeton University, Princeton, New Jersey, USA.
Behav Res Methods. 2010 Feb;42(1):141-7. doi: 10.3758/BRM.42.1.141.
Studies of human memory often generate data on the sequence and timing of recalled items, but scoring such data using conventional methods is difficult or impossible. We describe a Python-based semiautomated system that greatly simplifies this task. This software, called PyParse, can easily be used in conjunction with many common experiment authoring systems. Scored data is output in a simple ASCII format and can be accessed with the programming language of choice, allowing for the identification of features such as correct responses, prior-list intrusions, extra-list intrusions, and repetitions.
研究人类记忆时,通常会生成关于回忆项目的顺序和时间的数据,但使用传统方法对这些数据进行评分既困难又耗时。我们描述了一个基于 Python 的半自动系统,该系统极大地简化了这项任务。这个名为 PyParse 的软件可以与许多常见的实验创作系统轻松结合使用。评分后的数据以简单的 ASCII 格式输出,可以使用所选编程语言访问,从而可以识别正确的反应、先前列表的干扰、额外列表的干扰和重复等特征。