Ostrovsky Tehilla, Ungermann Paul, Donkin Chris
School of Psychology, Ludwig Maximilian University, Munich, Germany.
Technical University of Munich, Munich, Germany.
Behav Res Methods. 2025 Sep 12;57(10):285. doi: 10.3758/s13428-025-02800-5.
What people think and say during experiments is important for our understanding of the human mind. However, the collection and analysis of verbal-report data in experiments are relatively costly and are thus grossly underutilized. Here, we aim to reduce such costs by providing software that collects, transcribes, and analyzes verbal-report data. Verbal data are collected using jsPsych (De Leeuw, Behavior Research Methods, 47, 1-12, 2015), making it suitable for both online and lab-based experiments. The transcription and analyses rely on classical machine-learning methods as well as deep learning approaches (e.g., large language models), making them substantially more efficient than current methods using human coders. We demonstrate how to use the software we provide in a case study via a simple memory experiment. This collection of software was designed to be modular, allowing for the update and replacement of various components with superior models, as well as the easy addition of new methods. It is our sincere hope that this approach popularizes the collection of verbal-report data in psychology experiments.
人们在实验中的所思所言对于我们理解人类思维很重要。然而，实验中言语报告数据的收集和分析成本相对较高，因此未得到充分利用。在此，我们旨在通过提供一款用于收集、转录和分析言语报告数据的软件来降低此类成本。言语数据使用jsPsych进行收集（德利乌，《行为研究方法》，第47卷，第1 - 12页，2015年），这使其适用于在线实验和基于实验室的实验。转录和分析依赖于经典机器学习方法以及深度学习方法（例如大语言模型），这使得它们比当前使用人工编码员的方法效率大幅提高。我们通过一个简单的记忆实验在案例研究中展示如何使用我们提供的软件。这套软件设计为模块化，允许用更优模型更新和替换各个组件，以及轻松添加新方法。我们真诚希望这种方法能使心理学实验中言语报告数据的收集得到普及。
