使用机器学习来自动收集,转录和分析口头报告数据
Tehilla Ostrovsky1, Paul Ungermann2, Chris Donkin3
1School of Psychology, Ludwig Maximilian University, Munich, Germany. tehila.mechera-ostrovsky@psy.lmu.de.
Behavior research methods
|September 12, 2025
概括
这项研究引入了新的软件,以有效地收集,转录和分析心理实验中的口头报告数据. 这项创新旨在降低成本,并增加对人类思想有价值的见解的利用.
科学领域:
- 认知心理学 认知心理学
- 计算语言学 计算语言学
- 心理学研究方法 心理学研究方法
背景情况:
- 口头报告数据对于理解人类思维至关重要,但由于收集和分析成本高,因此未得到充分利用.
- 目前用于分析语言数据的方法耗时且昂贵,严重依赖人类编码器.
研究的目的:
- 开发和推出具有成本效益的软件,用于收集,转录和分析心理实验中的口头报告数据.
- 提高在人类思想研究中口头数据的可访问性和利用率.
主要方法:
- 利用jsPsych用于在线和实验室设置中的多功能口头数据收集.
- 采用经典的机器学习和深度学习 (大型语言模型) 进行高效的数据转录和分析.
- 通过一个涉及记忆实验的案例研究来证明软件功能.
主要成果:
- 与传统的人类编码相比,开发的软件大大降低了与口头数据分析相关的成本和时间.
- 模块化设计允许轻松更新和整合新的分析方法.
- 该软件在使用记忆实验的实践演示中被证明是有效的.
结论:
- 新软件提供了一个可扩展和高效的解决方案,用于将口头报告数据纳入心理研究中.
- 这种方法有可能普及口头数据的收集和分析,推进认知心理学.
- 这些工具有助于对人类认知和行为进行更全面的调查.
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