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桥梁意识和AI:聊天GPT辅助的现象学分析分析
David Martínez-Pernía1, Alejandro Troncoso1, Sergio E Chaigneau1
1Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile.
这项研究表明,ChatGPT如何对定性数据进行现象学分析,为意识研究提供了可扩展的方法. 人工智能工具有效地处理人类经验,提供详细的帐户和主题组织,以获得更深入的见解.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 定性研究方法 定性研究方法
背景情况:
- 混合方法研究在大数据分析中面临挑战.
- 需要可扩展的方法来分析广泛的定性数据集.
- 在意识研究中,保持深度和细微差别至关重要.
研究的目的:
- 为了利用ChatGPT进行现象学分析.
- 探索人工智能的能力在处理定性数据的意识研究.
- 为大数据时代适应混合方法研究.
主要方法:
- 现象学数据的准备和完善.
- 使用ChatGPT对参与者体验的个人和全球分析.
- 为精确的数据提取和合成提供定制提示.
- 从基本组件构建共享经验的结构.
主要成果:
- 聊天GPT展示了对人类经验的复杂处理.
- 有效地组织主题,反映感觉强度和同情心.
- 现象学基础的数据处理强调刺激参与.
- 人工智能对主题组织的熟练程度,用于定性分析.
结论:
- 聊天GPT在意识研究中显示出详细的现象学描述的潜力.
- 人工智能工具为人类体验研究提供了精度和可扩展性.
- 需要进一步的研究来完善AI在现象学分析方面的能力.
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