将生成人工智能应用于决策的认知模型
Tyler Malloy1, Cleotilde Gonzalez1
1Dynamic Decision Making Laboratory, Department of Social and Decision Sciences, Dietrich College, Carnegie Mellon University, Pittsburgh, PA, United States.
Frontiers in psychology
|May 20, 2024
概括
生成型人工智能通过创建记忆表征和预测行为来增强决策的认知模型. 这项研究将人工智能与基于实例的学习理论相结合,以改进计算认知建模.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 决策科学 决策科学 决策科学
背景情况:
- 在决策的计算认知建模中的生成人工智能 (AI) 应用缺乏理论整合.
- 现有的模型往往专注于特定领域,没有统一的框架.
研究的目的:
- 在决策的认知模型中对生成AI应用进行分类.
- 为比较现有文献和设计未来研究提供一个框架.
- 提出和评估一个集成的生成-认知模型.
主要方法:
- 在认知决策模型中开发了生成AI的分类框架.
- 进行了一项废除研究,比较了三种实验范式的模型.
- 利用基于实例的学习理论作为模型比较的基础.
- 模拟人类的学习和决策使用视觉和自然语言刺激.
主要成果:
- 性能最好的模型整合了用于内存表示和动作预测的生成组件.
- 在决策建模中,生成模型对于记忆形成和行动预测至关重要.
- 该研究表明了整合生成和认知模型的有效性.
结论:
- 生成型人工智能为推进计算认知建模提供了巨大的潜力.
- 拟议的综合模型为决策研究提供了强有力的方法.
- 研究结果为研究人员将生成人工智能集成到认知和决策模型中提供了指导方针.
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