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健康还是味道? 研究人类的食物决策模式和大型语言模型
1Department of Psychology, Soochow University, Suzhou, China.
Appetite
|February 21, 2026
概括
大型语言模型 (LLM) 在模拟人类的食物选择方面表现有前途,但在属性加权方面有所不同. LLM表现出增强的自我控制,为人工智能驱动的饮食干预提供了洞察力.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 行为经济学是一种行为经济学.
背景情况:
- 大型语言模型 (LLM) 越来越多地用于模拟人类决策.
- 它们模拟基于价值的复杂选择的能力,比如考虑到健康和味道的食物选择,还不太清楚.
研究的目的:
- 为了比较两个领先的LLM (GPT-4o,Gemini 1.5 Pro) 与人类参与者的食物决策模式.
- 研究LLM如何在基于价值的决策中权衡竞争属性 (健康,品味).
- 探索LLM和人类之间自我控制机制的相似之处和差异.
主要方法:
- 一项基于价值的食品决策任务,涉及126名中国大学生.
- 通过使用人口统计和食品评估数据 (健康,味道,偏好) 来模拟参与者的决策.
- 对LLM和人类参与者的属性权重和自我控制比例的分析.
主要成果:
- GPT-4o加权的健康属性比人类更重要,他们优先考虑了味道.
- 双子1.5 Pro的属性权重比GPT-4o更低.
- 与人类参与者相比,这两位LLM都表现出更好的自我控制能力.
- 味觉权重与自我控制有负相关;健康权重与所有药物有正相关.
结论:
- 尽管缺乏情感经验,但LLM部分反映了人类的自我控制机制.
- 属性权重差异突出显示,LLM并不能完全复制人类食品决策.
- 这些发现为机器心理学和人工智能辅助饮食干预措施的发展提供了信息.
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