人类灵活地整合社会信息,尽管在奖励中存在个体间的差异
Alexandra Witt1, Wataru Toyokawa2,3, Kevin N Lala4
1Human and Machine Cognition Lab, University of Tübingen, Tübingen 72074, Germany.
概括
人类的社会学习比以前认为的要灵活得多. 一个新的模型表明,即使目标不同,人们也可以从他人那里学习,并以现实的方式整合社会信息.
科学领域:
- 认知科学 认知科学
- 行为经济学是一种行为经济学.
- 计算神经科学是一种神经科学.
背景情况:
- 人类社会学习研究取得了进展,将社会信息纳入强化学习.
- 之前的模型通常假定相同的回报,限制了各种社会互动中的现实主义.
研究的目的:
- 开发一种更现实的社会学习模型,以解释个人之间的收益差异.
- 研究人类社会学习在整合多样化的社会信息方面的灵活性.
主要方法:
- 引入了一个社会相关的盗任务,以模拟现实的回报变化.
- 开发并测试了使用进化模拟和在线实验的社会泛化 (SG) 模型.
主要成果:
- 在预测社会学习行为方面,SG模型的表现优于现有的模型.
- 发现在SG模型中,社会信息被视为比个人观察更杂.
- 有证据表明,在资源与理性的权衡下,社会学习可以取代个人探索.
结论:
- 人类的社会学习非常灵活,能够适应不同的社会背景和个人差异.
- SG模型为理解人类如何从不同来源整合社会信息提供了更准确的框架.
- 这项研究为研究复杂环境中的适应性社会学习策略开辟了新的途径.
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