AI 中文总结
该研究提出理性心智化模型，通过推理智能体目标与行动信息性权衡社会、非社会学习的效用，用新游戏验证其可定量捕捉人类学习策略的权衡，揭示选择性社会学习受心智理论指导以实现效用最大化。
AI 中文摘要
社会学习是智能体从他人处学习世界的强大机制。然而，人类有时会选择直接经验而非社会学习，因为社会学习会带来时间和认知资源成本。人们如何平衡社会与非社会学习？我们提出了用于社会学习决策的理性心智化模型（Rational Mentalizing model），该模型通过推理另一个智能体的目标及其未来行动的信息性来估计社会学习的效用，随后权衡社会学习与非社会学习的效用。我们使用一种新游戏开展研究，该游戏中玩家可选择观察其他智能体或探索环境，结果表明理性心智化模型能够定量捕捉人类在这些策略间的权衡。这些发现表明，选择性社会学习受服务于效用最大化的“心智理论”（Theory of Mind）指导。
英文摘要
Social learning is a powerful mechanism through which agents learn about the world from others. However, humans sometimes choose direct experience over social learning, which can carry time and cognitive resource costs. How do people balance social and non-social learning? We propose a Rational Mentalizing model of the decision to engage in social learning. This model estimates the utility of social learning by reasoning about another agent's goal and the informativeness of their future actions. It then weighs the utility of social learning against the utility of non-social learning. Using a novel game where players choose between observing other agents or exploring the environment, we show that the Rational Mentalizing model can quantitatively capture human trade-offs between these strategies. These findings suggest that selective social learning is guided by 'Theory of Mind' in the service of utility maximization.
Comments35 pages, includes supplementary information
