Ten-month-old infants infer the value of goals from the costs of actions

Infants understand that people pursue goals, but how do they learn which goals people prefer? Here, we test whether infants solve this problem by inverting a mental model of action planning, trading off the costs of acting against the rewards actions bring. After seeing an agent attain two goals equally often at varying costs, infants expected the agent to prefer the goal it attained through costlier actions. These expectations held across three experiments conveying cost through different physical path features (jump height and width; incline angle), suggesting that an abstract variable, such as ‘force’, ‘work’ or ‘effort’, supported infants’ inferences. We model infants' expectations as Bayesian inferences over utility-theoretic calculations, providing a bridge to recent quantitative accounts of action understanding in older children and adults.

婴儿能够理解人们追求目标，然而他们是如何学习人们偏好的目标呢？在本研究中，我们测试婴儿是否通过反转行动规划的内在模型，权衡行动的成本与行动带来的奖励，来解决这一问题。在观察到一个智能体以不同的成本实现两个目标时，婴儿预期智能体会偏好通过更高成本实现的那个目标。这一预期在三个通过不同的物理路径特征（跳跃高度和宽度；倾斜角度）传达成本的实验中得到了验证，表明一个抽象变量，如‘力’、‘功’或‘努力’，支持了婴儿的推理。我们将婴儿的预期建模为基于效用理论计算的贝叶斯推理，从而为近期关于儿童和成人对行动理解的定量分析提供了桥梁。