The complex reward structure of the social world and the uncertainty endemic to social contexts poses a challenge for modeling. For example, during social interactions, the actions of one person influence the internal states of another. These social dependencies make it difficult to formalize social learning problems in a mathematically tractable way. While it is tempting to dispense with these complexities, they are a defining feature of social life. Because the structure of social interactions challenges the simplifying assumptions often made in models, they make an ideal testbed for computational models of cognition. By adopting a framework that embeds existing social knowledge into the model, we can go beyond explaining behaviors in laboratory tasks to explaining those observed in the wild.

社会世界复杂的奖励结构和社会环境普遍存在的不确定性给建模带来了挑战。例如，在社交互动中，一个人的行为会影响另一个人的内部状态。这些社会依赖性使得很难以数学上易于处理的方式形式化社会学习问题。虽然人们很想摆脱这些复杂性，但它们是社会生活的一个决定性特征。由于社交互动的结构挑战了模型中经常做出的简化假设，因此它们为认知计算模型提供了理想的测试平台。通过采用将现有社会知识嵌入到模型中的框架，我们不仅可以解释实验室任务中的行为，还可以解释在野外观察到的行为。