论文标题

灵活的社会推理在无法观察到的奖励时有助于有针对性的社会学习

Flexible social inference facilitates targeted social learning when rewards are not observable

论文作者

Hawkins, Robert D., Berdahl, Andrew M., Pentland, Alex "Sandy", Tenenbaum, Joshua B., Goodman, Noah D., Krafft, P. M.

论文摘要

当个人能够从他人的成功中学习时,小组更有效地协调。但是,获得这种知识并不总是那么容易,尤其是在公众视野中成功的现实环境中。我们建议,社会推断能力可能有助于弥合这一差距,使个人可以通过可观察到的行为轨迹来更新他人对他人的知识和成功的信念。在集体传感任务中,我们将我们的社会推理模型与更简单的启发式方法进行了比较。在实验1中,我们发现,平均性能的提高是群体大小的函数,其速率比非限制模型所预测的要大。实验2介绍了人工代理,以评估个人如何有选择地依赖社会信息。实验3将这些发现概括为更复杂的奖励景观。综上所述,我们的发现提供了对个人社会认知与集体行为灵活性之间关系的见解。

Groups coordinate more effectively when individuals are able to learn from others' successes. But acquiring such knowledge is not always easy, especially in real-world environments where success is hidden from public view. We suggest that social inference capacities may help bridge this gap, allowing individuals to update their beliefs about others' underlying knowledge and success from observable trajectories of behavior. We compared our social inference model against simpler heuristics in three studies of human behavior in a collective sensing task. In Experiment 1, we found that average performance improves as a function of group size at a rate greater than predicted by non-inferential models. Experiment 2 introduced artificial agents to evaluate how individuals selectively rely on social information. Experiment 3 generalized these findings to a more complex reward landscape. Taken together, our findings provide insight into the relationship between individual social cognition and the flexibility of collective behavior.

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