Abstract cognitive maps of social network structure aid adaptive inference.
Abstract cognitive maps of social network structure aid adaptive inference.
复制标题
社会网络结构的抽象认知图有助于适应性推理。
DOI：
10.1073/pnas.2310801120
复制
发表时间：
2023
影响因子：
11.1
通讯作者：
FeldmanHall,Oriel
中科院分区：
文献类型：
--
作者：
Son,Jae-Young;Bhandari,Apoorva;FeldmanHall,Oriel
社交导航--比如预测八卦可能在哪里传播，或者确定哪些熟人可以帮助找到一份工作--依赖于了解人们在更大的社交社区中是如何联系的。问题是，对于大多数社交网络来说，可能的关系空间太大了，无法观察和记忆。事实上，人们对这些社会关系的认识是有偏见和容易出错的。在这里，我们揭示了这些有偏见的表示反映了一种基本的计算，该计算对个体关系进行抽象，以实现对未见关系的原则性推断。我们提出了一种网络表征理论，该理论解释了人们是如何通过直接观察来学习社会关系的推理认知地图的，结果是出现了什么样的知识结构，以及为什么将系统性偏见编码到社会认知地图中是有益的。利用模拟、实验室实验和来自真实世界网络的“现场数据”，我们发现人们将对直接关系(例如，朋友)的观察抽象为对多步骤关系(例如，朋友的朋友)的推断。这种多步骤抽象机制使人们能够发现和表示复杂的社交网络结构，提供跨各种上下文的自适应推理，包括友谊、信任和建议提供。此外，这种多步骤抽象机制统一了关于社会行为的各种令人费解的经验观察。我们的建议将认知地图理论推广到社会推理的基本计算问题，为理解复杂社会世界中预测思维的工作方式提供了一个强大的框架。
Social navigation—such as anticipating where gossip may spread, or identifying which acquaintances can help land a job—relies on knowing how people are connected within their larger social communities. Problematically, for most social networks, the space of possible relationships is too vast to observe and memorize. Indeed, people's knowledge of these social relations is well known to be biased and error-prone. Here, we reveal that these biased representations reflect a fundamental computation that abstracts over individual relationships to enable principled inferences about unseen relationships. We propose a theory of network representation that explains how people learn inferential cognitive maps of social relations from direct observation, what kinds of knowledge structures emerge as a consequence, and why it can be beneficial to encode systematic biases into social cognitive maps. Leveraging simulations, laboratory experiments, and “field data” from a real-world network, we find that people abstract observations of direct relations (e.g., friends) into inferences of multistep relations (e.g., friends-of-friends). This multistep abstraction mechanism enables people to discover and represent complex social network structure, affording adaptive inferences across a variety of contexts, including friendship, trust, and advice-giving. Moreover, this multistep abstraction mechanism unifies a variety of otherwise puzzling empirical observations about social behavior. Our proposal generalizes the theory of cognitive maps to the fundamental computational problem of social inference, presenting a powerful framework for understanding the workings of a predictive mind operating within a complex social world.