Discovering Social Groups via Latent Structure Learning
Discovering Social Groups via Latent Structure Learning
复制标题
通过潜在结构学习发现社会群体
DOI：
10.1037/xge0000470
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
M. Cikara
中科院分区：
文献类型：
--
作者：
Tatiana Lau;Hillard Pouncy;S. Gershman;M. Cikara
人类在地球上的每个社会都形成了社会联盟，但我们对社会群体边界是如何学习和表现的知之甚少。我们从潜在结构的计算模型中得出预测，学习超越显式类别标签和仅仅作为社会群体表示的唯一输入的相似性。四个实验考察了(A)群体边界的证据是如何在相应的社会背景下积累起来的(即，学习他人的政治价值观)，(B)了解这些边界在多大程度上推动了一个人自己的选择以及对环境中其他主体的归因，以及(C)即使存在与潜在群体结构相矛盾的群体标签，这些潜在群体是否会影响选择。我们的结果表明，除了自己之外，人们还整合了关于环境中的代理人如何相互联系的信息，以推断社会群体结构。我们认为，这一机制是对其他社会关系理论的合理解释--例如，平衡理论。
Humans form social coalitions in every society on earth, yet we know very little about how social group boundaries are learned and represented. We derive predictions from a computational model of latent structure learning to move beyond explicit category labels and mere similarity as the sole inputs to social group representations. Four experiments examine (a) how evidence for group boundaries is accumulated in a consequential social context (i.e., learning about others’ political values), (b) to what extent learning about these boundaries drives one’s own choices as well as attributions about other agents in the environment, and (c) whether these latent groups affect choice even in the presence of group labels that contradict the latent group structure. Our results suggest that people integrate information about how agents in the environment relate to one another in addition to oneself to infer social group structure. We argue that this mechanism is a plausible explanation of other theories of social relations—for example, balance theory.