The Haas School of Business, The University of California, Berkeley, Berkeley, CA, 94720, USA.
Network Dynamics Group, The University of Pennsylvania, Philadelphia, PA, 19106, USA.
Nat Commun. 2021 Jan 12;12(1):327. doi: 10.1038/s41467-020-20037-y.
Individuals vary widely in how they categorize novel and ambiguous phenomena. This individual variation has led influential theories in cognitive and social science to suggest that communication in large social groups introduces path dependence in category formation, which is expected to lead separate populations toward divergent cultural trajectories. Yet, anthropological data indicates that large, independent societies consistently arrive at highly similar category systems across a range of topics. How is it possible for diverse populations, consisting of individuals with significant variation in how they categorize the world, to independently construct similar category systems? Here, we investigate this puzzle experimentally by creating an online "Grouping Game" in which we observe how people in small and large populations collaboratively construct category systems for a continuum of ambiguous stimuli. We find that solitary individuals and small groups produce highly divergent category systems; however, across independent trials with unique participants, large populations consistently converge on highly similar category systems. A formal model of critical mass dynamics in social networks accurately predicts this process of scale-induced category convergence. Our findings show how large communication networks can filter lexical diversity among individuals to produce replicable society-level patterns, yielding unexpected implications for cultural evolution.
个体在对新异和模糊现象进行分类时存在广泛差异。这种个体差异导致认知和社会科学领域的一些有影响力的理论提出,在大型社会群体中进行交流时,类别形成会出现路径依赖性,这可能导致不同群体朝着不同的文化轨迹发展。然而,人类学数据表明,大型独立社会在一系列主题上始终形成高度相似的类别系统。由在世界分类方面存在显著差异的个体组成的多样化群体,如何能够独立构建相似的类别系统?在这里,我们通过创建一个在线“分组游戏”来实验性地研究这个难题,在这个游戏中,我们观察小群体和大群体如何共同构建一系列模糊刺激的类别系统。我们发现,个体和小群体产生的类别系统差异很大;然而,在具有独特参与者的独立试验中,大群体始终趋向于高度相似的类别系统。社会网络中临界质量动态的正式模型准确地预测了这种由规模引起的类别趋同过程。我们的研究结果表明,大型通信网络如何在个体之间过滤词汇多样性,从而产生可复制的社会层面模式,为文化进化带来了意想不到的影响。