While the mechanisms underlying polarization are complex, scholars have consistently found a pervasive overestimation of perceptions of polarization to be a contributing factor. We argue that one mitigation strategy that can work at scale to address such misperceptions might be relatively straightforward: better data visualizations of cross‐party attitudes on key issues. In a large‐scale (N = 6603), international replication, we find that mode of presentation—or the manner in which data are visually presented—plays a significant role in moderating perceptions of polarization, even for longstanding, divisive issues for which partisans would likely hold strong prior beliefs. Additionally, we find the effects that different modes of presentation have on issue‐specific polarization also extend to participant beliefs about overall interparty polarization, with certain modes proving capable of not only promoting less polarized views but also enabling more accurate estimates of the extent to which political groups agree. Finally, our findings also suggest that the manner in which intergroup data are visualized may also exert influence over the degree to which political groups are essentialized—a finding with implications for not only political perception but also for apolitical social psychological phenomena such as dehumanization.

中文翻译：

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数据可视化方式如何影响和纠正对政治两极分化的（错误）看法


虽然极化背后的机制很复杂，但学者们一致发现，人们普遍高估了极化率。看法极化是一个影响因素。我们认为，一种可以大规模解决此类误解的缓解策略可能相对简单：对关键问题上的跨党派态度进行更好的数据可视化。在大规模（氮= 6603），国际复制，我们发现呈现方式或数据以视觉方式呈现的方式，在缓和两极分化的看法方面发挥着重要作用，即使是对于党派可能持有强烈先前信念的长期存在的、有分歧的问题也是如此。此外，我们发现不同的呈现方式对特定问题两极分化的影响也延伸到参与者的信念全面的政党间两极分化，事实证明某些模式不仅能够促进较少两极分化的观点，而且能够更准确地估计政治团体的共识程度。最后，我们的研究结果还表明，群体间数据可视化的方式也可能对政治群体的参与程度产生影响。本质化——这一发现不仅对政治观念有影响，而且对非人化等非政治社会心理现象也有影响。