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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数据的可视化方式如何影响和纠正对政治两极分化的（错误）认知


虽然两极分化的潜在机制很复杂，但学者们一直发现，对两极分化的看法普遍高估是一个促成因素。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：更好地将数据可视化，了解跨党派在关键问题上的态度。在大规模 （N = 6603） 的国际复制中，我们发现呈现模式——或数据的视觉呈现方式——在缓和对两极分化的看法方面发挥着重要作用，即使对于党派可能持有强烈先验信念的长期、分裂的问题也是如此。此外，我们发现不同的呈现模式对特定问题两极分化的影响也延伸到参与者对整体党际两极分化的看法，某些模式被证明不仅能够促进较少的两极分化观点，还可以更准确地估计政治团体的同意程度。最后，我们的研究结果还表明，群体间数据的可视化方式也可能对政治群体的本质化程度产生影响——这一发现不仅对政治认知有影响，而且对非政治社会心理现象（如非人化）也有影响。