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二元气候数据可视化增强了人们对气候变化影响的感知。

Binary climate data visuals amplify perceived impact of climate change.

作者信息

Liu Grace, Snell Jake C, Griffiths Thomas L, Dubey Rachit

机构信息

Department of Computer Science, Princeton University, Princeton, NJ, USA.

Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA.

出版信息

Nat Hum Behav. 2025 Apr 17. doi: 10.1038/s41562-025-02183-9.

Abstract

For much of the global population, climate change appears as a slow, gradual shift in daily weather. This leads many to perceive its impacts as minor and results in apathy (the 'boiling frog' effect). How can we convey the urgency of the crisis when its impacts appear so subtle? Here, through a series of large-scale cognitive experiments (N = 799), we find that presenting people with binary climate data (for example, lake freeze history) significantly increases the perceived impact of climate change (Cohen's d = 0.40, 95% confidence interval 0.26-0.54) compared with continuous data (for example, mean temperature). Computational modelling and follow-up experiments (N = 398) suggest that binary data enhance perceived impact by creating an 'illusion' of sudden shifts. Crucially, our approach does not involve selective data presentation but rather compares different datasets that reflect equivalent trends in climate change over time. These findings, robustly replicated across multiple experiments, provide a cognitive basis for the 'boiling frog' effect and offer a psychologically grounded approach for policymakers and educators to improve climate change communication while maintaining scientific accuracy.

摘要

对于全球大部分人口而言,气候变化表现为日常天气的缓慢、渐进式变化。这使得许多人将其影响视为微不足道,从而导致冷漠(即“温水煮青蛙”效应)。当气候变化的影响显得如此微妙时,我们如何传达这场危机的紧迫性呢?在此,通过一系列大规模认知实验(N = 799),我们发现,与连续数据(例如平均温度)相比,向人们展示二元气候数据(例如湖泊结冰历史)会显著增加人们对气候变化影响的感知(科恩d值 = 0.40,95%置信区间0.26 - 0.54)。计算建模和后续实验(N = 398)表明,二元数据通过制造突然变化的“错觉”来增强感知影响。至关重要的是,我们的方法并非涉及选择性的数据呈现,而是比较不同数据集,这些数据集反映了气候变化随时间的等效趋势。这些在多个实验中得到有力重复验证的结果,为“温水煮青蛙”效应提供了认知基础,并为政策制定者和教育工作者提供了一种基于心理学的方法,以便在保持科学准确性的同时改善气候变化的传播。

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