Evidence map›Paper›PMID 40246995›Full record

ArticleNature human behaviour2025

Binary climate data visuals amplify perceived impact of climate change.

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

Abstract read
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In one paragraph

Article in Nature human behaviour, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Grace LiuDepartment of Computer Science, Princeton University, Princeton, NJ, USA. gliu2@andrew.cmu.edu.
Jake C SnellDepartment of Computer Science, Princeton University, Princeton, NJ, USA.
Thomas L GriffithsDepartment of Computer Science, Princeton University, Princeton, NJ, USA.ORCID http://orcid.org/0000-0002-5138-7255
Rachit DubeyDepartment of Computer Science, Princeton University, Princeton, NJ, USA. rdubey@princeton.edu.ORCID http://orcid.org/0000-0001-8216-1999

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Climate ChangeData VisualizationHumans

Identifiers

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.