Evidence map›Paper›PMID 39982561›Full record

ArticleCognitive research: principles and implications2025

Narrative visualizations: Depicting accumulating risks and increasing trust in data.

Madison Fansher, Logan Walls, Chenxu Hao, Hari Subramonyam, Aysecan Boduroglu, Priti Shah, Jessica K Witt

Abstract read
In one paragraph

Article in Cognitive research: principles and implications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

7 authors.

Madison FansherDepartment of Physical Medicine & Rehabilitation, University of Michigan, Ann Arbor, USA. mfansher@umich.edu.ORCID 0000-0003-0009-4337
Logan WallsDepartment of Psychology, University of Michigan, Ann Arbor, USA.
Chenxu HaoDepartment of Intelligent Systems, Delft University of Technology, Delft, The Netherlands.
Hari SubramonyamGraduate School of Education, Stanford University, Stanford, USA.
Aysecan BodurogluDepartment of Psychology, Koç University, Istanbul, Turkey.
Priti ShahDepartment of Psychology, University of Michigan, Ann Arbor, USA.
Jessica K WittColorado State University, Fort Collins, USA.ORCID 0000-0003-1139-1599

Funding

National Science Foundation #2030059
6 · The paper itself

Abstract

In contexts where people lack prior knowledge and risk awareness-such as the COVID-19 pandemic-even truthful visualizations of data can seem surprising. This can lead people to mistrust the veracity of the data and to discount it, leading to poor risk decisions. In this work, we illustrate how narrative visualizations can achieve a balance between the benefits of three common risk communication mediums (static visualizations, interactive simulations, and affect-laden anecdotes). We demonstrate empirically that viewing a narrative visualization mitigates the reduced concern induced by a static visualization when communicating COVID-19 transmission risk (Study 1). Through mediation analysis, we show that narrative visualizations are more effective than static visualizations at increasing concern about large risks because they increase one's perceived understanding and trust in data (Study 2). We argue that narrative visualizations deserve attention as a distinct class of visualizations that have the potential to be powerful tools for scientific communication (especially in contexts where data are surprising, and empiricism is important).

Indexed as

CommunicationCOVID-19NarrationTrustAdultFemaleHumansMaleRiskYoung AdultData visualizationMisinformationRisk perception

Identifiers

PMID39982561
PMCPMC11845644

What OpenQuestion holds

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LicenceCC BY
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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.