Abstract
Uncertainty is intrinsic to scientific measurement, modelling, and forecasting, yet data visualization practices typically frame uncertainty communication as a task-oriented problem requiring statistical literacy. As a result, uncertainty is rarely leveraged as a resource for engagement, curiosity, or learning, especially for non-expert and non-motivated audiences. This paper examines uncertainty communication through the lens of data visualization design, drawing on cognitive psychology and aesthetic theory. Intuitive, gist-based encodings can reduce cognitive load, support pre-attentive processing, and foster reflective engagement. Adopting such encodings more broadly could advance uncertainty literacy and enable visualizations that are both epistemically rigorous and experientially engaging. Achieving this requires closer integration between statistical thinking and design practice.
1 Introduction
All measurements, projections, forecasts, and scientific statements that scientific activity uses or produces carry uncertainty. Samples capture only fragments of reality, instruments have limited accuracy and precision, models rest on assumptions and parameterizations, and forecasts accumulate the compound uncertainty of all preceding steps (both technical and epistemic). We need to calculate uncertainty in science to determine the reliability of a measurement, enable valid comparisons between results, and ensure that data is used to make sound decisions. Uncertainty is intrinsic to scientific work and shapes the meaning of its outputs, so understanding it is essential to interpreting scientific results ().
This has two practical consequences: uncertainty must be communicated as part of knowledge transfer, and recipients must be able to interpret that information.
Research on uncertainty communication has explored and debated both aspects at multiple levels. The common point of view has been to design strategies that match the user’s capacity to the communication purpose; a purpose that is commonly framed in terms of task performance and decision making of a readily interested audience. For communication goals led by engagement and inspiration, uncertainty is rarely considered as conduit to generate interest.
This piece examines these ideas in the context of data visualization.
2 Is anyone uncertainty literate?
The central debate about communicating data uncertainty is driven by concerns about the literacy of the receiver.
The statistical and epistemically sophistication behind uncertainty estimations are complex. At the very technical, the evaluation and expression of uncertainty need the support of formal guidelines that center on the numerical calculation (; ; ). The field is wide and there are several techniques to approach uncertainty evaluation (). Mismatched literacy between experts is an issue, rooted in technical approaches that serve different disciplines. This brings up the need for harmonization between disciplines in multidisciplinary studies, like IPCC (), to discuss communication supporting decision-making strategies (; ), and publish guidelines, recommendations and revision addressing specific professional ambits (; ; OSR, 2022; Yanai et al., 2021).
Literacy is widely discussed for the communication of data uncertainty to non-experts, from whom we can hardly expect such nuanced and niche level of understanding that not even experts can manage. Discussions include the role of media, and the effect that uncertainty communication has on science trust from the general public. Research shows that technical and quantified uncertainty is more accepted than consensus or vague statements (; ) but also that acceptance of the information depends on prior beliefs ( and refs within) and that the impact is sensitive to receiver and format (; Stavrova et al., 2024; Van Der Bles et al., 2019). Despite calls to authors to embrace the inclusion of uncertainty to stay truthful ( and refs within), all the previous results serve to the reported hesitancy to visualize uncertainty to the public ().
Data uncertainty literacy is mostly studied and assessed as a task performing problem (). It means situations where a motivated user needs to evaluate specific information. For example, knowing when the bus will arrive, determining whether my location is hurricane safe, or assessing whether statistical differences exist.
3 The data visualization toolbox for uncertainty communication
Data visualization can help to easy task performance, that is, to improved accuracy and speed of the data understanding. It is a particularly difficult challenge with such a complex concept as uncertainty. This has been the focus of the work from the uncertainty visualization research community, a field that combines statistical knowledge, cognitive science, and visual design.
In technical or scientific heavy contexts, uncertainty is typically visualised with techniques from statistics like error bars, box plots, confidence intervals, or distribution functions. These are specialized visual devices that assume a certain level of statistical literacy. This default toolbox has been evaluated and advanced during last decades. The uncertainty visualization community has looked into the errors and reasons for misinterpretation and come up with best practices. Generic practical and theoretical standards are summarised in recent works (; ; Padilla et al., 2022; Padilla et al., 2021a), and paralleled with field-specific works like bioinformatics (Weiskopf, 2022), biological data targeted to decision makers (), meteorology (), and thesis (; Yu, 2018). Following the lead of research, best practices have reached generic resources (e.g., Wilke, 2019).
The guiding criteria are reducing the user’s cognitive load and avoid them resorting to heuristics and subconscious mental shortcuts. This is achieved by minimizing the numeracy requirements () and ensuring congruence with salient features of information, effectively integrating understanding of uncertainty into pre-attentive processes (Padilla et al., 2021a).
4 Connect with the concept
Drawing on MacEachren’s semiotic framework, (; ), a helpful distinction within the toolbox is between extrinsic signifiers, which represent uncertainty using additional geometric objects or marks (error bars, hatched overlays, glyphs, object contours), and intrinsic signifiers, where uncertainty is embedded within the visual channel of the data itself (desaturation, fuzziness/blur, transparency, texture, sketchiness). An example of this two approaches is the box plot vs. the density strip ().
This second family of techniques relies on instant cognitive processing through global, pre-attentive features. In doing so, it anchors uncertainty communication in the gist extraction phase of visual perception, when observers form an immediate impression of the overall scene (sensu Oliva and Torralba, 2006). This holistic first-pass perception can support the interpretation of visual metaphors by providing an immediate structural or semantic frame for comparison. In this sense, global properties map naturally onto visual metaphors to ensure that the viewer’s first impression aligns with the statistical reality of the data. This is how intrinsic approaches connect with the core idea and visual gist of what data uncertainty represents: doubt, lack of clarity, and weakened signal strength. It is helpful to think in term of signal (data) and noise (uncertainty), where “data value’s visual saliency is proportional to its certainty” (; ). Signal suppression techniques support intuitive interaction and spontaneous interpretation because they embed cognitive affordance, that is, they lower the cognitive load to make sense of the data (; Padilla et al., 2021b).
These methods have been evaluated for, e.g., biomolecular structures (Sterzik et al., 2025), borderization in maps (), and are the basis for novel approaches like visual entropy (), or value suppressing palettes ().
The implementation of related data uncertainty visualization strategies is getting easier with specific codes developed to such effect. Recent examples are the Rpackage “ggdibbler” () (based on “distributional” by O’Hara-Wild et al. (2026)), which offers a flexible tool for uncertainty visualization that handles any plot, data, and distribution type. And the “bootplot” Phyton package (Petek et al., 2025) which generates hypothetical outcome plots and animations via bootstrap method of the original data.
5 Connect with the user
Addressing a non-expert audience that is not a priori motivated calls for visualization strategies that do not assume literacy, and also avoid the pervasive deterministic construal errors (). Such are the intrinsic, metaphor-laden intuitive visual representations of uncertainty (Section 4), where the visual attribute immediately communicates a statistical reality without requiring a user to read a legend or have prior statistical training.
In addition, if there is not a priori motivation, we need to engage the audience first, and capture their curiosity as a prelude to conveying the central message. A way of achieving this is by leveraging perceptual curiosity, that is, the drive to investigate novel or pleasant visual stimuli (as per ) as a door to epistemic curiosity (as per ). This is akin to a “welcome gesture” concept, widely used, for example, in science journalism (; ). It works as a narrative hook that transforms passive onlookers into information seekers.
The WWF Japan campaign on dwindling wildlife populations () illustrates a design choice that achieves both perceptual curiosity and immediate intuitive understanding, in this case, of conservation statistics. In their species portraits, the concept of disappearance is mapped to the destruction of visual resolution producing image blurriness. This blurriness was created via pixelation proportional to current population size, allowing viewers to grasp the severity of the decline at a glance. Another example is the work that prompted the writing of this piece, my own image presented to the VIZBI2025 image contest (Figure 1), where the visual element that creates interest also holds the core of the information.
FIGURE 1
These works aim to use aesthetic qualities, beauty, visual appeal, or sensory allure, to capture immediate attention and create an entry point for reflective cognitive processes, that should eventually lead to assimilation of new information. This mechanism is captured in the PIA-Pleasure-Interest-Model of Aesthetic Liking (; ) by proposing a dual-process account in which aesthetic pleasure can arise from both fluent, effortless processing and from the interest generated by disfluency. Unexpected or complex stimuli can be rewarding when their ambiguity is resolved, satisfying curiosity and deepening understanding. These mechanisms can be framed in terms familiar to both cognitive science and the visual arts: expectations, predictions, and the brain’s drive to minimize prediction error. Predictive-processing accounts of aesthetics (; Van de Cruys and Wagemans, 2011; Van de Cruys et al., 2024; ; ) describe aesthetic pleasure as emerging from the reduction of prediction error, the “Aha!” moment when ambiguity resolves. This view aligns with broader evolutionary accounts of fitness-maximizing perception (Schaffner et al., 2023). It also resonates with artistic practice, where techniques that gently violate expectations can evoke curiosity or awe without aiming for the full spectrum of aesthetic emotions (Stamkou et al., 2024).
Taken together, these perspectives suggest that it should be possible to design data uncertainty visualizations that incorporate aesthetic qualities (richness, subtle complexity, or mild expectation-violation) to spark interest and encourage viewers to linger. Such designs can support sense-making by aligning with how viewers naturally resolve ambiguity, offering an experience that is both engaging and conducive to absorbing the underlying information. In doing so, the visualization supports an enjoyable experience that meets users where they are.
6 Discussion
The search for an aesthetic quality that “intuitively convey a sense of uncertainty” approaches us to the resources used in the fine arts (). Art conveys uncertainty through ambiguity, complexity, coexisting meaning, expectation violation, or visibility of process hesitation. Intrinsic signifiers transmit uncertainty via ambiguous edges via e.g., blur or sketchiness. The example in Figure 1 transmits uncertainty using ambiguous spatial location of the date of interest and the texture it generates together with the illustrative elements.
The intentional incorporation of visual complexity would work against efficient processing of a data visualization, as mention in Section 3, because it introduces clutter, increases perceptual load, and slows rapid, accurate data interpretation (). As discussed, this is especially true for motivated users who approach visualization with a specific task in mind, but as suggested in Section 5, it may be an asset for the engagement of non-expert users. Hullman et al. (2011) already described how ‘visual difficulties’ promote active processing and engagement and anchors the attention of a non-motivated user.
Complexity can also be conceptual, inherent to the information. We can choose to use complexity at any step of the process and, as Windhager et al. (2024) propose, we can view complexity as an element of information design. Taking again the example in Figure 1, visual complexity plays the interest arising role, and at the same time the conceptual complexity of uncertainty is preserved. This follows from the decision to connect with the gist and the refusal to simplify the information (Figure 2). The amount of ambiguity rendered corresponds with the actual estimated uncertainty range.
FIGURE 2
Being at the edge of artistical expression benefits from the visual and cognitive lure found in art. But being complex just for the sake of artistry is not the point, passing of knowledge is non-negotiable. Aligning with the cognitive models introduced in Section 5, complexity drives interest if resolvable, therefore we are aiming at a balance point. A balance between order and complexity is important for aesthetic appreciation (Van Geert and Wagemans, 2020). Also, whether a data visualization will be remembered as image or information depends on the relative amount of graphical elements (; ). Caution is also advised regarding the addition of relatable illustrations intended as a welcoming anchor (the foxes in Figure 1). As () shows emotional arousing visuals elevate the perception of complexity of images.
I have argued that intuitive encoding and aesthetic lure offer accessible ways to communicate data uncertainty to non-expert audiences. By removing technical and cognitive demands, they are suited to become default visualization practice. That alone, making data uncertainty visible, would advance uncertainty literacy, and repeated exposure would increase familiarity, preference, and visual comfort, gradually normalizing data uncertainty and its visualization.
For this practice to be widespread, statistical, design, and art understanding should overlap. This is not always the case. Data visualization exists at the crossroads of multiple disciplines, each with its own norms, incentives, and literacies, conditions that can foster siloed practices. At the extremes, scientists do not incorporate artistical creativity and design thinking, and designers are not statistically literate. This is akin to the underlying tension between statistical graphics and information visualization discussed in the exchange between and . As also generically discussed in () for the art-science intersection. The disciplinary gap has been implicitly or explicitly discussed in the field of uncertainty visualization, as in discussing the disconnect where statisticians and designers do not share a common language.
Discipline disconnect appears at many levels. For example, show, the designer tendency to outweigh beauty over science and/or lack or data literacy are behind some of the design flaws produced by “everyday designers”. And discuss the disconnect in college-level data visualization courses, with a survey that shows that courses focus on design topics, whereas very few touch on statistical principles. Emphasis on the need for interdisciplinary collaboration is also voiced in , who report on the existing gap in digital humanity projects, which rarely implement uncertainty-aware visualization designs.
Overall, these insights point to the need for a more integrated practice, one in which statistical rigor, design literacy, and appreciation for aesthetic are mutually reinforcing components of effective communication (Figure 2). Data uncertainty visualization, in particular, stands to benefit from this convergence: it requires the precision of science, the sensitivity of design, and the interpretive openness often found in art. Moving toward such a hybrid mindset would enable visualizations that are both intellectually honest and experientially engaging, helping audiences become more comfortable with uncertainty, more attentive to nuance, and better equipped to learn from the data that shape our world.
Statements
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author contributions
NA: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review and editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. The publishing fee was kindly waived by the journal.
Conflict of interest
Author NA is the founder and sole employee of “Toiminimi Nuria Altimir”.
Generative AI statement
The author(s) declared that generative AI was used in the creation of this manuscript. To improve text legibility.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1
AltenmüllerM. S.SchleglS.StarkJ.EnglhardtK.DenglP.HenningW.et al (2022). Qualitative data for:‘certainly uncertainty. Public support for uncertainty communication in science museums. PsychArchives. 10.23668/PSYCHARCHIVES.12318
2
ArunkumarA.PadillaL.BaeG.-Y.BryanC. (2023). Image or Information? Examining the Nature and Impact of Visualization Perceptual Classification. 10.48550/arXiv.2307.10571arXiv:2307.10571
3
ArunkumarA.PadillaL.BryanC. (2025). Modeling and measuring the chart communication recall process. Comput. Graph. Forum44 (3), e70099. 10.1111/cgf.70099
4
BattistiT.DaquinoM. (2025). Uncertainty, narrativity, and critical approaches in digital humanities information visualisation projects.Available online at: https://hdl.handle.net/11585/1009129 (Accessed June 10, 2026).
5
BatziakoudiK.BortolasoC.FeketeJ.-D. (2025). “We need to talk (more) about numeracy! toward a numeracy-informed visualization design process,” in IEEE VIS Conference. Viscomm 2025 - 7th Workshop on Visualization for Communication. Vienna, Austria.Available online at: https://hal.science/hal-05241509v1 (Accessed June 10, 2026).
6
BerlyneD. E. (1954). A theory of human curiosity. Br. J. Psychol.45 (3), 180–191. 10.1111/j.2044-8295.1954.tb01243.x
7
BowmanA. W. (2019). Graphics for uncertainty. J. R. Stat. Soc. Ser. A Statistics Soc.182 (2), 403–418. 10.1111/rssa.12379
8
BransonZ.Paz ParraM.YurkoR. (2025). The landscape of college-level data visualization courses, and the benefits of incorporating statistical thinking. J. Statistics Data Sci. Educ.33 (4), 390–406. 10.1080/26939169.2025.2537049
9
BurgioV. (2019). Uncertain infographics: expressing doubt in data visualization. Semiotica2019 (230), 143–166. 10.1515/sem-2018-0114
10
CalvoL.ChristelI.TerradoM.CucchiettiF.Pérez-MontoroM. (2022). Users’ cognitive load: a key aspect to successfully communicate visual climate information. Bull. Am. Meteorological Soc.103 (1), E1–E16. 10.1175/BAMS-D-20-0166.1
11
ChristiansenJ. (2023). Building Science Graphics. an Illustrated Guide to Communicating Science Through Diagrams and Visualizations. 1st edn. Boca Raton, FL: A K Peters/CRC Press (an imprint of Routledge / Taylor & Francis Group). 10.1201/9781003217817
12
CorrellM.MoritzD.HeerJ. (2018). Value-suppressing uncertainty palettes. Proc. 2018 CHI Conf. Hum. Factors Comput. Syst.21, 1–11. 10.1145/3173574.3174216
13
CuzzolinF. (2025). Uncertainty measures: a critical survey. Inf. Fusion102, 102609. 10.1016/j.inffus.2024.102609
14
DhunganaA.DoyleE. E. H.PrasannaR.McDonaldG. (2025). From scientific models to decisions: exploring uncertainty communication gaps between scientists and decision-makers. Environ. Syst. Decis.45 (3), 44. 10.1007/s10669-025-10039-w
15
DriesC.McDowellM.SchneiderC. R.RebitschekF. G. (2025). The effect of uncertainty communication on public trust depends on belief – evidence consistency. PNAS Nexus4 (3), pgaf071. 10.1093/pnasnexus/pgaf071
16
EfsaS. C.BenfordD.HalldorssonT.JegerM. J.KnutsenH. K.MoreS.et al (2018). Guidance on uncertainty analysis in scientific assessments. EFSA J.16 (1), e05123. 10.2903/j.efsa.2018.5123
17
FengD.KwockL.LeeY.TaylorR. M. (2010). Matching visual saliency to confidence in plots of uncertain data. IEEE Trans. Vis. Comput. Graph.16 (6), 980–989. 10.1109/TVCG.2010.176
18
FischhoffB. (2012). Communicating uncertainty: fulfilling the duty to inform. Issues Sci. Technol.28 (4), 63–70. Available online at: https://issues.org/fischhoff/.
19
FischhoffB.DavisA. L. (2014). Communicating scientific uncertainty. Proc. Natl. Acad. Sci.111 (4), 13664–13671. 10.1073/pnas.1317504111
20
FranconeriS. L.PadillaL. M.ShahP.ZacksJ. M.HullmanJ. (2021). The science of visual data communication: what works. Psychol. Sci. Public Interest22 (3), 110–161. 10.1177/15291006211051956
21
FrascaroliJ.LederH.BratticoE.Van De CruysS. (2024). Aesthetics and predictive processing: grounds and prospects of a fruitful encounter. Philosophical Trans. R. Soc. B Biol. Sci.379 (1895), 20220410. 10.1098/rstb.2022.0410
22
FreyC.PenmanJ.HanleL.MonniS.OgleS. (2006). Uncertainties (IPCC guidelines for national greenhouse gas inventories volume 1: general guidance and reporting).Available online at: https://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html (Accessed June 10, 2026).
23
FygensonR.PadillaL.BertiniE. (2025). Cognitive affordances in visualization: related constructs, design factors, and framework. IEEE Trans. Vis. and Comput. Graph.31 (12), 10624–10639. 10.1109/TVCG.2025.3610803
24
GelmanA.UnwinA. (2013). Infovis and statistical graphics: different goals, different looks. J. Comput. Graph. Statistics22 (1), 2–28. 10.1080/10618600.2012.761137
25
GhisleniG.StolteC.GozzardM.Von SoostenL.BrunoA. (2025). Why science needs art. Front. Bioinforma.5, 1708311. 10.3389/fbinf.2025.1708311
26
GrafL. K. M.LandwehrJ. R. (2015). A dual-process perspective on fluency-based aesthetics: the pleasure-interest model of aesthetic liking. Personality Soc. Psychol. Rev.19 (4), 395–410. 10.1177/1088868315574978
27
GrafL. K. M.LandwehrJ. R. (2017). Aesthetic pleasure versus aesthetic interest: the two routes to aesthetic liking. Front. Psychol.8, 15. 10.3389/fpsyg.2017.00015
28
GustafsonA.RiceR. E. (2020). A review of the effects of uncertainty in public science communication. Public Underst. Sci.29 (6), 614–633. 10.1177/0963662520942122
29
HägeleD.SchulzC.BeschleC.BoothH.ButtM.BarthA.et al (2022). Uncertainty visualization: fundamentals and recent developments. It - Inf. Technol.64 (4–5), 121–132. 10.1515/itit-2022-0033
30
HakuhodoC. (2008). Population by pixel Print advertising campaign. Available online at: https://www.oneclub.org/awards/theoneshow/-award/6984/population-by-pixel (Accessed June 10, 2026).WWF Jpn.
31
HatchJ.JacksonN. (2022). “The KSJ science editing handbook (v2.1),” in Knight Science Journalism at MIT.Available online at: https://ksjhandbook.org/ (Accessed June 10, 2026).
32
HillM. (2018). “Developing an experiential language for uncertainty in data journalism. Department of graphic design and industrial design,” in College of Design. Raleigh, NC: North Carolina State University (NCSU) College of Design.
33
HillA.ChurchouseC.SchoberM. f. (2018). Seeking new ways to visually represent uncertainty in data: what we can learn from the fine arts. IEEE Vis. Arts Program (VISAP), 1–8. 10.1109/VISAP45312.2018.9046052
34
HollimanN. S.ColtekinA.FernstadS. J.McLaughlinL.SimpsonM. D.WoodsA. J. (2022). Visual entropy and the visualization of uncertainty. arXiv. arXiv:1907.12879. 10.48550/arXiv.1907.12879
35
HullmanJ. (2020). Why authors don’t visualize uncertainty. IEEE Trans. Vis. Comput. Graph.26 (1), 130–139. 10.1109/TVCG.2019.2934287
36
HullmanJ.AdarE.ShahP. (2011). Benefitting InfoVis with visual difficulties. IEEE Transactions on Visualization and Computer Graphics17 (12), 2213–2222. 10.1109/TVCG.2011.175
37
HullmanJ.QiaoX.CorrellM.KaleA.KayM. (2019). In pursuit of error: a survey of uncertainty visualization evaluation. IEEE Trans. Vis. Comput. Graph.25 (1), 903–913. 10.1109/TVCG.2018.2864889
38
JCGM (2008). Evaluation of measurement data—Supplement 1 to the “guide to the expression of uncertainty in measurement”—Propagation of distributions using a monte carlo method (JCGM 101:2008). Jt. Comm. Guid. Metrology (JCGM). 10.59161/JCGM101-2008
39
JCGM (2011). Evaluation of measurement data – supplement 2 to the “Guide to the expression of uncertainty in measurement” – extension to any number of output quantities JCGM 102:2011. Jt. Comm. Guid. Metrology (JCGM). 10.59161/JCGM102-2011
40
JCGM (2023). Guide to the Expression of Uncertainty in Measurement — Part 1: Introduction. Sèvres, France: Joint Committee for Guides in Metrology (JCGM) (Chaired and published via the Bureau International des Poids et Mesures (BIPM)). 10.59161/JCGMGUM-1-2023
41
JoslynS.SavelliS. (2021). Visualizing uncertainty for non-expert end users: the challenge of the deterministic construal error. Front. Comput. Sci.2 (January), 590232. 10.3389/fcomp.2020.590232
42
KerrJ.Van Der BlesA.-M.DryhurstS.SchneiderC. R.ChopurianV.FreemanA. L. J.et al (2023). The effects of communicating uncertainty around statistics, on public trust. R. Soc. Open Sci.10 (11), 230604. 10.1098/rsos.230604
43
KesnerL. (2014). The predictive mind and the experience of visual art work. Front. Psychol.5 (December), 1417. 10.3389/fpsyg.2014.01417
44
KosaraR. (2013). InfoVis is So much more: a comment on gelman and unwin and an invitation to consider the opportunities. J. Comput. Graph. Statistics22 (1), 29–32. 10.1080/10618600.2012.755465
45
LanX.LiuY. (2025). I came across a junk’: understanding design flaws of data visualization from the public’s perspective. IEEE Trans. Vis. Comput. Graph.31 (1), 393–403. 10.1109/TVCG.2024.3456341
46
LeffrangD.MüllerO. (2025). Visualizing uncertainty in time series forecasts: the impact of uncertainty visualization on users’ confidence, algorithmic advice utilization, and forecasting performance. J. Forecast.44 (4), 1235–1246. 10.1002/for.3222
47
LevontinP.WaltonJ. L. (2020). Visualising Uncertainty: A Short Introduction AU4DM Network. London, United Kingdom: Sad Press.
48
LibertiniL.ChuprikovaE.MengL. (2021). Effect of geospatial uncertainty borderization on users’ heuristic reasoning. Proc. ICA4, 1–8. 10.5194/ica-proc-4-66-2021
49
LoewensteinG. (1994). The psychology of curiosity: a review and reinterpretation. Psychol. Bull. (US)116 (1), 75–98. 10.1037/0033-2909.116.1.75
50
MacEachrenA. M.RobinsonA.HopperS.GardnerS.MurrayR.GaheganM.et al (2005). Visualizing geospatial information uncertainty: what we know and what we need to know. Cartogr. Geogr. Inf. Sci.32 (3), 139–160. 10.1559/1523040054738936
51
MacEachrenA. M.RothR. E.O’BrienJ.LiB.SwingleyD.GaheganM. (2012). Visual semiotics and uncertainty visualization: an empirical study. IEEE Trans. Vis. Comput. Graph.18 (12), 2496–2505. 10.1109/TVCG.2012.279
52
MadanC. R.BayerJ.GamerM.LonsdorfT. B.SommerT. (2018). Visual complexity and affect: ratings reflect more than meets the eye. Front. Psychol.8, 2368. 10.3389/fpsyg.2017.02368
53
MasonH.Dianne CookD.GoodwinS.TanakaE.VanderPlasS. (2024). The noisy work of uncertainty visualisation research: a review. arXiv:2411.10482. 10.48550/arXiv.2411.10482
54
MasonH.CookD.GoodwinS.VanderPlasS. (2026). Ggdibbler: add uncertainty to data visualisationsAvailable online at: https://harriet-mason.github.io/ggdibbler/ (Accessed June 10, 2026).
55
MastrandreaM. D.FieldC. B.StockerT. F.EdenhoferO.EbiK. L.FrameD. J.et al (2010). Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties. Geneva, Switzerland: Intergovernmental Panel on Climate Change (IPCC). Available online at: https://www.ipcc.ch/site/assets/uploads/2018/05/uncertainty-guidance-note.pdf (Accessed June 10, 2026).
56
MuthC.CarbonC.-C. (2016). SeIns: semantic instability in art. Art Percept.4 (1–2), 145–184. 10.1163/22134913-00002049
57
OlivaA.TorralbaA. (2006). “Chapter 2 building the gist of a scene: the role of global image features in recognition,”. Editors Martinez-CondeS.MacknikS. L.MartinezL. M.AlonsoJ. M.TseP. U. (Elsevier), 155. 10.1016/S0079-6123(06)55002-2Progress in Brain Research
58
OSR (2022). Approaches to presenting uncertainty in the statistical system. Office Statistics Regul.Available online at: https://osr.statisticsauthority.gov.uk/publication/approaches-to-presenting-uncertainty-in-the-statistical-system/ (Accessed June 10, 2026).
59
O’Hara-WildM.KayM.HayesA.HyndmanR. (2026). Distributional.Available online at: https://cran.r-project.org/web/packages/distributional/ (Accessed June 10, 2026).
60
PadillaL.CastroS. C.HosseinpourH. (2021a). A review of uncertainty visualization errors: working memory as an explanatory theory. Psychol. Learn. Motivation74, 275–315. 10.1016/bs.plm.2021.03.001
61
PadillaL. M. K.PowellM.KayM.HullmanJ. (2021b). Uncertain about uncertainty: how qualitative expressions of forecaster confidence impact decision-making with uncertainty visualizations. Front. Psychol.11, 579267. 10.3389/fpsyg.2020.579267
62
PadillaL.KayM.HullmanJ. (2022). “Uncertainty visualization,” in Computational Statistics and Data Science (Wiley), 405–421.
63
PetekB.NabergojD.ŠtrumbeljE. (2025). Bootplot: black-box uncertainty visualization.Available online at: https://github.com/davidnabergoj/bootplot (Accessed June 10, 2026).
64
SchaffnerJ.Dongqi BaoS.ToblerP.HareT.PolaniaR. (2023). Sensory perception relies on fitness-maximizing codes. Nat. Hum. Behav.7 (7), 1135–1151. 10.1038/s41562-023-01584-y
65
StamkouE.KeltnerD.CoronaR.AksoyE.CowenA. (2024). Emotional palette: a computational mapping of aesthetic experiences evoked by visual art. Sci. Rep.14 (1), 19932. 10.1038/s41598-024-69686-9
66
StavrovaO.KleinbergB.EvansA. M.IvanovićM. (2024). Expressions of uncertainty in online science communication hinder information diffusion. PNAS Nexus3 (10), pgae439. 10.1093/pnasnexus/pgae439
67
SterzikA.KroneM.BaumD.CunninghamD. W.LawonnK. (2025). Uncertainty visualization for biomolecular structures: an empirical evaluation. IEEE Trans. Vis. Comput. Graph.31 (12), 10296–10310. 10.1109/TVCG.2025.3596385
68
Van De CruysS.WagemansJ. (2011). Putting reward in art: a tentative prediction error account of visual art. I-Perception2 (9), 1035–1062. 10.1068/i0466aap
69
Van De CruysS.FrascaroliJ.FristonK. (2024). Order and change in art: towards an active inference account of aesthetic experience. Philosophical Trans. R. Soc. B Biol. Sci.379 (1895), 20220411. 10.1098/rstb.2022.0411
70
Van Der BlesA. M.Van Der LindenS.FreemanA. L. J.MitchellJ.GalvaoA. B.ZavalL.et al (2019). Communicating uncertainty about facts, numbers and science. R. Soc. Open Sci.6 (5), 181870. 10.1098/rsos.181870
71
Van GeertE.WagemansJ. (2020). Order, complexity, and aesthetic appreciation. Psychol. Aesthet. Creativity, Arts14 (2), 135–154. 10.1037/aca0000224
72
WeiskopfD. (2022). Uncertainty visualization: concepts, methods, and applications in biological data visualization. Front. Bioinforma.2, 793819. 10.3389/fbinf.2022.793819
73
WilkeC. O. (2019). Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures (Firsr). Sebastopol, CA: O’Reilly Media, Inc. Available online at: https://clauswilke.com/dataviz/index.html.
74
WindhagerF.Abduhl-RahmanA.BludauM.-J.HengesbachN.LamqaddamH.MeirellesI.et al (2024). Complexity as Design Material Position Paper. Evaluation and Beyond - Methodological Approaches for Visualization (BELIV), 71–80. 10.1109/BELIV64461.2024.00013
75
YanaiR. D.MannT. A.HongS. D.PuG.ZukswertJ. M. (2021). The current state of uncertainty reporting in ecosystem studies: a systematic evaluation of peer‐reviewed literature. Ecosphere12 (6), e03535. 10.1002/ecs2.3535
76
Yu, Z. Y. (2018). Visualizing Uncertainty: What We Talk About when We Talk About Uncertainty. Master’s thesis. Boston, MA: Northeastern University.
Summary
Keywords
complexity, data visualization, literacy, signal-suppression, uncertainty
Citation
Altimir N (2026) Engaging with uncertainty when engagement is uncertain. Front. Bioinform. 6:1801017. doi: 10.3389/fbinf.2026.1801017
Received
31 January 2026
Revised
19 June 2026
Accepted
19 June 2026
Published
09 July 2026
Volume
6 - 2026
Edited by
Barbora Kozlikova, Masaryk University, Czechia
Reviewed by
Sandy Claes, KU Leuven, Belgium
Updates
Copyright
© 2026 Altimir.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Nuria Altimir, contact@nuriaaltimir.com
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.