Cognitive and psychological science insights to improve climate change data visualization

Abstract

Visualization of climate data plays an integral role in the communication of climate change findings to both expert and non-expert audiences. The cognitive and psychological sciences can provide valuable insights into how to improve visualization of climate data based on knowledge of how the human brain processes visual and linguistic information. We review four key research areas to demonstrate their potential to make data more accessible to diverse audiences: directing visual attention, visual complexity, making inferences from visuals, and the mapping between visuals and language. We present evidence-informed guidelines to help climate scientists increase the accessibility of graphics to non-experts, and illustrate how the guidelines can work in practice in the context of Intergovernmental Panel on Climate Change graphics.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Example of an IPCC figure and aspects that might limit accessibility to non-experts.
Figure 2: Conceptual overview of the process of graphic comprehension and approaches to improving accessibility.
Figure 3: Example of visual attention for an IPCC figure for a non-expert viewer trying to interpret the graphic (measured using eye tracking: first 15 seconds of data shown).
Figure 4: Schematic of properties known to direct visual attention.
Figure 5: A cognitively inspired version of an IPCC figure.

References

  1. 1

    IPCC Climate Change 2014: Synthesis Report (eds Pachauri, R. K. et al.) (Cambridge Univ. Press, 2014).

  2. 2

    Clayton, S. et al. Psychological research and global climate change. Nat. Clim. Change 5, 640–646 (2015).

    Article  Google Scholar 

  3. 3

    Dilling, L. & Lemos, M. C. Creating usable science: opportunities and constraints for climate knowledge use and their implications for science policy. Glob. Environ. Change 21, 680–689 (2011).

    Article  Google Scholar 

  4. 4

    Overpeck, J. T., Meehl, G. A., Bony, S. & Easterling D. R. Climate data challenges in the 21st century. Science 331, 700–702 (2011).

    CAS  Article  Google Scholar 

  5. 5

    Barkemeyer, R., Dessai, S., Monge-Sanz, B., Renzi, B. G. & Napolitano, G. Linguistic analysis of IPCC summaries for policymakers and associated coverage. Nat. Clim. Change 6, 311–317 (2016).

    Article  Google Scholar 

  6. 6

    Hollin, G. J. S. & Pearce, W. Tension between scientific certainty and meaning complicates communication of IPCC reports. Nat. Clim. Change 5, 753–756 (2015).

    Article  Google Scholar 

  7. 7

    Budescu, D. V., Por, H. H., Broomell, S. B. & Smithson, M. The interpretation of IPCC probabilistic statements around the world. Nat. Clim. Change 4, 508–512 (2014).

    Article  Google Scholar 

  8. 8

    McMahon, R., Stauffacher, M. & Knutti, R. The unseen uncertainties in climate change: reviewing comprehension of an IPCC scenario graph. Climatic Change 133, 141–154 (2015). A study showing the misinterpretation of an IPCC SPM graphic by non-experts (that is, individuals who are not climate scientists).

    Article  Google Scholar 

  9. 9

    Mahony, M. & Hulme, M. The colour of risk: an exploration of the IPCC's “burning embers” diagram. Spontaneous Generations 6, 75–89 (2012).

    Google Scholar 

  10. 10

    Stofer, K. & Che, X. Comparing experts and novices on scaffolded data visualizations using eye-tracking. J. Eye Mov. Res. http://doi.org/bsng (2014).

  11. 11

    Daron, J. D., Lorenz, S., Wolski, P., Blamey, R. C. & Jack, C. Interpreting climate data visualisations to inform adaptation decisions. Clim. Risk Manage. 10, 17–26 (2015).

    Article  Google Scholar 

  12. 12

    Nocke, T., Sterzel, T., Böttinger, M. & Wrobel, M. in Digital Earth Summit on Geoinformatics 2008: Tools for Global Change Research (eds Ehlers, M. et al.) 226–232 (Wichmann, 2008).

    Google Scholar 

  13. 13

    Hegarty, M. The cognitive science of visual–spatial displays: implications for design. Top. Cogn. Sci. 3, 446–474 (2011). A review highlighting the different ways in which graphics can augment cognition.

    Article  Google Scholar 

  14. 14

    O'Neill, S., Williams, H. T. P., Kurz, T., Wiersma, B. & Boykoff, M. Dominant frames in legacy and social media coverage of the IPCC Fifth Assessment Report. Nat. Clim. Change 5, 380–385 (2015).

    Article  Google Scholar 

  15. 15

    IPCC Expert Meeting on Communication Meeting Report (eds Lynn, J. et al.) (World Meteorological Organization, 2016). Meeting report providing recommendations for the communication of future IPCC reports and engaging with stakeholders.

  16. 16

    Pinker, S. in Artificial Intelligence and the Future of Testing (ed. Freedle, R.) 73–126 (Lawrence Erlbaum Associates, 1990).

    Google Scholar 

  17. 17

    Neisser, U. Cognition and Reality: Principles and Implications of Cognitive Psychology (W. H. Freeman & Company, 1976).

    Google Scholar 

  18. 18

    Clark, A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36, 181–204 (2013).

    Article  Google Scholar 

  19. 19

    diSessa, A. A. Metarepresentation: native competence and targets for instruction. Cognition Instruct. 22, 293–331 (2004).

    Article  Google Scholar 

  20. 20

    Smallman, H. S. & St John, M. Naïve realism: misplaced faith in realistic displays. Ergon. Des. 13, 6–13 (2005).

    Google Scholar 

  21. 21

    Zacks, J., Levy, E., Tversky, B. & Schiano, D. J. Reading bar graphs: effects of extraneous depth cues and graphical context. J. Exp. Psychol. Appl. 4, 119–138 (1998).

    Article  Google Scholar 

  22. 22

    Hegarty, M., Smallman, H. S., Stull, A. T. & Canham, M. S. Naïve cartography: how intuitions about display configuration can hurt performance. Cartographica 44, 171–186 (2009).

    Article  Google Scholar 

  23. 23

    Rayner, K. Eye movements and attention in reading, scene perception, and visual search. Q. J. Exp. Psychol. 62, 1457–1506 (2009).

    Article  Google Scholar 

  24. 24

    Simons, D. J. & Chabris, C. F. Gorillas in our midst: sustained inattentional blindness for dynamic events. Perception 28, 1059–1074 (1999).

    CAS  Article  Google Scholar 

  25. 25

    Wolfe, J. M. & Horowitz, T. S. What attributes guide the deployment of visual attention and how do they do it? Nat. Rev. Neurosci. 5, 495–501 (2004).

    CAS  Article  Google Scholar 

  26. 26

    Bruce, V., Green, P. R. & Georgeson, M. A. Visual Perception: Physiology, Psychology and Ecology. (Psychology Press, 2003).

    Google Scholar 

  27. 27

    Hommel, B., Pratt, J., Colzato, L. & Godijn, R. Symbolic control of visual attention. Psychol. Sci. 12, 360–365 (2001).

    CAS  Article  Google Scholar 

  28. 28

    Itti, L. & Koch, C. Computational modelling of visual attention. Nat. Rev. Neurosci. 2, 194–203 (2001).

    CAS  Article  Google Scholar 

  29. 29

    Rosenholtz, R., Dorai, A. & Freeman, R. Do predictions of visual perception aid design? ACM Trans. Appl. Percep. http://doi.org/dj4tdb (2011).

  30. 30

    Grant, E. R. & Spivey, M. J. Eye movements and problem solving: guiding attention guides thought. Psychol. Sci. 14, 462–466 (2003). A study demonstrating that directing visual attention can support individuals in solving a problem depicted in a graphic.

    Article  Google Scholar 

  31. 31

    Henderson, J. M., Weeks, P. A. & Hollingworth, A. The effects of semantic consistency on eye movements during complex scene viewing. J. Exp. Psychol. Hum. Percept. Perform. 25, 210–228 (1999).

    Article  Google Scholar 

  32. 32

    Brunyé, T. T. & Taylor, H. A. When goals constrain: eye movements and memory for goal-oriented map study. Appl. Cognitive Psych. 23, 772–787 (2009).

    Article  Google Scholar 

  33. 33

    Carpenter, P. A. & Shah, P. A model of the perceptual and conceptual processes in graph comprehension. J. Exp. Psychol. Appl. 4, 75–100 (1998).

    Article  Google Scholar 

  34. 34

    Peebles, D. & Cheng, P. C. H. Modeling the effect of task and graphical representation on response latency in a graph reading task. Hum. Factors 45, 28–46 (2003).

    Article  Google Scholar 

  35. 35

    Hegarty, M., Canham, M. S. & Fabrikant, S. I. Thinking about the weather: how display salience and knowledge affect performance in a graphic inference task. J. Exp. Psychol. Learn. Mem. Cogn. 36, 37–53 (2010).

    Article  Google Scholar 

  36. 36

    Potter, K. et al. Ensemble-vis: a framework for the statistical visualization of ensemble data. In IEEE Int. Conference on Data Mining Workshops (eds Saygin, Y. et al.) 233–240 (2009).

    Google Scholar 

  37. 37

    Meyer, J., Shinar, D. & Leiser, D. Multiple factors that determine performance with tables and graphs. Hum. Factors 39, 268–286 (1997).

    Article  Google Scholar 

  38. 38

    Shah P., Mayer, R. E. & Hegarty, M. Graphs as aids to knowledge construction: signaling techniques for guiding the process of graph comprehension. J. Educ. Psychol. 91, 690–702 (1999).

    Article  Google Scholar 

  39. 39

    Rosenholtz, R., Li, Y. & Nakano, L. Measuring visual clutter. J. Vision http://doi.org/bqtpr4 (2007).

  40. 40

    Neider, M. B. & Zelinsky, G. J. Cutting through the clutter: searching for targets in evolving complex scenes. J. Vision http://doi.org/bq8n55 (2011).

  41. 41

    Baldassi, S., Megna, N. & Burr, D. C. Visual clutter causes high-magnitude errors. PLoS Biol. 4, e56 (2006).

    Article  CAS  Google Scholar 

  42. 42

    Coco, M. I. & Keller, F. The impact of visual information on reference assignment in sentence production. In Proc. 31st Annual Conference Cognitive Science Society (eds Taatgen, N. & van Rijn, H.) 274–279 (Cognitive Science Society, 2009).

    Google Scholar 

  43. 43

    Kosslyn, S. M. Graph Design for the Eye and Mind (OUP, 2006).

    Google Scholar 

  44. 44

    Chase, W. G. & Simon, H. A. Perception in chess. Cognitive Psychol. 4, 55–81 (1973).

    Article  Google Scholar 

  45. 45

    Gobet, F. Chunking models of expertise: implications for education. Appl. Cognitive Psych. 19, 183–204 (2005).

    Article  Google Scholar 

  46. 46

    Wickens, C. D. & Carswell, C. M. The proximity compatibility principle: its psychological foundation and relevance to display design. Hum. Factors 37, 473–494 (1995).

    Article  Google Scholar 

  47. 47

    Decisions Taken with Respect to the Review of IPCC Processes and Procedures: Communications Strategy (IPCC, 2012); www.ipcc.ch/meetings/session35/IAC_CommunicationStrategy.pdf

  48. 48

    Tversky, B. in Handbook of Higher-Level Visuospatial Thinking (eds Shah, P. & Miyake, A.) 1–34 (Cambridge Univ. Press, 2005).

    Google Scholar 

  49. 49

    Heiser, J. & Tversky, B. Arrows in comprehending and producing mechanical diagrams. Cognitive Sci. 30, 581–592 (2006).

    Article  Google Scholar 

  50. 50

    Lakoff, G. & Johnson, M. The metaphorical structure of the human conceptual system. Cognitive Sci. 4, 195–208 (1980).

    Article  Google Scholar 

  51. 51

    Ho, H. N., Van Doorn, G. H., Kawabe, T., Watanabe, J. & Spence, C. Colour-temperature correspondences: when reactions to thermal stimuli are influenced by colour. PLoS ONE 9, e91854 (2014).

    Article  CAS  Google Scholar 

  52. 52

    Kövecses, Z. Metaphor in culture: universality and variation (Cambridge Univ. Press, 2005).

    Google Scholar 

  53. 53

    Shah, P. & Carpenter, P. A. Conceptual limitations in comprehending line graphs. J. Exp. Psychol. Gen. 124, 43–61 (1995).

    Article  Google Scholar 

  54. 54

    Joshi, M., Hawkins, E., Sutton R., Lowe, J. & Frame, D. Projections of when temperature change will exceed 2 °C above pre-industrial levels. Nat. Clim. Change 1, 407–412 (2011).

    Article  Google Scholar 

  55. 55

    Trickett, S. B., Trafton, J. G., Saner, L. & Schunn, C. D. in Thinking with Data (eds Lovett, M. C. & Shah, P.) 65–85 (Psychology Press, 2007).

    Google Scholar 

  56. 56

    Trafton, J. G. et al. Turning pictures into numbers: extracting and generating information from complex visualizations. Int. J. Hum. Comput. Stud. 53, 827–850 (2000).

    Article  Google Scholar 

  57. 57

    Trafton, J. G., Trickett, S. B. & Mintz, F. E. Connecting internal and external representations: spatial transformations of scientific visualizations. Found. Sci. 10, 89–106 (2005). A study demonstrating that experts use complex mental spatial transformations to make inferences about scientific data presented in graphics.

    Article  Google Scholar 

  58. 58

    Trafton, J. G., Marshall, S., Mintz, F. & Trickett, S. B. in Diagrammatic Representation and Inference, Volume 2317 Lecture Notes in Computer Science (eds Hegarty, M. et al.) 206–220 (Springer Berlin, 2002).

    Google Scholar 

  59. 59

    Mayer, R. E. Multimedia Learning (Cambridge Univ. Press, 2009).

    Google Scholar 

  60. 60

    Holsanova, J., Holmberg, N. & Holmqvist, K. Reading information graphics: the role of spatial contiguity and dual attentional guidance. Appl. Cognitive Psychol. 23, 1215–1226 (2009). A study showing the cognitive benefits of closely integrating graphics with their associated text.

    Article  Google Scholar 

  61. 61

    Ginns, P. Integrating information: a meta-analysis of the spatial contiguity and temporal contiguity effects. Learn. Instr. 16, 511–525 (2006).

    Article  Google Scholar 

  62. 62

    Tufte, E. R. Beautiful Evidence (Graphics Press, 2006).

    Google Scholar 

  63. 63

    Loewenstein, J. & Gentner, D. Relational language and the development of relational mapping. Cognitive Psychol. 50, 315–353 (2005).

    Article  Google Scholar 

  64. 64

    Harold, J., Coventry, K. R., Lorenzoni, I. & Shipley, T. F. Making sense of time-series data: how language can help identify long-term trends. In Proc. 37th Annual Meeting of the Cognitive Science Society (eds Noelle, D. C. et al.) 872–877 (Cognitive Science Society, 2015).

    Google Scholar 

  65. 65

    Coventry, K. R., Christophel, T. B., Fehr, T., Valdés-Conroy, B. & Herrmann, M. Multiple routes to mental animation: language and functional relations drive motion processing for static Images. Psychol. Sci. 24, 1379–1388 (2013).

    Article  Google Scholar 

  66. 66

    Light, A. & Bartlein, P. J. The end of the rainbow? Color schemes for improved data graphics. Eos 85, 385–391 (2004).

    Article  Google Scholar 

  67. 67

    Thierry, G., Athanasopoulos, P., Wiggett, A., Dering, B. & Kuipers, J. R. Unconscious effects of language-specific terminology on preattentive color perception. Proc. Natl Acad. Sci. USA 106, 4567–4570 (2009).

    CAS  Article  Google Scholar 

  68. 68

    Moreland, K. Diverging color maps for scientific visualization. In Proc. 5th International Symposium on Advances in Visual Computing: Part II (eds Bebis, G. et al.) 92–103 (Springer-Verlag Berlin, 2009).

    Google Scholar 

  69. 69

    Harrower, M. & Brewer, C. A. ColorBrewer.org: an online tool for selecting colour schemes for maps. Cartogr. J. 40, 27–37 (2003).

    Article  Google Scholar 

  70. 70

    Shipley, T. F., Tikoff, B., Ormand, C. & Manduca, C. Structural geology practice and learning, from the perspective of cognitive science. J. Struct. Geol. 54, 72–84 (2013).

    Article  Google Scholar 

  71. 71

    Hambrick, D. Z. et al. A test of the circumvention-of-limits hypothesis in scientific problem solving: the case of geological bedrock mapping. J. Exp. Psychol. Gen. 141, 397–403 (2012).

    Article  Google Scholar 

  72. 72

    Shaki S., Fischer M. H. & Petrusic W. M. Reading habits for both words and numbers contribute to the SNARC effect. Psychon. B. Rev. 16, 328–331 (2009).

    Article  Google Scholar 

  73. 73

    Torralba, A., Oliva, A., Castelhano, M. S. & Henderson, J. M. Contextual guidance of eye movements and attention in real-world scenes: The role of global features in object search. Psychol. Rev. 113, 766–786 (2006).

    Article  Google Scholar 

  74. 74

    Ratwani, R. M. & Trafton, J. G. (2008). Shedding light on the graph schema: Perceptual features versus invariant structure. Psychon. B. Rev. 15, 757–762 (2008).

    Article  Google Scholar 

  75. 75

    Gentner, D. & Gentner, D. R. in Mental Models (eds Gentner, D. & Stevens, A. L.) 99–129 (Lawrence Erlbaum Associates, 1983).

    Google Scholar 

  76. 76

    Sterman, J. D. & Sweeney, L. B. Understanding public complacency about climate change: adults' mental models of climate change violate conservation of matter. Climatic Change 80, 213–238 (2007).

    CAS  Article  Google Scholar 

  77. 77

    Gigerenzer, G., Hertwig, R., van den Broek, E., Fasolo, B. & Katsikopoulos, K. V. “A 30% chance of rain tomorrow”: How does the public understand probabilistic weather forecasts? Risk Anal. 25, 623–629 (2005).

    Article  Google Scholar 

  78. 78

    Budescu, D. V., Broomell, S. & Por, H. H. Improving communication of uncertainty in the reports of the Intergovernmental Panel on Climate Change. Psychol. Sci. 20, 299–308 (2009).

    Article  Google Scholar 

  79. 79

    Spiegelhalter, D., Pearson, M. & Short, I. Visualizing uncertainty about the future. Science 333, 1393–1400 (2011). A review highlighting the challenges of visually communicating uncertainty to diverse audiences.

    CAS  Article  Google Scholar 

  80. 80

    Andrienko, G. et al. Space, time and visual analytics. Int. J. Geogr. Inf. Sci. 24, 1577–1600 (2010).

    Article  Google Scholar 

  81. 81

    Crampton, J. W. Interactivity types in geographic visualization. Cartogr. Geogr. Inf. Sci. 29, 85–98 (2002).

    Article  Google Scholar 

  82. 82

    Cohen, C. A. & Hegarty, M. Individual differences in use of external visualisations to perform an internal visualisation task. Appl. Cognitive Psych. 21, 701–711 (2007).

    Article  Google Scholar 

  83. 83

    Tversky, B., Morrison, J. B. & Betrancourt, M. Animation: Can it facilitate? Int. J. Hum. Comput. Stud. 57, 247–262 (2002).

    Article  Google Scholar 

  84. 84

    Mayer, R. E., Hegarty, M., Mayer, S. & Campbell, J. When static media promote active learning: annotated illustrations versus narrated animations in multimedia instruction. J. Exp. Psychol. Appl. 11, 256–265 (2005).

    Article  Google Scholar 

  85. 85

    Lowe, R. K. Extracting information from an animation during complex visual learning. Eur. J. Psychol. Educ. 14, 225–244 (1999).

    Article  Google Scholar 

  86. 86

    Lowe, R. K. Animation and learning: selective processing of information in dynamic graphics. Learn. Instr. 13, 157–176 (2003).

    Article  Google Scholar 

  87. 87

    Hegarty, M., Kriz, S. & Cate, C. The roles of mental animations and external animations in understanding mechanical systems. Cognition Instruct. 21, 325–360 (2003).

    Article  Google Scholar 

  88. 88

    Griffin, A. L., MacEachren, A. M., Hardisty, F., Steiner, E. & Li, B. A comparison of animated maps with static small-multiple maps for visually identifying space–time clusters. Ann. Assoc. Am. Geogr. 96, 740–753 (2006).

    Article  Google Scholar 

  89. 89

    Betrancourt, M. in The Cambridge Handbook of Multimedia Learning (ed. Mayer, R. E.) 287–296 (Cambridge Univ. Press, 2005).

    Google Scholar 

  90. 90

    Shipley, T. F., Fabrikant, S. I. & Lautenschütz, A. K. in Cognitive and Linguistic Aspects of Geographic Space (eds Raubal, M. et al.) 259–270 (Springer-Verlag Berlin, 2013).

    Google Scholar 

  91. 91

    Report of the 41st Session of the IPCC (IPCC, 2015); http://ipcc.ch/meetings/session41/final_report_p41.pdf

  92. 92

    Rapley, C. G. et al. Time for Change? Climate Science Reconsidered, Report of the UCL Policy Commission on Communicating Climate Science (UCL Policy Commission on Communicating Climate Science, 2014).

    Google Scholar 

  93. 93

    Davis, M., Lowe, R., Steffen, S., Doblas-Reyes, F. & Rodó, X. in Communicating Climate-Change and Natural Hazard Risk and Cultivating Resilience (eds Drake, J. L. et al.) 95–113 (Springer International Publishing, 2016).

    Google Scholar 

  94. 94

    Fabrikant, S. I., Hespanha, S. R. & Hegarty, M. Cognitively inspired and perceptually salient graphic displays for efficient spatial inference making. Ann. Assoc. Am. Geogr. 100, 13–29 (2010). A study demonstrating the interaction between bottom-up and top-down cognitive processing of graphics, and the diagnostic value of eye-tracking data.

    Article  Google Scholar 

  95. 95

    Kosslyn, S. M. Understanding charts and graphs. Appl. Cognitive Psych. 3, 185–225 (1989).

    Article  Google Scholar 

  96. 96

    Meier, B. P. & Robinson, M. D. Why the sunny side is up: associations between affect and vertical position. Psychol. Sci. 15, 243–247 (2004).

    Article  Google Scholar 

  97. 97

    IPCC: Summary for Policymakers. In Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

Download references

Acknowledgements

This work was supported by a PhD Studentship from the School of Psychology, University of East Anglia (UEA) to J.H. and support from the Spatial Intelligence and Learning Centre (SILC), Temple University (SBE-1041707 from the National Science Foundation) including a travel grant to J.H. We would like to thank members of the Cognition Action Perception research group in the School of Psychology, UEA for their participation in a workshop to explore the scope of the Review, and members of the Tyndall Centre for Climate Change Research, UEA for their feedback on how the presented guidelines could work in practice.

Author information

Affiliations

Authors

Contributions

J.H. and K.R.C. outlined the scope of the Review with input from T.F.S. and I.L. The manuscript was drafted and prepared by J.H. with critical feedback from K.R.C., I.L. and T.F.S. All authors contributed to editing of the final manuscript.

Corresponding authors

Correspondence to Jordan Harold or Kenny R. Coventry.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Harold, J., Lorenzoni, I., Shipley, T. et al. Cognitive and psychological science insights to improve climate change data visualization. Nature Clim Change 6, 1080–1089 (2016). https://doi.org/10.1038/nclimate3162

Download citation

Further reading

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing