Review Article | Published:

Cognitive and psychological science insights to improve climate change data visualization

Nature Climate Change volume 6, pages 10801089 (2016) | Download Citation

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 optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

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

  2. 2.

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

  3. 3.

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

  4. 4.

    , , & Climate data challenges in the 21st century. Science 331, 700–702 (2011).

  5. 5.

    , , , & Linguistic analysis of IPCC summaries for policymakers and associated coverage. Nat. Clim. Change 6, 311–317 (2016).

  6. 6.

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

  7. 7.

    , , & The interpretation of IPCC probabilistic statements around the world. Nat. Clim. Change 4, 508–512 (2014).

  8. 8.

    , & 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).

  9. 9.

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

  10. 10.

    & Comparing experts and novices on scaffolded data visualizations using eye-tracking. J. Eye Mov. Res. (2014).

  11. 11.

    , , , & Interpreting climate data visualisations to inform adaptation decisions. Clim. Risk Manage. 10, 17–26 (2015).

  12. 12.

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

  13. 13.

    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.

  14. 14.

    , , , & Dominant frames in legacy and social media coverage of the IPCC Fifth Assessment Report. Nat. Clim. Change 5, 380–385 (2015).

  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.

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

  17. 17.

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

  18. 18.

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

  19. 19.

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

  20. 20.

    & Naïve realism: misplaced faith in realistic displays. Ergon. Des. 13, 6–13 (2005).

  21. 21.

    , , & Reading bar graphs: effects of extraneous depth cues and graphical context. J. Exp. Psychol. Appl. 4, 119–138 (1998).

  22. 22.

    , , & Naïve cartography: how intuitions about display configuration can hurt performance. Cartographica 44, 171–186 (2009).

  23. 23.

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

  24. 24.

    & Gorillas in our midst: sustained inattentional blindness for dynamic events. Perception 28, 1059–1074 (1999).

  25. 25.

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

  26. 26.

    , & Visual Perception: Physiology, Psychology and Ecology. (Psychology Press, 2003).

  27. 27.

    , , & Symbolic control of visual attention. Psychol. Sci. 12, 360–365 (2001).

  28. 28.

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

  29. 29.

    , & Do predictions of visual perception aid design? ACM Trans. Appl. Percep. (2011).

  30. 30.

    & 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.

  31. 31.

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

  32. 32.

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

  33. 33.

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

  34. 34.

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

  35. 35.

    , & 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).

  36. 36.

    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).

  37. 37.

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

  38. 38.

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

  39. 39.

    , & Measuring visual clutter. J. Vision (2007).

  40. 40.

    & Cutting through the clutter: searching for targets in evolving complex scenes. J. Vision (2011).

  41. 41.

    , & Visual clutter causes high-magnitude errors. PLoS Biol. 4, e56 (2006).

  42. 42.

    & 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).

  43. 43.

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

  44. 44.

    & Perception in chess. Cognitive Psychol. 4, 55–81 (1973).

  45. 45.

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

  46. 46.

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

  47. 47.

    Decisions Taken with Respect to the Review of IPCC Processes and Procedures: Communications Strategy (IPCC, 2012);

  48. 48.

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

  49. 49.

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

  50. 50.

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

  51. 51.

    , , , & Colour-temperature correspondences: when reactions to thermal stimuli are influenced by colour. PLoS ONE 9, e91854 (2014).

  52. 52.

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

  53. 53.

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

  54. 54.

    , , Sutton R., & Projections of when temperature change will exceed 2 °C above pre-industrial levels. Nat. Clim. Change 1, 407–412 (2011).

  55. 55.

    , , & in Thinking with Data (eds Lovett, M. C. & Shah, P.) 65–85 (Psychology Press, 2007).

  56. 56.

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

  57. 57.

    , & 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.

  58. 58.

    , , & in Diagrammatic Representation and Inference, Volume 2317 Lecture Notes in Computer Science (eds Hegarty, M. et al.) 206–220 (Springer Berlin, 2002).

  59. 59.

    Multimedia Learning (Cambridge Univ. Press, 2009).

  60. 60.

    , & 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.

  61. 61.

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

  62. 62.

    Beautiful Evidence (Graphics Press, 2006).

  63. 63.

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

  64. 64.

    , , & 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).

  65. 65.

    , , , & Multiple routes to mental animation: language and functional relations drive motion processing for static Images. Psychol. Sci. 24, 1379–1388 (2013).

  66. 66.

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

  67. 67.

    , , , & Unconscious effects of language-specific terminology on preattentive color perception. Proc. Natl Acad. Sci. USA 106, 4567–4570 (2009).

  68. 68.

    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).

  69. 69.

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

  70. 70.

    , , & Structural geology practice and learning, from the perspective of cognitive science. J. Struct. Geol. 54, 72–84 (2013).

  71. 71.

    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).

  72. 72.

    , & Reading habits for both words and numbers contribute to the SNARC effect. Psychon. B. Rev. 16, 328–331 (2009).

  73. 73.

    , , & 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).

  74. 74.

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

  75. 75.

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

  76. 76.

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

  77. 77.

    , , , & “A 30% chance of rain tomorrow”: How does the public understand probabilistic weather forecasts? Risk Anal. 25, 623–629 (2005).

  78. 78.

    , & Improving communication of uncertainty in the reports of the Intergovernmental Panel on Climate Change. Psychol. Sci. 20, 299–308 (2009).

  79. 79.

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

  80. 80.

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

  81. 81.

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

  82. 82.

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

  83. 83.

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

  84. 84.

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

  85. 85.

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

  86. 86.

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

  87. 87.

    , & The roles of mental animations and external animations in understanding mechanical systems. Cognition Instruct. 21, 325–360 (2003).

  88. 88.

    , , , & A comparison of animated maps with static small-multiple maps for visually identifying space–time clusters. Ann. Assoc. Am. Geogr. 96, 740–753 (2006).

  89. 89.

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

  90. 90.

    , & in Cognitive and Linguistic Aspects of Geographic Space (eds Raubal, M. et al.) 259–270 (Springer-Verlag Berlin, 2013).

  91. 91.

    Report of the 41st Session of the IPCC (IPCC, 2015);

  92. 92.

    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).

  93. 93.

    , , , & in Communicating Climate-Change and Natural Hazard Risk and Cultivating Resilience (eds Drake, J. L. et al.) 95–113 (Springer International Publishing, 2016).

  94. 94.

    , & 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.

  95. 95.

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

  96. 96.

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

  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

  1. School of Psychology and Tyndall Centre for Climate Change Research, University of East Anglia, Norwich NR4 7TJ, UK

    • Jordan Harold
  2. School of Environmental Sciences, Tyndall Centre for Climate Change Research, and Science, Society and Sustainability (3S) Research Group, University of East Anglia, Norwich NR4 7TJ, UK

    • Irene Lorenzoni
  3. Department of Psychology, Temple University, Philadelphia, Pennsylvania 19122, USA

    • Thomas F. Shipley
  4. School of Psychology, University of East Anglia, Norwich NR4 7TJ, UK

    • Kenny R. Coventry

Authors

  1. Search for Jordan Harold in:

  2. Search for Irene Lorenzoni in:

  3. Search for Thomas F. Shipley in:

  4. Search for Kenny R. Coventry in:

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.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Jordan Harold or Kenny R. Coventry.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nclimate3162

Further reading