Kelsey J. Mulder, Louis Williams, Matthew Lickiss, Alison Black, A. Charlton-Perez, R. McCloy, E. McSorley
{"title":"Understanding representations of uncertainty, an eye-tracking study – Part 1: The effect of anchoring","authors":"Kelsey J. Mulder, Louis Williams, Matthew Lickiss, Alison Black, A. Charlton-Perez, R. McCloy, E. McSorley","doi":"10.5194/gc-6-97-2023","DOIUrl":null,"url":null,"abstract":"Abstract. Geoscience communicators must think carefully about how\nuncertainty is represented and how users may interpret these\nrepresentations. Doing so will help communicate risk more effectively, which\ncan elicit appropriate responses. Communication of uncertainty is not just a\ngeosciences problem; recently, communication of uncertainty has come to the\nforefront over the course of the COVID-19 pandemic, but the lessons learned\nfrom communication during the pandemic can be adopted across geosciences as\nwell. To test interpretations of environmental forecasts with uncertainty,\na decision task survey was administered to 65 participants who saw different\nhypothetical forecast representations common to presentations of\nenvironmental data and forecasts: deterministic, spaghetti plot with and\nwithout a median line, fan plot with and without a median line, and box plot\nwith and without a median line. While participants completed the survey,\ntheir eye movements were monitored with eye-tracking software. Participants'\neye movements were anchored to the median line, not focusing on possible\nextreme values to the same extent as when no median line was present.\nAdditionally, participants largely correctly interpreted extreme values from\nthe spaghetti and fan plots, but misinterpreted extreme values from the box\nplot, perhaps because participants spent little time fixating on the key.\nThese results suggest that anchoring lines, such as median lines, should\nonly be used where users should be guided to particular values and where\nextreme values are not as important in data interpretation. Additionally,\nfan or spaghetti plots should be considered instead of box plots to reduce\nmisinterpretation of extreme values. Further study on the role of expertise\nand the change in eye movements across the graph area and key is explored in more detail in the companion paper to this study (Williams et al., 2023; hereafter Part 2).\n","PeriodicalId":52877,"journal":{"name":"Geoscience Communication","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoscience Communication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5194/gc-6-97-2023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 1
Abstract
Abstract. Geoscience communicators must think carefully about how
uncertainty is represented and how users may interpret these
representations. Doing so will help communicate risk more effectively, which
can elicit appropriate responses. Communication of uncertainty is not just a
geosciences problem; recently, communication of uncertainty has come to the
forefront over the course of the COVID-19 pandemic, but the lessons learned
from communication during the pandemic can be adopted across geosciences as
well. To test interpretations of environmental forecasts with uncertainty,
a decision task survey was administered to 65 participants who saw different
hypothetical forecast representations common to presentations of
environmental data and forecasts: deterministic, spaghetti plot with and
without a median line, fan plot with and without a median line, and box plot
with and without a median line. While participants completed the survey,
their eye movements were monitored with eye-tracking software. Participants'
eye movements were anchored to the median line, not focusing on possible
extreme values to the same extent as when no median line was present.
Additionally, participants largely correctly interpreted extreme values from
the spaghetti and fan plots, but misinterpreted extreme values from the box
plot, perhaps because participants spent little time fixating on the key.
These results suggest that anchoring lines, such as median lines, should
only be used where users should be guided to particular values and where
extreme values are not as important in data interpretation. Additionally,
fan or spaghetti plots should be considered instead of box plots to reduce
misinterpretation of extreme values. Further study on the role of expertise
and the change in eye movements across the graph area and key is explored in more detail in the companion paper to this study (Williams et al., 2023; hereafter Part 2).