Visual Elements and Cognitive Biases Influence Interpretations of Trends in Scatter Plots
Visualizations are common methods to convey information but also increasingly used to spread misinformation. It is therefore important to understand the factors people use to interpret visualizations. In this paper, we focus on factors that influence interpretations of scatter plots, investigating t...
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creator | Filipowicz, Alexandre Carter, Scott Bravo, Nayeli Iliev, Rumen Hakimi, Shabnam Shamma, David Ayman Lyons, Kent Hogan, Candice Wu, Charlene |
description | Visualizations are common methods to convey information but also increasingly
used to spread misinformation. It is therefore important to understand the
factors people use to interpret visualizations. In this paper, we focus on
factors that influence interpretations of scatter plots, investigating the
extent to which common visual aspects of scatter plots (outliers and trend
lines) and cognitive biases (people's beliefs) influence perception of
correlation trends. We highlight three main findings: outliers skew trend
perception but exert less influence than other points; trend lines make trends
seem stronger but also mitigate the influence of some outliers; and people's
beliefs have a small influence on perceptions of weak, but not strong
correlations. From these results we derive guidelines for adjusting visual
elements to mitigate the influence of factors that distort interpretations of
scatter plots. We explore how these guidelines may generalize to other
visualization types and make recommendations for future studies. |
doi_str_mv | 10.48550/arxiv.2310.15406 |
format | Article |
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used to spread misinformation. It is therefore important to understand the
factors people use to interpret visualizations. In this paper, we focus on
factors that influence interpretations of scatter plots, investigating the
extent to which common visual aspects of scatter plots (outliers and trend
lines) and cognitive biases (people's beliefs) influence perception of
correlation trends. We highlight three main findings: outliers skew trend
perception but exert less influence than other points; trend lines make trends
seem stronger but also mitigate the influence of some outliers; and people's
beliefs have a small influence on perceptions of weak, but not strong
correlations. From these results we derive guidelines for adjusting visual
elements to mitigate the influence of factors that distort interpretations of
scatter plots. We explore how these guidelines may generalize to other
visualization types and make recommendations for future studies.</description><identifier>DOI: 10.48550/arxiv.2310.15406</identifier><language>eng</language><subject>Computer Science - Computers and Society ; Computer Science - Graphics ; Computer Science - Human-Computer Interaction ; Computer Science - Multimedia ; Computer Science - Social and Information Networks</subject><creationdate>2023-10</creationdate><rights>http://creativecommons.org/licenses/by-nc-sa/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,782,887</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2310.15406$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2310.15406$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Filipowicz, Alexandre</creatorcontrib><creatorcontrib>Carter, Scott</creatorcontrib><creatorcontrib>Bravo, Nayeli</creatorcontrib><creatorcontrib>Iliev, Rumen</creatorcontrib><creatorcontrib>Hakimi, Shabnam</creatorcontrib><creatorcontrib>Shamma, David Ayman</creatorcontrib><creatorcontrib>Lyons, Kent</creatorcontrib><creatorcontrib>Hogan, Candice</creatorcontrib><creatorcontrib>Wu, Charlene</creatorcontrib><title>Visual Elements and Cognitive Biases Influence Interpretations of Trends in Scatter Plots</title><description>Visualizations are common methods to convey information but also increasingly
used to spread misinformation. It is therefore important to understand the
factors people use to interpret visualizations. In this paper, we focus on
factors that influence interpretations of scatter plots, investigating the
extent to which common visual aspects of scatter plots (outliers and trend
lines) and cognitive biases (people's beliefs) influence perception of
correlation trends. We highlight three main findings: outliers skew trend
perception but exert less influence than other points; trend lines make trends
seem stronger but also mitigate the influence of some outliers; and people's
beliefs have a small influence on perceptions of weak, but not strong
correlations. From these results we derive guidelines for adjusting visual
elements to mitigate the influence of factors that distort interpretations of
scatter plots. We explore how these guidelines may generalize to other
visualization types and make recommendations for future studies.</description><subject>Computer Science - Computers and Society</subject><subject>Computer Science - Graphics</subject><subject>Computer Science - Human-Computer Interaction</subject><subject>Computer Science - Multimedia</subject><subject>Computer Science - Social and Information Networks</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj1FLwzAUhfPig0x_gE_mD3TmNmnTPGqZOhhMsAh7KnfNjQS6dCTZ0H9vnT6dw_ngwMfYHYilaqpKPGD88udlKecBKiXqa7b78OmEI1-NdKCQE8dgeTt9Bp_9mfiTx0SJr4MbTxQGmlumeIyUMfspJD453kUKNnEf-PuAecb8bZxyumFXDsdEt_-5YN3zqmtfi832Zd0-bgqsdV1AYw0ZhQDUWCzNXlbGCqhR2hqcUUZobXXlBAzOki6V1BoMaGhKIq0buWD3f7cXt_4Y_QHjd__r2F8c5Q9N0Uwq</recordid><startdate>20231023</startdate><enddate>20231023</enddate><creator>Filipowicz, Alexandre</creator><creator>Carter, Scott</creator><creator>Bravo, Nayeli</creator><creator>Iliev, Rumen</creator><creator>Hakimi, Shabnam</creator><creator>Shamma, David Ayman</creator><creator>Lyons, Kent</creator><creator>Hogan, Candice</creator><creator>Wu, Charlene</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20231023</creationdate><title>Visual Elements and Cognitive Biases Influence Interpretations of Trends in Scatter Plots</title><author>Filipowicz, Alexandre ; Carter, Scott ; Bravo, Nayeli ; Iliev, Rumen ; Hakimi, Shabnam ; Shamma, David Ayman ; Lyons, Kent ; Hogan, Candice ; Wu, Charlene</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-18d9e94a11e8da29b359d016a3d61f949077d75f01cfde7243771917182ee7783</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Computers and Society</topic><topic>Computer Science - Graphics</topic><topic>Computer Science - Human-Computer Interaction</topic><topic>Computer Science - Multimedia</topic><topic>Computer Science - Social and Information Networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Filipowicz, Alexandre</creatorcontrib><creatorcontrib>Carter, Scott</creatorcontrib><creatorcontrib>Bravo, Nayeli</creatorcontrib><creatorcontrib>Iliev, Rumen</creatorcontrib><creatorcontrib>Hakimi, Shabnam</creatorcontrib><creatorcontrib>Shamma, David Ayman</creatorcontrib><creatorcontrib>Lyons, Kent</creatorcontrib><creatorcontrib>Hogan, Candice</creatorcontrib><creatorcontrib>Wu, Charlene</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Filipowicz, Alexandre</au><au>Carter, Scott</au><au>Bravo, Nayeli</au><au>Iliev, Rumen</au><au>Hakimi, Shabnam</au><au>Shamma, David Ayman</au><au>Lyons, Kent</au><au>Hogan, Candice</au><au>Wu, Charlene</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Visual Elements and Cognitive Biases Influence Interpretations of Trends in Scatter Plots</atitle><date>2023-10-23</date><risdate>2023</risdate><abstract>Visualizations are common methods to convey information but also increasingly
used to spread misinformation. It is therefore important to understand the
factors people use to interpret visualizations. In this paper, we focus on
factors that influence interpretations of scatter plots, investigating the
extent to which common visual aspects of scatter plots (outliers and trend
lines) and cognitive biases (people's beliefs) influence perception of
correlation trends. We highlight three main findings: outliers skew trend
perception but exert less influence than other points; trend lines make trends
seem stronger but also mitigate the influence of some outliers; and people's
beliefs have a small influence on perceptions of weak, but not strong
correlations. From these results we derive guidelines for adjusting visual
elements to mitigate the influence of factors that distort interpretations of
scatter plots. We explore how these guidelines may generalize to other
visualization types and make recommendations for future studies.</abstract><doi>10.48550/arxiv.2310.15406</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Computers and Society Computer Science - Graphics Computer Science - Human-Computer Interaction Computer Science - Multimedia Computer Science - Social and Information Networks |
title | Visual Elements and Cognitive Biases Influence Interpretations of Trends in Scatter Plots |
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