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Color inference in visual communication: the meaning of colors in recycling

Overview of attention for article published in Cognitive Research: Principles and Implications, February 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

news
3 news outlets
twitter
4 X users
peer_reviews
1 peer review site

Citations

dimensions_citation
59 Dimensions

Readers on

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74 Mendeley
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Title
Color inference in visual communication: the meaning of colors in recycling
Published in
Cognitive Research: Principles and Implications, February 2018
DOI 10.1186/s41235-018-0090-y
Pubmed ID
Authors

Karen B. Schloss, Laurent Lessard, Charlotte S. Walmsley, Kathleen Foley

Abstract

People interpret abstract meanings from colors, which makes color a useful perceptual feature for visual communication. This process is complicated, however, because there is seldom a one-to-one correspondence between colors and meanings. One color can be associated with many different concepts (one-to-many mapping) and many colors can be associated with the same concept (many-to-one mapping). We propose that to interpret color-coding systems, people perform assignment inference to determine how colors map onto concepts. We studied assignment inference in the domain of recycling. Participants saw images of colored but unlabeled bins and were asked to indicate which bins they would use to discard different kinds of recyclables and trash. In Experiment 1, we tested two hypotheses for how people perform assignment inference. The local assignment hypothesis predicts that people simply match objects with their most strongly associated color. The global assignment hypothesis predicts that people also account for the association strengths between all other objects and colors within the scope of the color-coding system. Participants discarded objects in bins that optimized the color-object associations of the entire set, which is consistent with the global assignment hypothesis. This sometimes resulted in discarding objects in bins whose colors were weakly associated with the object, even when there was a stronger associated option available. In Experiment 2, we tested different methods for encoding color-coding systems and found that people were better at assignment inference when color sets simultaneously maximized the association strength between assigned color-object parings while minimizing associations between unassigned pairings. Our study provides an approach for designing intuitive color-coding systems that facilitate communication through visual media such as graphs, maps, signs, and artifacts.

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X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 74 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 15%
Student > Bachelor 8 11%
Student > Ph. D. Student 7 9%
Student > Doctoral Student 4 5%
Researcher 4 5%
Other 3 4%
Unknown 37 50%
Readers by discipline Count As %
Psychology 8 11%
Social Sciences 6 8%
Engineering 4 5%
Computer Science 3 4%
Business, Management and Accounting 3 4%
Other 12 16%
Unknown 38 51%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 28. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 September 2020.
All research outputs
#1,202,969
of 23,028,364 outputs
Outputs from Cognitive Research: Principles and Implications
#62
of 322 outputs
Outputs of similar age
#29,194
of 331,227 outputs
Outputs of similar age from Cognitive Research: Principles and Implications
#2
of 7 outputs
Altmetric has tracked 23,028,364 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 322 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.8. This one has done well, scoring higher than 80% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 331,227 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.