Show simple item record

dc.contributor.authorNalcaci, Atilla Alpayen_US
dc.contributor.authorGirgin, Dilaraen_US
dc.contributor.authorBalki, Semihen_US
dc.contributor.authorTalay, Fatihen_US
dc.contributor.authorBoz, Hasan Alpen_US
dc.contributor.authorBalcisoy, Selimen_US
dc.contributor.editorKosara, Robert and Lawonn, Kai and Linsen, Lars and Smit, Noeskaen_US
dc.date.accessioned2019-06-02T18:16:17Z
dc.date.available2019-06-02T18:16:17Z
dc.date.issued2019
dc.identifier.isbn978-3-03868-091-8
dc.identifier.urihttps://doi.org/10.2312/trvis.20191185
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/trvis20191185
dc.description.abstractCognitive bias is a systematic error that introduces drifts and distortions in the human judgment in terms of visual decomposition in the direction of the dominant instance. It has a significant role in decision-making process by means of evaluation of data visualizations. This paper elaborates on the experimental depiction of two cognitive bias types, namely Distinction Bias and Confirmation Bias, through the examination of cognate visual experimentations. The main goal of this implementation is to indicate the existence of cognitive bias in visual analytics systems through the adjustment of data visualization and crowdsourcing in terms of confirmation and distinction biases. Two distinct surveys that include biased and unbiased data visualizations which are related to a given data set were established in order to detect and measure the level of existence of introduced bias types. Practice of crowdsourcing which is provided by Amazon Mechanical Turk have been used for experimentation purposes through prepared surveys. Results statistically indicate that both distinction and confirmation biases has substantial effect and prominent significance on decision-making process.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectEmpirical studies in visualization
dc.subjectVisualization design and evaluation methods
dc.titleDetection of Confirmation and Distinction Biases in Visual Analytics Systemsen_US
dc.description.seriesinformationEuroVis Workshop on Trustworthy Visualization (TrustVis)
dc.description.sectionheadersPapers
dc.identifier.doi10.2312/trvis.20191185
dc.identifier.pages13-17


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record