{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T10:19:25Z","timestamp":1783937965085,"version":"3.55.0"},"reference-count":83,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T00:00:00Z","timestamp":1722643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["72271053"],"award-info":[{"award-number":["72271053"]}]},{"name":"National Natural Science Foundation of China","award":["71871056"],"award-info":[{"award-number":["71871056"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Non-expert users often find it challenging to perceive the reliability of computer vision systems accurately. In human\u2013computer decision-making applications, users\u2019 perceptions of system reliability may deviate from the probabilistic characteristics. Intuitive visualization of system recognition results within probability distributions can serve to enhance interpretability and support cognitive processes. Different visualization formats may impact users\u2019 reliability perceptions and cognitive abilities. This study first compared the mapping relationship between users\u2019 perceived values of system recognition results and the actual probabilistic characteristics of the distribution when using density strips, violin plots, and error bars to visualize normal distributions. The findings indicate that when density strips are used for visualization, users\u2019 perceptions align most closely with the probabilistic integrals, exhibiting the shortest response times and highest cognitive arousal. However, users\u2019 perceptions often exceed the actual probability density, with an average coefficient of 2.53 times, unaffected by the form of uncertainty visualization. Conversely, this perceptual bias did not appear in triangular distributions and remained consistent across symmetric and asymmetric distributions. The results of this study contribute to a better understanding of user reliability perception for interaction designers, helping to improve uncertainty visualization and thereby mitigate perceptual biases and potential trust risks.<\/jats:p>","DOI":"10.3390\/sym16080986","type":"journal-article","created":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T18:21:40Z","timestamp":1722882100000},"page":"986","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing User Perception of Reliability in Computer Vision: Uncertainty Visualization for Probability Distributions"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0418-0456","authenticated-orcid":false,"given":"Xinyue","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruoyu","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengqi","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.patcog.2018.03.027","article-title":"Spatial and Class Structure Regularized Sparse Representation Graph for Semi-Supervised Hyperspectral Image Classification","volume":"81","author":"Shao","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3774","DOI":"10.1109\/TCSVT.2021.3113041","article-title":"Instance-Based Feature Pyramid for Visual Object Tracking","volume":"32","author":"Pi","year":"2022","journal-title":"IEEE Trans. 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