{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:21:22Z","timestamp":1781713282519,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":76,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T00:00:00Z","timestamp":1681862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,4,19]]},"DOI":"10.1145\/3544548.3581320","type":"proceedings-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T04:26:08Z","timestamp":1681964768000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Graphical Perception of Saliency-based Model Explanations"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7214-9619","authenticated-orcid":false,"given":"Yayan","family":"Zhao","sequence":"first","affiliation":[{"name":"Vanderbilt University, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0457-8091","authenticated-orcid":false,"given":"Mingwei","family":"Li","sequence":"additional","affiliation":[{"name":"Vanderbilt University, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8876-2418","authenticated-orcid":false,"given":"Matthew","family":"Berger","sequence":"additional","affiliation":[{"name":"Vanderbilt University, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,4,19]]},"reference":[{"key":"e_1_3_3_3_1_1","volume-title":"Sanity checks for saliency maps. Advances in neural information processing systems 31","author":"Adebayo Julius","year":"2018","unstructured":"Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. 2018. Sanity checks for saliency maps. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_3_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377325.3377519"},{"key":"e_1_3_3_3_3_1","volume-title":"Does Explainable Artificial Intelligence Improve Human Decision-Making?Proceedings of the AAAI Conference on Artificial Intelligence 35, 8 (May","author":"Alufaisan Yasmeen","year":"2021","unstructured":"Yasmeen Alufaisan, Laura\u00a0R. Marusich, Jonathan\u00a0Z. Bakdash, Yan Zhou, and Murat Kantarcioglu. 2021. Does Explainable Artificial Intelligence Improve Human Decision-Making?Proceedings of the AAAI Conference on Artificial Intelligence 35, 8 (May 2021), 6618\u20136626."},{"key":"e_1_3_3_3_4_1","unstructured":"Marco Ancona Enea Ceolini Cengiz \u00d6ztireli and Markus Gross. 2017. Towards better understanding of gradient-based attribution methods for deep neural networks. arXiv preprint arXiv:1711.06104(2017)."},{"key":"e_1_3_3_3_5_1","volume-title":"6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net.","author":"Ancona Marco","year":"2018","unstructured":"Marco Ancona, Enea Ceolini, Cengiz \u00d6ztireli, and Markus Gross. 2018. Towards better understanding of gradient-based attribution methods for Deep Neural Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net."},{"key":"e_1_3_3_3_6_1","volume-title":"Physical determinants of the judged complexity of shapes.Journal of experimental Psychology 53, 4","author":"Attneave Fred","year":"1957","unstructured":"Fred Attneave. 1957. Physical determinants of the judged complexity of shapes.Journal of experimental Psychology 53, 4 (1957), 221."},{"key":"e_1_3_3_3_7_1","volume-title":"Proceedings, Part XV","author":"Bae Wonho","year":"2020","unstructured":"Wonho Bae, Junhyug Noh, and Gunhee Kim. 2020. Rethinking Class Activation Mapping for Weakly Supervised Object Localization. In Computer Vision \u2013 ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XV (Glasgow, United Kingdom). Springer-Verlag, Berlin, Heidelberg, 618\u2013634."},{"key":"e_1_3_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1531326.1531392"},{"key":"e_1_3_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.354"},{"key":"e_1_3_3_3_10_1","volume-title":"GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. In 7th International Conference on Learning Representations, ICLR 2019","author":"Bau David","year":"2019","unstructured":"David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua\u00a0B. Tenenbaum, William\u00a0T. Freeman, and Antonio Torralba. 2019. GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net."},{"key":"e_1_3_3_3_11_1","volume-title":"Map lineups: effects of spatial structure on graphical inference","author":"Beecham Roger","year":"2016","unstructured":"Roger Beecham, Jason Dykes, Wouter Meulemans, Aidan Slingsby, Cagatay Turkay, and Jo Wood. 2016. Map lineups: effects of spatial structure on graphical inference. IEEE transactions on visualization and computer graphics 23, 1(2016), 391\u2013400."},{"key":"e_1_3_3_3_12_1","volume-title":"The influence of complexity and novelty in visual figures on orienting responses.Journal of experimental psychology 55, 3","author":"Berlyne E","year":"1958","unstructured":"Daniel\u00a0E Berlyne. 1958. The influence of complexity and novelty in visual figures on orienting responses.Journal of experimental psychology 55, 3 (1958), 289."},{"key":"e_1_3_3_3_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3491102.3501965"},{"key":"e_1_3_3_3_14_1","doi-asserted-by":"publisher","DOI":"10.32614\/RJ-2017-066"},{"key":"e_1_3_3_3_15_1","volume-title":"The good, the bad, and the ugly: A theoretical framework for the assessment of continuous colormaps","author":"Bujack Roxana","year":"2017","unstructured":"Roxana Bujack, Terece\u00a0L Turton, Francesca Samsel, Colin Ware, David\u00a0H Rogers, and James Ahrens. 2017. The good, the bad, and the ugly: A theoretical framework for the assessment of continuous colormaps. IEEE transactions on visualization and computer graphics 24, 1(2017), 923\u2013933."},{"key":"e_1_3_3_3_16_1","volume-title":"Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics(Proceedings of Machine Learning Research, Vol.\u00a084)","author":"Chen Yuxin","year":"2018","unstructured":"Yuxin Chen, Oisin Mac\u00a0Aodha, Shihan Su, Pietro Perona, and Yisong Yue. 2018. Near-Optimal Machine Teaching via Explanatory Teaching Sets. In Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics(Proceedings of Machine Learning Research, Vol.\u00a084), Amos Storkeyand Fernando Perez-Cruz (Eds.). PMLR, 1970\u20131978."},{"key":"e_1_3_3_3_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2014.6907100"},{"key":"e_1_3_3_3_18_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1984.10478080"},{"key":"e_1_3_3_3_19_1","doi-asserted-by":"crossref","unstructured":"Elijah Cole Kimberly Wilber Grant Van\u00a0Horn Xuan Yang Marco Fornoni Pietro Perona Serge Belongie Andrew Howard and Oisin Mac\u00a0Aodha. 2022. On Label Granularity and Object Localization. arXiv preprint arXiv:2207.10225(2022).","DOI":"10.1007\/978-3-031-20080-9_35"},{"key":"e_1_3_3_3_20_1","volume-title":"Real time image saliency for black box classifiers. Advances in neural information processing systems 30","author":"Dabkowski Piotr","year":"2017","unstructured":"Piotr Dabkowski and Yarin Gal. 2017. Real time image saliency for black box classifiers. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_3_3_21_1","unstructured":"Fahim Dalvi Nadir Durrani Hassan Sajjad Yonatan Belinkov Anthony Bau and James Glass. 2019. What is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP Models. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence (Honolulu Hawaii USA) (AAAI\u201919\/IAAI\u201919\/EAAI\u201919). AAAI Press Article 774 9\u00a0pages."},{"key":"e_1_3_3_3_22_1","volume-title":"The effect of color scales on climate scientists","author":"Dasgupta Aritra","year":"2018","unstructured":"Aritra Dasgupta, Jorge Poco, Bernice Rogowitz, Kyungsik Han, Enrico Bertini, and Claudio\u00a0T Silva. 2018. The effect of color scales on climate scientists\u2019 objective and subjective performance in spatial data analysis tasks. IEEE transactions on visualization and computer graphics 26, 3(2018), 1577\u20131591."},{"key":"e_1_3_3_3_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_3_3_24_1","volume-title":"How does the brain solve visual object recognition?Neuron 73, 3","author":"DiCarlo J","year":"2012","unstructured":"James\u00a0J DiCarlo, Davide Zoccolan, and Nicole\u00a0C Rust. 2012. How does the brain solve visual object recognition?Neuron 73, 3 (2012), 415\u2013434."},{"key":"e_1_3_3_3_25_1","unstructured":"Finale Doshi-Velez and Been Kim. 2017. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608(2017)."},{"key":"e_1_3_3_3_26_1","volume-title":"Inverting Visual Representations with Convolutional Networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016","author":"Dosovitskiy Alexey","year":"2016","unstructured":"Alexey Dosovitskiy and Thomas Brox. 2016. Inverting Visual Representations with Convolutional Networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016. IEEE Computer Society, 4829\u20134837."},{"key":"e_1_3_3_3_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00331"},{"key":"e_1_3_3_3_28_1","unstructured":"Thomas Fel Julien Colin Remi Cadene and Thomas Serre. 2021. What I Cannot Predict I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods. ArXiv abs\/2112.04417(2021)."},{"key":"e_1_3_3_3_29_1","unstructured":"Thomas Fel Ivan Felipe Drew Linsley and Thomas Serre. 2022. Harmonizing the object recognition strategies of deep neural networks with humans. arXiv preprint arXiv:2211.04533(2022)."},{"key":"e_1_3_3_3_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00304"},{"key":"e_1_3_3_3_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00910"},{"key":"e_1_3_3_3_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.371"},{"key":"e_1_3_3_3_33_1","unstructured":"Jonah Gabry Daniel Simpson Aki Vehtari Michael Betancourt and Andrew Gelman. 2017. Visualization in Bayesian workflow. arXiv preprint arXiv:1709.01449(2017)."},{"key":"e_1_3_3_3_34_1","unstructured":"Shang-Hua Gao Zhong-Yu Li Ming-Hsuan Yang Ming-Ming Cheng Junwei Han and Philip Torr. 2021. Large-scale Unsupervised Semantic Segmentation. arXiv preprint arXiv:2106.03149(2021)."},{"key":"e_1_3_3_3_35_1","volume-title":"Deep learning in drug discovery. Molecular informatics 35, 1","author":"Gawehn Erik","year":"2016","unstructured":"Erik Gawehn, Jan\u00a0A Hiss, and Gisbert Schneider. 2016. Deep learning in drug discovery. Molecular informatics 35, 1 (2016), 3\u201314."},{"key":"e_1_3_3_3_36_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v38i3.2741"},{"key":"e_1_3_3_3_37_1","volume-title":"Ranking visualizations of correlation using weber\u2019s law","author":"Harrison Lane","year":"2014","unstructured":"Lane Harrison, Fumeng Yang, Steven Franconeri, and Remco Chang. 2014. Ranking visualizations of correlation using weber\u2019s law. IEEE transactions on visualization and computer graphics 20, 12(2014), 1943\u20131952."},{"key":"e_1_3_3_3_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_3_3_39_1","volume-title":"Fooling neural network interpretations via adversarial model manipulation. Advances in Neural Information Processing Systems 32","author":"Heo Juyeon","year":"2019","unstructured":"Juyeon Heo, Sunghwan Joo, and Taesup Moon. 2019. Fooling neural network interpretations via adversarial model manipulation. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_3_3_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00505"},{"key":"e_1_3_3_3_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01386"},{"key":"e_1_3_3_3_42_1","volume-title":"Keep CALM and Improve Visual Feature Attribution. In 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021","author":"Kim Jae-Myung","year":"2021","unstructured":"Jae-Myung Kim, Junsuk Choe, Zeynep Akata, and Seong\u00a0Joon Oh. 2021. Keep CALM and Improve Visual Feature Attribution. In 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021. IEEE, 8330\u20138340."},{"key":"e_1_3_3_3_43_1","volume-title":"Proceedings, Part XII","author":"Kim Sunnie","year":"2022","unstructured":"Sunnie S.\u00a0Y. Kim, Nicole Meister, Vikram\u00a0V. Ramaswamy, Ruth Fong, and Olga Russakovsky. 2022. HIVE: Evaluating the Human Interpretability of Visual Explanations. In Computer Vision \u2013 ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part XII (Tel Aviv, Israel). Springer-Verlag, Berlin, Heidelberg, 280\u2013298."},{"key":"e_1_3_3_3_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445522"},{"key":"e_1_3_3_3_45_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3820\u20133828","author":"Mac\u00a0Aodha Oisin","year":"2018","unstructured":"Oisin Mac\u00a0Aodha, Shihan Su, Yuxin Chen, Pietro Perona, and Yisong Yue. 2018. Teaching categories to human learners with visual explanations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3820\u20133828."},{"key":"e_1_3_3_3_46_1","volume-title":"Splatterplots: Overcoming overdraw in scatter plots","author":"Mayorga Adrian","year":"2013","unstructured":"Adrian Mayorga and Michael Gleicher. 2013. Splatterplots: Overcoming overdraw in scatter plots. IEEE transactions on visualization and computer graphics 19, 9(2013), 1526\u20131538."},{"key":"e_1_3_3_3_47_1","doi-asserted-by":"publisher","DOI":"10.2352\/ISSN.2470-1173.2016.16.HVEI-133"},{"key":"e_1_3_3_3_48_1","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3497163"},{"key":"e_1_3_3_3_49_1","first-page":"26422","article-title":"The effectiveness of feature attribution methods and its correlation with automatic evaluation scores","volume":"34","author":"Nguyen Giang","year":"2021","unstructured":"Giang Nguyen, Daeyoung Kim, and Anh Nguyen. 2021. The effectiveness of feature attribution methods and its correlation with automatic evaluation scores. Advances in Neural Information Processing Systems 34 (2021), 26422\u201326436.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_3_50_1","doi-asserted-by":"publisher","DOI":"10.1609\/hcomp.v7i1.5284"},{"key":"e_1_3_3_3_51_1","volume-title":"Evaluating the impact of binning 2d scalar fields","author":"Padilla Lace","year":"2016","unstructured":"Lace Padilla, P\u00a0Samuel Quinan, Miriah Meyer, and Sarah\u00a0H Creem-Regehr. 2016. Evaluating the impact of binning 2d scalar fields. IEEE transactions on visualization and computer graphics 23, 1(2016), 431\u2013440."},{"key":"e_1_3_3_3_52_1","volume-title":"Rise: Randomized input sampling for explanation of black-box models. arXiv preprint arXiv:1806.07421(2018).","author":"Petsiuk Vitali","year":"2018","unstructured":"Vitali Petsiuk, Abir Das, and Kate Saenko. 2018. Rise: Randomized input sampling for explanation of black-box models. arXiv preprint arXiv:1806.07421(2018)."},{"key":"e_1_3_3_3_53_1","volume-title":"Linear and nonlinear mixed effects models, version 3, 1","author":"Pinheiro Jos\u00e9","year":"2017","unstructured":"Jos\u00e9 Pinheiro, Douglas Bates, Saikat DebRoy, Deepayan Sarkar, Siem Heisterkamp, Bert Van\u00a0Willigen, and R Maintainer. 2017. Package \u2018nlme\u2019. Linear and nonlinear mixed effects models, version 3, 1 (2017)."},{"key":"e_1_3_3_3_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445315"},{"key":"e_1_3_3_3_55_1","volume-title":"Proceedings 3rd International Space Syntax Symposium. Citeseer, 28\u20131.","author":"Psarra Sophia","year":"2001","unstructured":"Sophia Psarra and Tadeusz Grajewski. 2001. Describing shape and shape complexity using local properties. In Proceedings 3rd International Space Syntax Symposium. Citeseer, 28\u20131."},{"key":"e_1_3_3_3_56_1","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO.2017.8081219"},{"key":"e_1_3_3_3_57_1","volume-title":"Graphical Perception of Continuous Quantitative Maps: The Effects of Spatial Frequency and Colormap Design(CHI \u201918)","author":"Reda Khairi","unstructured":"Khairi Reda, Pratik Nalawade, and Kate Ansah-Koi. 2018. Graphical Perception of Continuous Quantitative Maps: The Effects of Spatial Frequency and Colormap Design(CHI \u201918). Association for Computing Machinery, New York, NY, USA, 1\u201312."},{"key":"e_1_3_3_3_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_3_3_59_1","volume-title":"Attention-based Interpretability with Concept Transformers. In International Conference on Learning Representations.","author":"Rigotti Mattia","year":"2021","unstructured":"Mattia Rigotti, Christoph Miksovic, Ioana Giurgiu, Thomas Gschwind, and Paolo Scotton. 2021. Attention-based Interpretability with Concept Transformers. In International Conference on Learning Representations."},{"key":"e_1_3_3_3_60_1","volume-title":"Scatterplots: Tasks, data, and designs","author":"Sarikaya Alper","year":"2017","unstructured":"Alper Sarikaya and Michael Gleicher. 2017. Scatterplots: Tasks, data, and designs. IEEE transactions on visualization and computer graphics 24, 1(2017), 402\u2013412."},{"key":"e_1_3_3_3_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_3_3_62_1","volume-title":"Deep learning in medical image analysis. Annual review of biomedical engineering 19","author":"Shen Dinggang","year":"2017","unstructured":"Dinggang Shen, Guorong Wu, and Heung-Il Suk. 2017. Deep learning in medical image analysis. Annual review of biomedical engineering 19 (2017), 221\u2013248."},{"key":"e_1_3_3_3_63_1","doi-asserted-by":"crossref","unstructured":"Hua Shen and Ting-Hao\u00a0\u2019Kenneth\u2019 Huang. 2020. How Useful Are the Machine-Generated Interpretations to General Users? A Human Evaluation on Guessing the Incorrectly Predicted Labels. ArXiv abs\/2008.11721(2020).","DOI":"10.1609\/hcomp.v8i1.7477"},{"key":"e_1_3_3_3_64_1","doi-asserted-by":"publisher","DOI":"10.1167\/jov.20.12.7"},{"key":"e_1_3_3_3_65_1","unstructured":"Karen Simonyan Andrea Vedaldi and Andrew Zisserman. 2013. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034(2013)."},{"key":"e_1_3_3_3_66_1","unstructured":"Daniel Smilkov Nikhil Thorat Been Kim Fernanda Vi\u00e9gas and Martin Wattenberg. 2017. Smoothgrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825(2017)."},{"key":"e_1_3_3_3_67_1","unstructured":"Jost\u00a0Tobias Springenberg Alexey Dosovitskiy Thomas Brox and Martin Riedmiller. 2014. Striving for simplicity: The all convolutional net. arXiv preprint arXiv:1412.6806(2014)."},{"key":"e_1_3_3_3_68_1","volume-title":"A multimodal deep learning system to distinguish late stages of AMD and to compare expert vs. AI ocular biomarkers. Scientific reports 12, 1","author":"Thakoor A","year":"2022","unstructured":"Kaveri\u00a0A Thakoor, Jiaang Yao, Darius Bordbar, Omar Moussa, Weijie Lin, Paul Sajda, and Royce\u00a0WS Chen. 2022. A multimodal deep learning system to distinguish late stages of AMD and to compare expert vs. AI ocular biomarkers. Scientific reports 12, 1 (2022), 1\u201311."},{"key":"e_1_3_3_3_69_1","volume-title":"Visual complexity of websites: Effects on users","author":"Tuch N","year":"2009","unstructured":"Alexandre\u00a0N Tuch, Javier\u00a0A Bargas-Avila, Klaus Opwis, and Frank\u00a0H Wilhelm. 2009. Visual complexity of websites: Effects on users\u2019 experience, physiology, performance, and memory. International journal of human-computer studies 67, 9 (2009), 703\u2013715."},{"key":"e_1_3_3_3_70_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1513198113"},{"key":"e_1_3_3_3_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2010.161"},{"key":"e_1_3_3_3_72_1","volume-title":"Scented widgets: Improving navigation cues with embedded visualizations","author":"Willett Wesley","year":"2007","unstructured":"Wesley Willett, Jeffrey Heer, and Maneesh Agrawala. 2007. Scented widgets: Improving navigation cues with embedded visualizations. IEEE transactions on visualization and computer graphics 13, 6(2007), 1129\u20131136."},{"key":"e_1_3_3_3_73_1","volume-title":"Perceptual proxies for extracting averages in data visualizations. Psychonomic bulletin & review 26, 2","author":"Yuan Lei","year":"2019","unstructured":"Lei Yuan, Steve Haroz, and Steven Franconeri. 2019. Perceptual proxies for extracting averages in data visualizations. Psychonomic bulletin & review 26, 2 (2019), 669\u2013676."},{"key":"e_1_3_3_3_74_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"e_1_3_3_3_75_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-017-1059-x"},{"key":"e_1_3_3_3_76_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"}],"event":{"name":"CHI '23: CHI Conference on Human Factors in Computing Systems","location":"Hamburg Germany","acronym":"CHI '23","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction"]},"container-title":["Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3544548.3581320","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3544548.3581320","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:47:29Z","timestamp":1750178849000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3544548.3581320"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,19]]},"references-count":76,"alternative-id":["10.1145\/3544548.3581320","10.1145\/3544548"],"URL":"https:\/\/doi.org\/10.1145\/3544548.3581320","relation":{},"subject":[],"published":{"date-parts":[[2023,4,19]]},"assertion":[{"value":"2023-04-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}