IX Lab Publications

IX Lab Publications

Note: list of publications is generated automatically with Zotpress. Thanks to Katie Seaborn for the useful plug-in!

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Chang, Y.-S., Zhang, Y., & Gwizdka, J. (2021). The effects of information source and eHealth literacy on consumer health information credibility evaluation behavior. Computers in Human Behavior, 115, 106629. https://doi.org/10.1016/j.chb.2020.106629 Cite
Bautista, J. R., Zhang, Y., & Gwizdka, J. (2021). Healthcare professionals’ acts of correcting health misinformation on social media. International Journal of Medical Informatics, 104375. https://doi.org/10.1016/j.ijmedinf.2021.104375 Cite
Gwizdka, J. (2021). “Overloading” Cognitive (Work)Load:  What Are We Really Measuring? Information Systems and Neuroscience. NeuroIS Retreat 2021. Cite
Wu, D., Dong, J., Shi, L., Liu, C., & Ding, J. (2020). Credibility assessment of good abandonment results in mobile search. Information Processing & Management, 57(6), 102350. https://doi.org/10.1016/j.ipm.2020.102350 Cite
Bhattacharya, N., Rakshit, S., & Gwizdka, J. (2020). Towards Real-time Webpage Relevance Prediction UsingConvex Hull Based Eye-tracking Features. ACM Symposium on Eye Tracking Research and Applications, 1–10. https://doi.org/10.1145/3379157.3391302 Cite
Bhattacharya, N., Rakshit, S., Gwizdka, J., & Kogut, P. (2020). Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks. Proceedings of the 2020 Conference on Human Information Interaction and Retrieval, 223–233. https://doi.org/10.1145/3343413.3377960 Cite
Tsai, T.-H., Lin, W.-Y., Chang, Y.-S., Chang, P.-C., & Lee, M.-Y. (2020). Technology anxiety and resistance to change behavioral study of a wearable cardiac warming system using an extended TAM for older adults. PLOS ONE, 15(1), e0227270. https://doi.org/10.1371/journal.pone.0227270 Cite
Jia, C., & Gwizdka, J. (2020). An eye-tracking study of differences in reading between automated and human-written news. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, A. Randolph, & T. Fischer (Eds.), NeuroIS’2020. Cite
Gwizdka, J., & Chang, Y.-S. (2020). Search Results Viewing Behavior vis-à-vis Relevance Criteria. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, A. Randolph, & T. Fischer (Eds.), Information Systems and Neuroscience (pp. 181–188). Springer International Publishing. https://doi.org/10.1007/978-3-030-28144-1_20 Cite
Gwizdka, J., & Dillon, A. (2020). Eye-Tracking as a Method for Enhancing Research on Information Search. In W. T. Fu & H. van Oostendorp (Eds.), Understanding and Improving Information Search: A Cognitive Approach (pp. 161–181). Springer International Publishing. https://doi.org/10.1007/978-3-030-38825-6_9 Cite
Bhattacharya, N., Li, Q., & Gurari, D. (2019, October). Why Does a Visual Question Have Different Answers? The IEEE International Conference on Computer Vision (ICCV). Cite
Soboczenski, F., Trikalinos, T. A., Kuiper, J., Bias, R. G., Wallace, B. C., & Marshall, I. J. (2019). Machine learning to help researchers evaluate biases in clinical trials: a prospective, randomized user study. BMC Medical Informatics & Decision Making, 19(1), N.PAG-N.PAG. rzh. https://doi.org/10.1186/s12911-019-0814-z Cite
Sun, Y., Zhang, Y., Gwizdka, J., & Trace, C. B. (2019). Consumer Evaluation of the Quality of Online Health Information: Systematic Literature Review of Relevant Criteria and Indicators. Journal of Medical Internet Research, 21(5), e12522. https://doi.org/10.2196/12522 Cite
Gwizdka, J., Zhang, Y., & Dillon, A. (2019). Using the eye-tracking method to study consumer online health information search behaviour. Aslib Journal of Information Management, 71(6), 739–754. https://doi.org/10.1108/AJIM-02-2019-0050 Cite
Ebeid, I. A., Bhattacharya, N., Gwizdka, J., & Sarkar, A. (2019). Analyzing Gaze Transition Behavior Using Bayesian Mixed Effects Markov Models. Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications, 5:1–5:5. https://doi.org/10.1145/3314111.3319839 Cite
Gwizdka, J. (2019). Exploring Eye-Tracking Data for Detection of Mind-Wandering on Web Tasks. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, & A. B. Randolph (Eds.), Information Systems and Neuroscience (pp. 47–55). Springer International Publishing. https://doi.org/10.1007/978-3-030-01087-4_6 Cite
Gwizdka, J., Moshfeghi, Y., & Wilson, M. L. (2019). Introduction to the special issue on neuro-information science. Journal of the Association for Information Science and Technology, 70(9), 911–916. https://doi.org/10.1002/asi.24263 Cite
Bhattacharya, N., & Gwizdka, J. (2019). Measuring Learning During Search: Differences in Interactions, Eye-Gaze, and Semantic Similarity to Expert Knowledge. Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, 63–71. https://doi.org/10.1145/3295750.3298926 Cite
Bilal, D., & Gwizdka, J. (2018). Children’s query types and reformulations in Google search. Information Processing & Management, 54(6), 1022–1041. https://doi.org/10.1016/j.ipm.2018.06.008 Cite
Chang, Y.-S., & Gwizdka, J. (2018). Relevance criteria dynamics: A study of online news selection on SERPs. Proceedings of the Association for Information Science and Technology, 55(1), 768–769. https://doi.org/10.1002/pra2.2018.14505501108 Cite
Gwizdka, J. (2018). Neuro-physiological data as a source of evaluation metrics for personalized IR. Proceedings of the Workshop on Evaluation of Personalisation in Information Retrieval - WEPIR’2018 Held at ACM SIGIR CHIIR’2018. Workshop on Evaluation of Personalisation in Information Retrieval - WEPIR’2018, New Brunswick, NJ, USA. Cite
Gwizdka, J. (2018). Inferring Web Page Relevance Using Pupillometry and Single Channel EEG. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, & A. B. Randolph (Eds.), Information Systems and Neuroscience (74; pp. 175–183). Springer International Publishing. https://doi.org/10.1007/978-3-319-67431-5_20 Cite
Bhattacharya, N., & Gwizdka, J. (2018). Relating Eye-tracking Measures with Changes in Knowledge on Search Tasks. Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications, 62:1–62:5. https://doi.org/10.1145/3204493.3204579 Cite
Ebeid, I. A., & Gwizdka, J. (2018). Real-time Gaze Transition Entropy. Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications, 94:1–94:3. https://doi.org/10.1145/3204493.3208340 Cite
Gwizdka, J., Hosseini, R., Cole, M., & Wang, S. (2017). Temporal dynamics of eye-tracking and EEG during reading and relevance decisions. Journal of the Association for Information Science and Technology, 68(10), 2299–2312. https://doi.org/10.1002/asi.23904 Cite
Eickhoff, C., Gwizdka, J., Hauff, C., & He, J. (2017). Introduction to the special issue on search as learning. Information Retrieval Journal, 20(5), 399–402. https://doi.org/10.1007/s10791-017-9315-9 Cite
Smith, C. L., Gwizdka, J., & Feild, H. (2017). The use of query auto-completion over the course of search sessions with multifaceted information needs. Information Processing & Management, 53(5), 1139–1155. https://doi.org/10.1016/j.ipm.2017.05.001 Cite
Tsai, T.-H., Chang, H.-T., Chen, Y.-J., & Chang, Y.-S. (2017). Determinants of user acceptance of a specific social platform for older adults: An empirical examination of user interface characteristics and behavioral intention. PLOS ONE, 12(8), e0180102. https://doi.org/10.1371/journal.pone.0180102 Cite
Ye, Z., Gwizdka, J., Lopes, C. T., & Zhang, Y. (2017). Towards understanding consumers’ quality evaluation of online health information: A case study. Proceedings of the Association for Information Science and Technology, 54, 838–839. https://doi.org/10.1002/pra2.2017.14505401178 Cite
Tsai, T.-H., Chang, H.-T., Chang, Y.-C., & Chang, Y.-S. (2017). Personality disclosure on social network sites: An empirical examination of differences in Facebook usage behavior, profile contents and privacy settings. Computers in Human Behavior, 76, 469–482. https://doi.org/10.1016/j.chb.2017.08.003 Cite
Tsai, T.-H., Tseng, K. C., & Chang, Y.-S. (2017). Testing the usability of smartphone surface gestures on different sizes of smartphones by different age groups of users. Computers in Human Behavior, 75, 103–116. https://doi.org/10.1016/j.chb.2017.05.013 Cite
Gwizdka, J. (2017). Differences in Reading Between Word Search and Information Relevance Decisions: Evidence from Eye-Tracking. In D. F. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, & B. A. Randolph (Eds.), Information Systems and Neuroscience: Gmunden Retreat on NeuroIS 2016 (pp. 141–147). Springer International Publishing. https://doi.org/10.1007/978-3-319-41402-7_18 Cite
Gwizdka, J. (2017). I Can and So I Search More: Effects Of Memory Span On Search Behavior. Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval, 341–344. https://doi.org/10.1145/3020165.3022148 Cite
Gwizdka, J., & Bilal, D. (2017). Analysis of Children’s Queries and Click Behavior on Ranked Results and Their Thought Processes in Google Search. Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval, 377–380. https://doi.org/10.1145/3020165.3022157 Cite
Gwizdka, J., & Mostafa, J. (2017). NeuroIIR 2017: Challenges in Bringing Neuroscience to Research in Human-Information Interaction. Proceedings of the 2017 ACM on Conference on Human Information Interaction and Retrieval. https://doi.org/10.1145/3020165.3022165 Cite
Radhakrishnan, K., Toprac, P., O’Hair, M., Bias, R., Kim, M. T., Bradley, P., & Mackert, M. (2016). Interactive digital e-health game for heart failure self-management: A feasibility study. Games for Health, 5(6), 366–374. psyh. https://doi.org/10.1089/g4h.2016.0038 Cite
RADHAKRISHNAN, K., TOPRAC, P., O’HAIR, M., BIAS, R., MACKERT, M., XIE, B., KIM, M., & BRADLEY, P. (2016). Perceptions of Older Adults with Heart Failure on Playing an Interactive Digital e-Health Game (IDEG) for Learning About Heart Failure (HF): Prototype Development and Usability Testing. Studies in Health Technology & Informatics, 225, 1026–1027. rzh. https://doi.org/10.3233/978-1-61499-658-3-1026 Cite
Wang, S., Gwizdka, J., & Chaovalitwongse, W. A. (2016). Using Wireless EEG Signals to Assess Memory Workload in the n-Back Task. IEEE Transactions on Human-Machine Systems, 46(3), 424–435. https://doi.org/10.1109/THMS.2015.2476818 Cite
Gwizdka, J., & Mostafa, J. (2016). NeuroIR 2015: SIGIR 2015 Workshop on Neuro-Physiological Methods in IR Research. SIGIR Forum, 49, 83–88. https://doi.org/10.1145/2888422.2888435 Cite
Gwizdka, J., Hansen, P., Hauff, C., He, J., & Kando, N. (Eds.). (2016). Proceedings of the Second International Workshop on Search as Learning co-located with the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016) (Vol. 1647). CEUR. http://ceur-ws.org/Vol-1647/ Cite
Gwizdka, J., Hansen, P., Hauff, C., He, J., & Kando, N. (2016). Search As Learning (SAL) Workshop 2016. Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1249–1250. https://doi.org/10.1145/2911451.2917766 Cite
Gwizdka, J., & Chen, X. (2016). Towards Observable Indicators of Learning on Search. Proceedings of the Second International Workshop on Search as Learning Co-Located with the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016). http://ceur-ws.org/Vol-1647/SAL2016_paper_19.pdf Cite
Smith, C. L., Gwizdka, J., & Feild, H. (2016). Exploring the Use of Query Auto Completion: Search Behavior and Query Entry Profiles. Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval, 101–110. https://doi.org/10.1145/2854946.2854975 Cite
Bilal, D., & Gwizdka, J. (2016). Children’s Eye-fixations on Google Search Results. Proceedings of the 79th ASIS&T Annual Meeting, 79, 89:1–89:6. http://onlinelibrary.wiley.com.ezproxy.lib.utexas.edu/doi/10.1002/pra2.2016.14505301089/abstract. https://doi.org/10.1002/pra2.2016.14505301089 Cite
Mostafa, J., & Gwizdka, J. (2016). Deepening the Role of the User: Neuro-Physiological Evidence As a Basis for Studying and Improving Search. Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval, 63–70. https://doi.org/10.1145/2854946.2854979 Cite
Bias, R., & Gillan, D. (2015). Why widget design is giving us fitts. Interactions, 22(6), 66–68. https://doi.org/10.1145/2834799 Cite
Bias, R. G., Moon, B. M., & Hoffman, R. R. (2015). Concept Mapping Usability Evaluation: An Exploratory Study of a New Usability Inspection Method. International Journal of Human-Computer Interaction, 31(9), 571–583. cph. http://ezproxy.lib.utexas.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=cph&AN=109171709&site=ehost-live Cite
Huang, S.-C., Bias, R. G., & Schnyer, D. (2015). How are icons processed by the brain? Neuroimaging measures of four types of visual stimuli used in information systems. Journal of the Association for Information Science & Technology, 66(4), 702–720. https://doi.org/10.1002/asi.23210 Cite
Gwizdka, J., & Zhang, Y. (2015). Towards Inferring Web Page Relevance – An Eye-Tracking Study. Proceedings of IConference’2015, 5. https://www.ideals.illinois.edu/handle/2142/73709 Cite
Gwizdka, J., Jose, J., Mostafa, J., & Wilson, M. (2015). NeuroIR 2015: Neuro-Physiological Methods in IR Research. Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1151–1153. https://doi.org/10.1145/2766462.2767856 Cite