2018

1. Chang, Y.-S., & Gwizdka, J. (2018). Relevance Criteria Dynamics: A Study of Online News Selection on SERPs. In ASIST’2018 (p. 2).
2. Ruotsalo, T., Peltonen, J., Eugster, M. J. A., G\lowacka, D., Floréen, P., Myllymäki, P., … Kaski, S. (2018). Interactive Intent Modeling for Exploratory Search. ACM Trans. Inf. Syst., 36(4), 44:1–44:46. https://doi.org/10.1145/3231593
3. Salma, N., Mai, B., Namuduri, K., Mamun, R., Hashem, Y., Takabi, H., … Nielsen, R. (2018). Using EEG Signal to Analyze IS Decision Making Cognitive Processes. In Information Systems and Neuroscience (pp. 211–218). Springer, Cham. https://doi.org/10.1007/978-3-319-67431-5_24
4. Weber, B., Neurauter, M., Burattin, A., Pinggera, J., & Davis, C. (2018). Measuring and Explaining Cognitive Load During Design Activities: A Fine-Grained Approach. In Information Systems and Neuroscience (pp. 47–53). Springer, Cham. https://doi.org/10.1007/978-3-319-67431-5_6
5. Gwizdka, J. (2018a). Inferring Web Page Relevance Using Pupillometry and Single Channel EEG. In Information Systems and Neuroscience (pp. 175–183). Springer International Publishing. https://doi.org/10.1007/978-3-319-67431-5_20
6. Gwizdka, J. (2018b). Neuro-physiological data as a source of evaluation metrics for personalized IR. In Proceedings of the Workshop on Evaluation of Personalisation in Information Retrieval - WEPIR’2018 held at ACM SIGIR CHIIR’2018. New Brunswick, NJ, USA.
7. Jimenez-Molina, A., Retamal, C., & Lira, H. (2018). Using Psychophysiological Sensors to Assess Mental Workload During Web Browsing. Sensors, 18(2), 458. https://doi.org/10.3390/s18020458
8. Lu, Q., Zhang, J., Chen, J., & Li, J. (2018). Predicting readers’ domain knowledge based on eye-tracking measures. The Electronic Library, 36(6), 1027–1042. https://doi.org/10.1108/EL-05-2017-0108

2016

1. Adamos, D. A., Dimitriadis, S. I., & Laskaris, N. A. (2016). Towards the bio-personalization of music recommendation systems: A single-sensor EEG biomarker of subjective music preference. Information Sciences, 343–344, 94–108. https://doi.org/10.1016/j.ins.2016.01.005
2. Barral, O., Kosunen, I., Ruotsalo, T., Spapé, M. M., Eugster, M. J. A., Ravaja, N., … Jacucci, G. (2016). Extracting relevance and affect information from physiological text annotation. User Modeling and User-Adapted Interaction, 26(5), 493–520. https://doi.org/10.1007/s11257-016-9184-8
3. Eugster, M. J. A., Ruotsalo, T., Spapé, M. M., Barral, O., Ravaja, N., Jacucci, G., & Kaski, S. (2016). Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals. Scientific Reports, 6, 38580. https://doi.org/10.1038/srep38580
4. 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
5. Kim, Y. H., & Kim, H. H. (2016). Automatic Extraction Techniques of Topic-relevant Visual Shots Using Realtime Brainwave Responses. Journal of Korea Multimedia Society, 19(8), 1260–1274. https://doi.org/10.9717/kmms.2016.19.8.1260
6. Loyola, P., Brunetti, E., Martinez, G., Velásquez, J. D., & Maldonado, P. (2016). Leveraging Neurodata to Support Web User Behavior Analysis. In N. Zhong, J. Ma, J. Liu, R. Huang, & X. Tao (Eds.), Wisdom Web of Things (pp. 181–207). Springer International Publishing. https://doi.org/10.1007/978-3-319-44198-6_8
7. Moshfeghi, Y., Triantafillou, P., & Pollick, F. E. (2016). Understanding Information Need: An fMRI Study. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 335–344). New York, NY, USA: ACM. https://doi.org/10.1145/2911451.2911534
8. Mostafa, J., & Gwizdka, J. (2016). Deepening the Role of the User: Neuro-Physiological Evidence As a Basis for Studying and Improving Search. In Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval (pp. 63–70). New York, NY, USA: ACM. https://doi.org/10.1145/2854946.2854979
9. Scharinger, C., Kammerer, Y., & Gerjets, P. (2016a). Fixation-Related EEG Frequency Band Power Analysis: A Promising Neuro-Cognitive Methodology to Evaluate the Matching-Quality of Web Search Results? In HCI International 2016 – Posters’ Extended Abstracts (pp. 245–250). Springer, Cham. https://doi.org/10.1007/978-3-319-40548-3_41
10. Scharinger, C., Kammerer, Y., & Gerjets, P. (2016b). Fixation-Related EEG Frequency Band Power Analysis: A Promising Neuro-Cognitive Methodology to Evaluate the Matching-Quality of Web Search Results? In C. Stephanidis (Ed.), HCI International 2016 – Posters’ Extended Abstracts (pp. 245–250). Springer International Publishing. https://doi.org/10.1007/978-3-319-40548-3_41
11. Wittek, P., Liu, Y.-H., Darányi, S., Gedeon, T., & Lim, I. S. (2016). Risk and Ambiguity in Information Seeking: Eye Gaze Patterns Reveal Contextual Behavior in Dealing with Uncertainty. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.01790

2015

1. Healy, G. F., Boran, L., & Smeaton, A. F. (2015). Neural Patterns of the Implicit Association Test. Frontiers in Human Neuroscience, 605. https://doi.org/10.3389/fnhum.2015.00605
2. 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 and Technology, 66(4), 702–720. https://doi.org/10.1002/asi.23210
3. Kauppi, J.-P., Kandemir, M., Saarinen, V.-M., Hirvenkari, L., Parkkonen, L., Klami, A., … Kaski, S. (2015). Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals. NeuroImage, 112, 288–298. https://doi.org/10.1016/j.neuroimage.2014.12.079
4. Loyola, P., Martinez, G., Muñoz, K., Velásquez, J. D., Maldonado, P., & Couve, A. (2015). Combining eye tracking and pupillary dilation analysis to identify Website Key Objects. Neurocomputing, 168, 179–189. https://doi.org/10.1016/j.neucom.2015.05.108
5. Mostafa, J., Carrasco, V., Foster, C., & Giovenallo, K. (2015). Identifying Neurological Patterns Associated with Information Seeking: A Pilot fMRI Study. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, & A. B. Randolph (Eds.), Information Systems and Neuroscience (pp. 167–173). Springer International Publishing. Retrieved from http://link.springer.com/chapter/10.1007/978-3-319-18702-0_22
6. Wenzel, M. A., Moreira, C., Lungu, I.-A., Bogojeski, M., & Blankertz, B. (2015). Neural Responses to Abstract and Linguistic Stimuli with Variable Recognition Latency. In SpringerLink (pp. 172–178). Springer, Cham. https://doi.org/10.1007/978-3-319-24917-9_19
7. Allegretti, M., Moshfeghi, Y., Hadjigeorgieva, M., Pollick, F. E., Jose, J. M., & Pasi, G. (2015). When Relevance Judgement is Happening?: An EEG-based Study. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 719–722). New York, NY, USA: ACM. http://doi.org/10.1145/2766462.2767811
8. Gwizdka, J., & Zhang, Y. (2015). Differences in eye-tracking measures between visits / revisits to relevant and irrelevant Web pages. Short paper and poster at SIGIR 2015.
9. Gwizdka, J., & Zhang, Y. (2015). Towards Inferring Web Page Relevance – An Eye-Tracking Study. Poster presented at iConference'2015. Retrieved from https://www.ideals.illinois.edu/handle/2142/73709

2014

1. Eugster, M. J. A., Ruotsalo, T., Spapé, M. M., Kosunen, I., Barral, O., Ravaja, N., … Kaski, S. (2014). Predicting Term-relevance from Brain Signals. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 425–434). New York, NY, USA: ACM. http://doi.org/10.1145/2600428.2609594
2. Gwizdka, J. (2014a). Characterizing Relevance with Eye-tracking Measures. In Proceedings of the 5th Information Interaction in Context Symposium (pp. 58–67). New York, NY, USA: ACM. http://doi.org/10.1145/2637002.2637011
3. Gwizdka, J. (2014b). News Stories Relevance Effects on Eye-movements. In Proceedings of the Symposium on Eye Tracking Research and Applications (pp. 283–286). New York, NY, USA: ACM. http://doi.org/10.1145/2578153.2578198
4. Ševcech, J., & Bieliková, M. (2014). User’s Interest Detection through Eye Tracking for Related Documents Retrieval. In 2014 9th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) (pp. 9–13). http://doi.org/10.1109/SMAP.2014.20
5. Zhang, Y. , Gwizdka, J. (2014). Effects of Tasks at Similar and Different Complexity Level . Poster presented at ASIST 2014.
   

2013

1. Frey, A., Ionescu, G., Lemaire, B., Lopez-Orozco, F., Baccino, T., & Guerin-Dugue, A. (2013). Decision-making in information seeking on texts: an Eye-Fixation-Related Potentials investigation. Frontiers in Systems Neuroscience, 7(39). http://doi.org/10.3389/fnsys.2013.00039
   
2. Gwizdka J. (2013). Looking for Information Relevance In the Brain. Paper presented at NeuroIS 2013. BEST PAPER
3. Moshfeghi, Y., & Jose, J. M. (2013a). An effective implicit relevance feedback technique using affective, physiological and behavioural features. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (pp. 133–142). New York, NY, USA: ACM. http://doi.org/10.1145/2484028.2484074
4. Moshfeghi, Y., & Jose, J. M. (2013b). On Cognition, Emotion, and Interaction Aspects of Search Tasks with Different Search Intentions. In Proceedings of the 22Nd International Conference on World Wide Web (pp. 931–942). Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee. Retrieved from http://dl.acm.org/citation.cfm?id=2488388.2488469
   
5. Moshfeghi, Y., Pinto, L. R., Pollick, F. E., & Jose, J. M. (2013). Understanding Relevance: An fMRI Study. In P. Serdyukov, P. Braslavski, S. O. Kuznetsov, J. Kamps, S. Rüger, E. Agichtein, … E. Yilmaz (Eds.), Advances in Information Retrieval (pp. 14–25). Springer Berlin Heidelberg. Retrieved from http://link.springer.com.ezproxy.lib.utexas.edu/chapter/10.1007/978-3-642-36973-5_2  BEST PAPER
6. Ajanki, A. (2013). Inference of relevance for proactive information retrieval. Retrieved from https://aaltodoc.aalto.fi:443/handle/123456789/10962
   

2012

1. Gwizdka, J. Cole, M. (2012). Towards Neuro–Information Science. Proceedings of Gmunden Retreat on NeuroIS 2012. June 3-6, 2012. Gmunden, Austria. [abstract-PDF] [presentation]
2. Balatsoukas, P., & Ruthven, I. (2012). An eye-tracking approach to the analysis of relevance judgments on the Web: The case of Google search engine. Journal of the American Society for Information Science and Technology, 63(9), 1728–1746. http://doi.org/10.1002/asi.22707
   

1. Fahey, D., Gedeon, T., & Zhu, D. (2011). Document Classification on Relevance: A Study on Eye Gaze Patterns for Reading. In B.-L. Lu, L. Zhang, & J. Kwok (Eds.), Neural Information Processing (pp. 143–150). Springer Berlin Heidelberg. Retrieved from http://link.springer.com/chapter/10.1007/978-3-642-24958-7_17
2. Vo, T., & Gedeon, T. (2011). Reading Your Mind: EEG during Reading Task. In B.-L. Lu, L. Zhang, & J. Kwok (Eds.), Neural Information Processing (pp. 396–403). Springer Berlin Heidelberg. Retrieved from http://link.springer.com/chapter/10.1007/978-3-642-24955-6_48
   
3. Loboda, T. D., Brusilovsky, P., & Brunstein, J. (2011). Inferring word relevance from eye-movements of readers. In Proceedings of the 16th international conference on Intelligent user interfaces (pp. 175–184). New York, NY, USA: ACM. http://doi.org/10.1145/1943403.1943431
   

1. Arapakis, I., Athanasakos, K., & Jose, J. M. (2010). A comparison of general vs personalised affective models for the prediction of topical relevance. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval (pp. 371–378). New York, NY, USA: ACM. http://doi.org/10.1145/1835449.1835512
   

1. Ajanki, A., Hardoon, D., Kaski, S., Puolamäki, K., & Shawe-Taylor, J. (2009). Can eyes reveal interest? Implicit queries from gaze patterns. User Modeling and User-Adapted Interaction, 19(4), 307–339. http://doi.org/10.1007/s11257-009-9066-4
   
2. Arapakis, I., Moshfeghi, Y., Joho, H., Ren, R., Hannah, D., & Jose, J. M. (2009). Enriching User Profiling with Affective Features for the Improvement of a Multimodal Recommender System. In Proceedings of the ACM International Conference on Image and Video Retrieval (pp. 29:1–29:8). New York, NY, USA: ACM. http://doi.org/10.1145/1646396.1646433  BEST STUDENT PAPER
3. Oliveira, F. T. P., Aula, A., & Russell, D. M. (2009). Discriminating the relevance of web search results with measures of pupil size. In Proceedings of the 27th international conference on Human factors in computing systems (pp. 2209–2212). Boston, MA, USA: ACM. http://doi.org/10.1145/1518701.1519038

1. Klami, A., Saunders, C., de Campos, T. E., & Kaski, S. (2008). Can relevance of images be inferred from eye movements? In Proceedings of the 1st ACM international conference on Multimedia information retrieval (pp. 134–140). New York, NY, USA: ACM. http://doi.org/10.1145/1460096.1460120
   
2. Salojärvi, J. (2008). Inferring relevance from eye movements with wrong models. Teknillinen korkeakoulu. Retrieved from http://lib.tkk.fi/Diss/2008/isbn9789512296132/.  (ISBN 978-951-22-9613-2)