GUEST EDITORIAL
Introduction to the special issue on neuro-information science
Jacek Gwizdka,
Yashar Moshfeghi,
Max L. Wilson,
Corresponding Author
Jacek Gwizdka
School of Information, University of Texas at Austin, Austin, TX
E-mail: [email protected]Search for more papers by this authorYashar Moshfeghi
Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom
Search for more papers by this authorMax L. Wilson
Computer Science, University of Nottingham, Jubilee Campus, Nottingham, NG81BB United Kingdom
Search for more papers by this authorJacek Gwizdka,
Yashar Moshfeghi,
Max L. Wilson,
Corresponding Author
Jacek Gwizdka
School of Information, University of Texas at Austin, Austin, TX
E-mail: [email protected]Search for more papers by this authorYashar Moshfeghi
Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom
Search for more papers by this authorMax L. Wilson
Computer Science, University of Nottingham, Jubilee Campus, Nottingham, NG81BB United Kingdom
Search for more papers by this author
References
- Ajanki, A. (2013). Inference of relevance for proactive information retrieval. Retrieved from https://aaltodoc.aalto.fi:443/handle/123456789/10962
- 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).
- Ariely, D., & Berns, G.S. (2010). Neuromarketing: The hope and hype of neuroimaging in business. Nature Reviews Neuroscience, 11(4), 284–292.
- 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.
- Brierley-Bowers, P., Sexton, S., & Brown, D. (2011). Measures of autonomic nervous system regulation. Defense Centers of Excellence for Psychological Health and Traumatic Brain Injury (pp. 1–24). Arlington, Virginia.
- Buscher, G., Dengel, A., Biedert, R., & Elst, L.V. (2012). Attentive documents: Eye tracking as implicit feedback for information retrieval and beyond. ACM Transactions on Interactive Intelligent Systems (TiiS), 1(2), 9:1–9:30.
- Camerer, C., Loewenstein, G., & Prelec, D. (2005). Neuroeconomics: How neuroscience can inform economics. Journal of Economic Literature, 43(1), 9–64.
- Cline, R.J.W., & Haynes, K.M. (2001). Consumer health information seeking on the internet: The state of the art. Health Education Research, 16(6), 671–692.
- Cole, C. (2011). A theory of information need for information retrieval that connects information to knowledge. Journal of the American Society for Information Science and Technology, 62(7), 1216–1231.
- Critchley, H.D. (2002). Review: Electrodermal responses: What happens in the brain. The Neuroscientist, 8(2), 132–142.
- Dien, J. (2009). The neurocognitive basis of reading single words as seen through early latency ERPs: A model of converging pathways. Biological Psychology, 80(1), 10–22.
- Dien, J., Michelson, C.A., & Franklin, M.S. (2010). Separating the visual sentence N400 effect from the P400 sequential expectancy effect: Cognitive and neuroanatomical implications. Brain Research, 1355, 126–140.
- Duchowski, A.T. (2007). Eye tracking methodology: Theory & practice. Heidelberg, Berlin: Springer.
- 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. https://doi.org/10.3389/fnsys.2013
- Friston, K.J. (2011). Functional and effective connectivity: A review. Brain Connectivity, 1(1), 13–36.
- Gwizdka, J. (2013). Looking for information relevance in the brain. Gmunden Retreat on NeuroIS, 2013, 14.
- Gwizdka, J. (2014). Characterizing relevance with eye-tracking measures. In Proceedings of the 5th Information Interaction in Context Symposium (pp. 58–67).
- 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). Cham, Switzerland: Springer International Publishing.
- 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: Gmunden Retreat on NeuroIS'2017 (pp. 175–183). Cham, Switzerland: Springer International Publishing.
- 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.
- Gwizdka, J., Moshfeghi, Y., Pollick, F.E., Mostafa, J., & Bergman, O. (2013). Applications of neuroimaging in information science: Challenges and opportunities. In Proceedings of the 76th ASIS&T Annual Meeting: Beyond the Cloud: Rethinking Information Boundaries (pp. 67:1–67:4). Retrieved from http://dl.acm.org/citation.cfm?id=2655780.2655847
- Gwizdka, J., & Mostafa, J. (2016). NeuroIR 2015: SIGIR 2015 workshop on Neuro-physiological methods in IR research. ACM SIGIR Forum, 49, 83–88.
- Gwizdka, J., & Mostafa, J. (2017). NeuroIIR 2017: Challenges in bringing neuroscience to research in human-information interaction. In Proceedings of the 2017 ACM on Conference on Human Information Interaction and Retrieval - SIGIR'2017. New York: ACM Press.
- Gwizdka, J., & Zhang, Y. (2015). Differences in eye-tracking measures between visits and revisits to relevant and irrelevant web pages. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR'2015. (pp. 811–814).
- Herculano-Houzel, S. (2009). The human brain in numbers: A linearly scaled-up primate brain. Frontiers in Human Neuroscience, 3, 31. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2776484.
- Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & van de Weijer, J. (2011). Eye tracking: A comprehensive guide to methods and measures. Oxford, UK: Oxford University Press.
- Jacucci, G., Barral, O., Daee, P., Wenzel, M., Serim, B., Ruotsalo, T., … Blankertz, B. (2019). Integrating neurophysiologic relevance feedback in intent modeling for information retrieval. Journal of the Association for Information Science and Technology, 70(9), 917–930. https://doi.org/10.1002/asi.24161
- Jimenez-Molina, A., Retamal, C., & Lira, H. (2018). Using psychophysiological sensors to assess mental workload during web browsing. Sensors, 18(2), 458.
- Jones, L.M., Wright, K.D., Jack, A.I., Friedman, J.P., Fresco, D.M., Veinot, T., … Moore, S.M. (2019). The relationships between health information behavior and neural processing in African Americans with prehypertension. Journal of the Association for Information Science and Technology, 70(9), 968–980. https://doi.org/10.1002/asi.24098
- Just, M.A., & Carpenter, P.A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87(4), 329–354.
- Kahneman, D., & Beatty, J. (1966). Pupil diameter and load on memory. Science, 154(3756), 1583–1585.
- Kim, H.H., & Kim, Y.H. (2019). ERP/MMR algorithm for classifying topic-relevant and topic-irrelevant visual shots of documentary videos. Journal of the Association for Information Science and Technology, 70(9), 931–941. https://doi.org/10.1002/asi.24179
- Krugman, H.E. (1964). Some applications of pupil measurement. Journal of Marketing Research, 1(4), 15–19.
- Logothetis, N.K. (2008). What we can do and what we cannot do with fMRI. Nature, 453(7197), 869–878.
- Lopes da Silva, F.H., Gonçalves, S.I., & De Munck, J.C. (2009). Electroencephalography (EEG). In Encyclopedia of neuroscience (pp. 849–855). Oxford: Academic Press.
- Maior, H.A., Pike, M., Sharples, S., & Wilson, M.L. (2015). Examining the reliability of using fNIRS in realistic HCI settings for spatial and verbal tasks. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI’15) (pp. 3039–3042).
- Makeig, S. (2009). Electrophysiology: EEG and ERP analysis. In Encyclopedia of neuroscience (pp. 879–882). Oxford: Academic Press.
- Moshfeghi, Y. & Jose, J.M. (2013). 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 (SIGIR '13) (pp. 133–142). New York: ACM.
- 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, et al. (Eds.), Advances in information retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. (pp. 14–25). Berlin, Heidelberg: Springer.
- Moshfeghi, Y., & Pollick, F.E. (2018). Search process as transitions between neural states. In Proceedings of the 2018 World Wide Web Conference (pp. 1683–1692).
- Moshfeghi, Y., & Pollick, F.E. (2019). Neuropsychological model of the realization of information need. Journal of the Association for Information Science and Technology, 70(9), 954–967. https://doi.org/10.1002/asi.24242
- 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 (SIGIR '16) (pp. 335–344). New York: ACM
- Moshfeghi, Y., Triantafillou, P., & Pollick, F.E. (2019). Towards predicting a realisation of an information need based on brain signals. In Proceedings of the 2019 World Wide Web Conference. New York: ACM.
- 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: ACM.
- O'Brien, H.L., Gwizdka, J., Lopatovska, I., & Mostafa, J. (2015). Psycho-physiological methods in information science: Fit or Fad? In iConference 2015 Proceedings. Presented at the iConference 2015. Retrieved from https://www.ideals.illinois.edu/handle/2142/73773
- Ogawa, S., Menon, R.S., Tank, D.W., Kim, S.G., Merkle, H., Ellermann, J.M., & Ugurbil, K. (1993). Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model. Biophysical Journal, 64(3), 803–812.
- 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).
- Onorati, F., Barbieri, R., Mauri, M., Russo, V., & Mainardi, L. (2013). Characterization of affective states by pupillary dynamics and autonomic correlates. Frontiers in Neuroengineering, 6, 9.
- Pirolli, P. (2009). Powers of 10: Modeling complex information-seeking systems at multiple scales. Computer, 42(3), 33–40.
- Preuschoff, K., Hart, B.M.‘t., & Einhäuser, W. (2011). Pupil dilation signals surprise: Evidence for noradrenaline's role in decision making. Frontiers in Decision Neuroscience, 5, 115.
- Rayner, K., Pollatsek, A., Ashby, J., & Clifton Jr., C. (2011). Psychology of Reading (2nd ed.). London, UK: Psychology Press.
- Riedl, R. & Léger, P.-M. (2016). Fundamentals of NeuroIS. Berlin, Heidelberg: Springer.
- Salojärvi, J., Puolamäki, K., & Kaski, S. (2005). Implicit relevance feedback from eye movements. In W. Duch, J. Kacprzyk, E. Oja, & S. Zadrożny (Eds.), Artificial Neural Networks: Biological Inspirations – ICANN 2005 (Vol. 3696, pp. 513–518). Berlin, Heidelberg: Springer. Retrieved from http://www.springerlink.com/content/58k718c7q2g5rkq5/
- Scharinger, C., Kammerer, Y., & Gerjets, P. (2016). 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). Cham, Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-319-40548-3_41
- Simola, J., Salojärvi, J., & Kojo, I. (2008). Using hidden Markov model to uncover processing states from eye movements in information search tasks. Cognitive Systems Research, 9(4), 237–251.
- Slanzi, G., Balazs, J.A., & Velásquez, J.D. (2017). Combining eye tracking, pupil dilation and EEG analysis for predicting web users click intention. Information Fusion, 35, 51–57.
- Soares, J.M., Magalhães, R., Moreira, P.S., Sousa, A., Ganz, E., Sampaio, A., … Sousa, N. (2016). A Hitchhiker's guide to functional magnetic resonance imaging. Frontiers in Neuroscience, 10, 433–435.
- Tran, V.T., & Fuhr, N. (2012). Using eye-tracking with dynamic areas of interest for analyzing interactive information retrieval. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1165–1166).
- Tudor, M., Tudor, L., & Tudor, K.I. (2005). Hans Berger (1873-1941)—the history of electroencephalography. Acta Medica Croatica: Casopis Hravatske Akademije Medicinskih Znanosti, 59(4), 307–313.
- Wu, Y., Liu, Y., Tsai, Y.-H.R., & Yau, S.-T. (2019). Investigating the role of eye movements and physiological signals in search satisfaction prediction using geometric analysis. Journal of the Association for Information Science and Technology, 70(9), 981–999. https://doi.org/10.1002/asi.24240
- Xu, C., & Zhang, Q. (2019). The dominant factor of social tags for users' decision behavior on e-commerce websites: Color or text. Journal of the Association for Information Science and Technology, 70(9), 942–953. https://doi.org/10.1002/asi.24118