Skip to main content
SpringerLink
Log in

Towards Understanding Neuroscience of Realisation of Information Need in Light of Relevance and Satisfaction Judgement

  • Conference paper
  • First Online:
Machine Learning, Optimization, and Data Science (LOD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13163))

Abstract

Understanding how to satisfy searchers’ information need (IN) is the main goal of Information Retrieval (IR) systems. In this study, we investigate the relationships between information need and the two key concepts of relevance and satisfaction from the perspective of neuroscience. We utilise functional Magnetic Resonance Imaging (fMRI) to measure the brain activity of twenty-four participants during performing a Question Answering (Q/A) task that, following the realisation of an information need, included the opportunity to initiate searches and evaluate returned documents. We contrast brain activity between the time of realisation of information need (IN) and two other periods, relevance judgement (RJ), i.e. IN vs RJ, and satisfaction judgement (SJ), i.e. IN vs SJ. To interpret these results, we use meta-analytic techniques of reverse inference to identify the functional significance of the discovered brain regions. The results provide consistent evidence of the involvement of several cognitive functions, including imagery, attention, planning, calculation and working memory. Our findings lead us to obtain a better understanding associated with the characteristic of information need and its relationships to relevance and satisfaction.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.neurosynth.org.

  2. 2.

    http://www.talairach.org.

  3. 3.

    https://github.com/neurosynth/neurosynth.

References

  1. Allegretti, M., Moshfeghi, Y., Hadjigeorgieva, M., Pollick, F.E., Jose, J.M., Pasi, G.: 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. ACM (2015)

    Google Scholar 

  2. Baddeley, A.: Working memory: looking back and looking forward. Nature Rev. Neurosci. 4(10), 829 (2003)

    Article  Google Scholar 

  3. Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. Ser. B (Methodol.) 57, 289–300 (1995)

    MathSciNet  MATH  Google Scholar 

  4. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)

    MATH  Google Scholar 

  5. Borlund, P.: The concept of relevance in IR. J. Am. Soc. Inf. Sci. Technol 54(10), 913–925 (2003)

    Article  Google Scholar 

  6. Chang, L.J., Yarkoni, T., Khaw, M.W., Sanfey, A.G.: Decoding the role of the insula in human cognition: functional parcellation and large-scale reverse inference. Cereb. Cortex 23(3), 739–749 (2012)

    Article  Google Scholar 

  7. Eugster, M.J., et al.: Predicting term-relevance from brain signals. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 425–434. ACM (2014)

    Google Scholar 

  8. Finc, K., Bonna, K., He, X., Lydon-Staley, D.M., Kühn, S., Duch, W., Bassett, D.S.: Dynamic reconfiguration of functional brain networks during working memory training. Nature Commun. 11(1), 1–15 (2020)

    Google Scholar 

  9. Goebel, R.: Brainvoyager qx, vers. 2.1. Brain Innovation BV, Maastricht, Netherlands (2017)

    Google Scholar 

  10. Griffiths, J.R., Johnson, F., Hartley, R.J.: User satisfaction as a measure of system performance. J. Librarianship Inf. Sci. 39(3), 142–152 (2007)

    Article  Google Scholar 

  11. Gwizdka, J., Hosseini, R., Cole, M., Wang, S.: Temporal dynamics of eye-tracking and EEG during reading and relevance decisions. J. Assoc. Inf. Sci. Technol. 68(10), 2299–2312 (2017)

    Article  Google Scholar 

  12. Gwizdka, J., Moshfeghi, Y., Wilson, M.L., et al.: Introduction to the special issue on neuro-information science. J. Assoc. Inf. Sci. Technol. 70(9), 911–916 (2019)

    Google Scholar 

  13. Gwizdka, J., Mostafa, J.: Neuroir 2015: Sigir 2015 workshop on neuro-physiological methods in IR research. In: ACM Sigir Forum, vol. 49, pp. 83–88. ACM, New York (2016)

    Google Scholar 

  14. Henson, R.: Forward inference using functional neuroimaging: dissociations versus associations. Trends Cogn. Sci. 10(2), 64–69 (2006)

    Article  MathSciNet  Google Scholar 

  15. Jansen, B.J., Booth, D., Smith, B.: Using the taxonomy of cognitive learning to model online searching. Inf. Process. Manag. 45(6), 643–663 (2009)

    Article  Google Scholar 

  16. Kauppi, J.P., et al.: Towards brain-activity-controlled information retrieval: decoding image relevance from meg signals. NeuroImage 112, 288–298 (2015)

    Article  Google Scholar 

  17. Kelly, D.: Methods for evaluating interactive information retrieval systems with users. Found. Trends Inf. Retrieval 3(1–2), 1–224 (2009)

    Google Scholar 

  18. Kelly, D., Fu, X.: Eliciting better information need descriptions from users of information search systems. Inf. Process. Manag. 43(1), 30–46 (2007)

    Article  Google Scholar 

  19. Kuhlthau, C.C.: Inside the search process: information seeking from the user’s perspective. J. Am. Soc. Inf. Sci. 42(5), 361–371 (1991)

    Article  Google Scholar 

  20. Kuhlthau, C.C.: A principle of uncertainty for information seeking. J. Documentation 49(4), 339–355 (1993)

    Article  Google Scholar 

  21. Lacadie, C.M., Fulbright, R.K., Rajeevan, N., Constable, R.T., Papademetris, X.: More accurate talairach coordinates for neuroimaging using non-linear registration. Neuroimage 42(2), 717–725 (2008)

    Article  Google Scholar 

  22. Liu, M., Liu, Y., Mao, J., Luo, C., Zhang, M., Ma, S.: “Satisfaction with failure” or “unsatisfied success”: investigating the relationship between search success and user satisfaction (2018)

    Google Scholar 

  23. Moshfeghi, Y., Pinto, L.R., Pollick, F.E., Jose, J.M.: Understanding relevance: an fMRI study. In: Serdyukov, P., et al. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 14–25. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36973-5_2

    Chapter  Google Scholar 

  24. Moshfeghi, Y., Pollick, F.E.: Search process as transitions between neural states. In: Proceedings of the 2018 World Wide Web Conference, pp. 1683–1692 (2018)

    Google Scholar 

  25. Moshfeghi, Y., Pollick, F.E.: Neuropsychological model of the realization of information need. J. Assoc. Inf. Sci. Technol 70(9), 954–967 (2019)

    Google Scholar 

  26. Moshfeghi, Y., Triantafillou, P., Pollick, F.: Towards predicting a realisation of an information need based on brain signals. In: The World Wide Web Conference, pp. 1300–1309 (2019)

    Google Scholar 

  27. Moshfeghi, Y., Triantafillou, P., Pollick, F.E.: 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. ACM (2016)

    Google Scholar 

  28. Oldfield, R.C.: The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9(1), 97–113 (1971)

    Article  Google Scholar 

  29. Pinkosova, Z., McGeown, W.J., Moshfeghi, Y.: The cortical activity of graded relevance. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 299–308 (2020)

    Google Scholar 

  30. Poldrack, R.A.: Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding. Neuron 72(5), 692–697 (2011)

    Article  Google Scholar 

  31. Poldrack, R.A.: The future of fMRI in cognitive neuroscience. Neuroimage 62(2), 1216–1220 (2012)

    Article  Google Scholar 

  32. Poldrack, R.A., Mumford, J.A., Schonberg, T., Kalar, D., Barman, B., Yarkoni, T.: Discovering relations between mind, brain, and mental disorders using topic mapping. PLoS Comput. Biol. 8(10), e1002707 (2012)

    Article  Google Scholar 

  33. Power, J.D., et al.: Functional network organization of the human brain. Neuron 72(4), 665–678 (2011)

    Article  Google Scholar 

  34. Saracevic, T.: Relevance: a review of and a framework for the thinking on the notion in information science. J. Assoc. Inf. Sci. Technol 26(6), 321–343 (1975)

    Google Scholar 

  35. Schamber, L., Eisenberg, M.B., Nilan, M.S.: A re-examination of relevance: toward a dynamic, situational definition. Inf. Process. Manag. 26(6), 755–776 (1990)

    Article  Google Scholar 

  36. Shine, J., Poldrack, R.: Principles of dynamic network reconfiguration across diverse brain states. Neuroimage 180(part b), 396–405 (2018)

    Article  Google Scholar 

  37. Swanson, D.R.: Subjective versus objective relevance in bibliographic retrieval systems. Libr. Q. 56(4), 389–398 (1986)

    Article  Google Scholar 

  38. de la Vega, A., Chang, L.J., Banich, M.T., Wager, T.D., Yarkoni, T.: Large-scale meta-analysis of human medial frontal cortex reveals tripartite functional organization. J. Neurosci 36(24), 6553–6562 (2016)

    Article  Google Scholar 

  39. Wendelken, C.: Meta-analysis: how does posterior parietal cortex contribute to reasoning? Front. Hum. Neurosci. 8, 1042 (2015)

    Article  Google Scholar 

  40. Wilson, T.D.: Models in information behaviour research. J. Documentation 55(3), 249–270 (1999)

    Article  Google Scholar 

  41. Yarkoni, T., Poldrack, R.A., Nichols, T.E., Van Essen, D.C., Wager, T.D.: Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8(8), 665 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Glasgow, Glasgow, UK

    Sakrapee Paisalnan & Frank Pollick

  2. University of Strathclyde, Glasgow, UK

    Yashar Moshfeghi

Authors
  1. Sakrapee Paisalnan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frank Pollick
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yashar Moshfeghi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sakrapee Paisalnan .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. Department of Computer Science, University of Reading, Reading, UK

    Varun Ojha

  3. Department of Computer Science, University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. Cambridge Judge Business School, University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Giorgio Jansen

  6. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Department of Informatics, Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Paisalnan, S., Pollick, F., Moshfeghi, Y. (2022). Towards Understanding Neuroscience of Realisation of Information Need in Light of Relevance and Satisfaction Judgement. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science(), vol 13163. Springer, Cham. https://doi.org/10.1007/978-3-030-95467-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-95467-3_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95466-6

  • Online ISBN: 978-3-030-95467-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation