Skip to main content

Picture Classification into Different Levels of Narrativity Using Subconscious Processes and Behavioral Data: An EEG Study

  • Conference paper
  • First Online:
Information Systems and Neuroscience (NeuroIS 2022)

Part of the book series: Lecture Notes in Information Systems and Organisation ((LNISO,volume 58))

Included in the following conference series:

Abstract

In this study, the narrativity of pictures is evaluated using behavioral scales and subconscious processes. The narrative context of the stimulus pictures was classified into four different Levels. For eliciting evoked potentials (EPs), a P300-based picture ranking system was adopted. The EPs were analyzed on significant differences between seen/unseen and Levels of the pictures. In the first paradigm, pictures were continuously presented for 15 s, and the subjects were asked to focus on the picture’s narrative. In the second paradigm, the pictures were randomly flashed, whereby one of the previously presented images was chosen as the target and unseen (non-target) pictures across Levels. The preliminary results from this Work in Progress (WIP) study show that seen images cause significantly different EPs compared to unseen images, especially in pictures with abstract and dramatic narratives. Therefore, target stimuli are ranked higher by the picture ranking system. In addition, the N600 potential is evident with abstract narrative stimuli, which have been previously reported to indicate memory function and post-perceptual processing. Further investigation will focus on differences in ERPs and ranking results across Levels and the extraction of possible EEG-biomarkers for narrative Levels in visual stimuli.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
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

Similar content being viewed by others

References

  1. Escalas, J. E. (2007). Self‐referencing and persuasion: Narrative transportation versus analytical elaboration. Journal of Consumer Research, 33, 421–429. https://doi.org/10.1086/510216

  2. Kalaganis, F. P., Georgiadis, K., Oikonomou, V. P., et al. (2021). Unlocking the subconscious consumer bias: A survey on the past, present, and future of hybrid EEG schemes in neuromarketing. Frontiers in Neuroergonomics, 2.

    Google Scholar 

  3. Nanay, B. (2009). Narrative pictures. Journal of Aesthetics and Art Criticism, 67, 119–129. https://doi.org/10.1111/j.1540-6245.2008.01340.x

    Article  Google Scholar 

  4. Ryan, M.-L. (2007). Toward a definition of narrative. In D. Herman (Ed.), The Cambridge companion to narrative (1st ed., pp. 22–36). Cambridge University Press.

    Chapter  Google Scholar 

  5. Jääskeläinen, I. P., Klucharev, V., Panidi, K., & Shestakova, A. N. (2020). Neural Processing of Narratives: From Individual Processing to Viral Propagation. Frontiers in Human Neuroscience, 14, 253. https://doi.org/10.3389/fnhum.2020.00253

    Article  Google Scholar 

  6. Birba, A., Beltrán, D., Martorell Caro, M., et al. (2020). Motor-system dynamics during naturalistic reading of action narratives in first and second language. NeuroImage, 216, 116820. https://doi.org/10.1016/j.neuroimage.2020.116820

    Article  Google Scholar 

  7. Cohen, M. X. (2008). Assessing transient cross-frequency coupling in EEG data. Journal of Neuroscience Methods, 168, 494–499. https://doi.org/10.1016/j.jneumeth.2007.10.012

    Article  Google Scholar 

  8. Dini, H., Simonetti, A., Bigne, E., & Bruni, L. E. (2022). EEG theta and N400 responses to congruent versus incongruent brand logos. Science and Reports, 12, 4490. https://doi.org/10.1038/s41598-022-08363-1

    Article  Google Scholar 

  9. Pritchard, W. S. (1981). Psychophysiology of P300. Psychological Bulletin, 89, 506–540. https://doi.org/10.1037/0033-2909.89.3.506

    Article  Google Scholar 

  10. Harauzov, A. K., Shelepin, Y. E., Noskov, Y. A., et al. (2016). The time course of pattern discrimination in the human brain. Vision Research, 125, 55–63. https://doi.org/10.1016/j.visres.2016.05.005

    Article  Google Scholar 

  11. Sutaj, N., Walchshofer, M., & Schreiner, L., et al. (2021). Evaluating a novel P300-based real-time image ranking BCI. Frontiers in Computer Science, 3.

    Google Scholar 

  12. Dimoka, A., Davis, F. D., Gupta, A., et al. (2012). On the use of neurophysiological tools in IS research: Developing a research agenda for NeuroIS. MIS Quarterly, 36, 679–702. https://doi.org/10.2307/41703475

    Article  Google Scholar 

  13. Müller-Putz, G., Riedl, R., & Wriessnegger, S. (2015). Electroencephalography (EEG) as a research tool in the information systems discipline: Foundations, measurement, and applications. Communications of the Association for Information Systems, 37, 911–948. https://doi.org/10.17705/1CAIS.03746

  14. Riedl, R., Fischer, T., Léger, P.-M., & Davis, F. D. (2020). A decade of NeuroIS research: Progress, challenges, and future directions. SIGMIS Database, 51, 13–54. https://doi.org/10.1145/3410977.3410980

    Article  Google Scholar 

  15. Müller-Putz, G. R., Tunkowitsch, U., Minas, R. K., et al. (2021). On electrode layout in EEG studies: 13th annual information systems and neuroscience, NeuroIS 2021. Information Systems and Neuroscience—NeuroIS Retreat, 2021, 90–95. https://doi.org/10.1007/978-3-030-88900-5_10

    Article  Google Scholar 

  16. Riedl, R., Minas, R., Dennis, A., & Müller-Putz, G. (2020). Consumer-grade EEG instruments: Insights on the measurement quality based on a literature review and implications for NeuroIS research (pp. 350–361).

    Google Scholar 

  17. Busselle, R., & Bilandzic, H. (2009). Measuring narrative engagement. Media Psychology, 12, 321–347. https://doi.org/10.1080/15213260903287259

    Article  Google Scholar 

  18. Joshi, A., Kale, S., Chandel, S., & Pal, D. (2015). Likert scale: Explored and explained. BJAST, 7, 396–403. https://doi.org/10.9734/BJAST/2015/14975

    Article  Google Scholar 

  19. Cai, X., Ding, C., Nie, F., & Huang, H. (2013). On the equivalent of low-rank linear regressions and linear discriminant analysis based regressions.

    Google Scholar 

  20. Althen, H., Banaschewski, T., Brandeis, D., & Bender, S. (2020). Stimulus probability affects the visual N700 component of the event-related potential. Clinical Neurophysiology, 131, 655–664. https://doi.org/10.1016/j.clinph.2019.11.059

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially funded via the European Commission project RHUMBO—H2020-MSCA-ITN-2018-813234.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Leonhard Schreiner .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Schreiner, L., Dini, H., Pretl, H., Bruni, L.E. (2022). Picture Classification into Different Levels of Narrativity Using Subconscious Processes and Behavioral Data: An EEG Study. In: Davis, F.D., Riedl, R., vom Brocke, J., Léger, PM., Randolph, A.B., Müller-Putz, G.R. (eds) Information Systems and Neuroscience. NeuroIS 2022. Lecture Notes in Information Systems and Organisation, vol 58. Springer, Cham. https://doi.org/10.1007/978-3-031-13064-9_34

Download citation

Publish with us

Policies and ethics