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A Field Dependence-Independence Perspective on Eye Gaze Behavior within Affective Activities

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Human-Computer Interaction – INTERACT 2021 (INTERACT 2021)

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Abstract

Evidence suggests that human cognitive differences affect users’ visual behavior within various tasks and activities. However, a human cognitive processing perspective on the interplay between visual and affective aspects remains up-to-date understudied. In this paper, we aim to investigate this relationship by adopting an accredited cognitive style framework (Field Dependence-Independence – FD-I) and provide empirical evidence on main interaction effects between human cognition and emotional processing towards eye gaze behavior. For doing so, we designed and implemented an eye tracking study (n = 22) in which participants were initially classified according to their FD-I cognitive processing characteristics, and were further exposed to a series of images, which triggered specific emotional valence. Analysis of results yield that affective images had a different effect on FD and FI users in terms of visual information exploration time and comprehension, which was reflected on eye gaze metrics. Findings highlight a hidden and rather unexplored effect between human cognition and emotions towards eye gaze behavior, which could lead to a more holistic and comprehensive approach in affective computing.

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References

  1. Picard, R.: Affective Computing. MIT Press, , Cambridge (1997)

    Google Scholar 

  2. Picard, R.: Affective Computing for HCI. Conference on Human-Computer Interaction: Ergonomics and User Interfaces-Volume I. Lawrence Erlbaum Associates (1999)

    Google Scholar 

  3. Norman, D.: Emotional Design: Why we Love (or Hate) Everyday Things. Basic Books, New York (2003)

    Google Scholar 

  4. Natasha, J., Conati, C., Harley, J., Azevedo, R.: Predicting affect from gaze data during interaction with an intelligent tutoring system. intelligent tutoring systems (2014)

    Google Scholar 

  5. Lemos, J., Sadeghnia, G., Ólafsdóttir, Í., Jensen, O.: Measuring emotions using eye tracking (2008)

    Google Scholar 

  6. Schmid, P., Mast, M., Mast, F., Lobmaier, J.: How mood states affect information processing during facial emotion recognition: an eye tracking study. Swiss J. Psychol. 70, 223–231 (2011)

    Article  Google Scholar 

  7. Sears, C., Kristin, N., Ference, J., Thomas, C.: Attention to emotional images in previously depressed individuals: an eye-tracking study. Cogn. Therapy Res. 35, 517–528 (2011)

    Article  Google Scholar 

  8. Stanley, J., Zhang, X., Fung, H., Isaacowitz, D.: Cultural differences in gaze and emotion recognition: Americans contrast more than Chinese. Emotion 13(1), 36–46 (2013)

    Article  Google Scholar 

  9. Charoenpit, S., Ohkura, M.: Exploring emotion in an e-learning system using eye tracking. In: Symposium on Computational Intelligence in Healthcare and E-Health, pp. 141–147 (2014)

    Google Scholar 

  10. Zheng, W., Dong, B., Lu, B.: Multimodal emotion recognition using EEG and eye tracking data. In: Conference on Engineering in Medicine and Biology Society, pp. 5040–5043 (2014)

    Google Scholar 

  11. Zhen-Fen, S., Chang, Z., Wei-Long, Z., Bao-Liang, L.: Attention evaluation with eye tracking glasses for EEG-based emotion recognition. Neural Eng. 86–89 (2017)

    Google Scholar 

  12. Hübner, R., Volberg, G.: The integration of object levels and their content: a theory of global/local processing and related hemispheric differences. J. Exp. Psychol. Hum. Percept. Perform. 31(3), 520–541 (2005)

    Article  Google Scholar 

  13. Oliva, A.: Coarse blobs or fine edges? Evidence that information diagnosticity changes the perception of complex visual stimuli. Cogn. Psychol. 34(1), 72–107 (1997)

    Article  Google Scholar 

  14. Davidoff, J., Fonteneau, E., Fagot, J.: Local and global processing: observations from a remote culture. Cognition 108(3), 702–709 (2008)

    Article  Google Scholar 

  15. Witkin, H., Moore, C., Goodenough, D., Cox, P.: Field-dependent and field-independent cognitive styles and their educational implications. Res. Bull. 1–64 (1975)

    Google Scholar 

  16. Hong, J., Hwang, M., Tam, K., Lai, Y., Liu, L.: Effects of cognitive style on digital jigsaw puzzle performance: a gridware analysis. Comput. Hum. Behav. 28(3), 920–928 (2012)

    Article  Google Scholar 

  17. Rittschof, K.: Field dependence-independence as visuospatial and executive functioning in working nemory: implications for instructional systems design and research. Educ. Tech. Res. Dev. 58(1), 99–114 (2010)

    Article  Google Scholar 

  18. Belk, M., Fidas, C., Germanakos, P., Samaras, G. : The interplay between humans, technology and user authentication: a cognitive processing perspective. Comput. Hum. Behav. 76, 184–200 (2017). Elsevier

    Google Scholar 

  19. Calder, A., Young, A., Keane, J., Dean, M.: Configural information in facial expression perception. Exp. Psych.: Hum. Percept. Perform. 26, 527–551 (2000)

    Google Scholar 

  20. Prkachin, G.: the effect of orientation on detection and identification of facial expressions of emotion. Br. J. Psychol. 94, 45–62 (2003)

    Article  Google Scholar 

  21. Watson, D., Wiese, D., Vaidya, J., Tellegen, A.: The two general activation systems of affect: structural findings, evolutionary considerations, and psychobiological evidence. J. Pers. Soc. Psychol. 76(5), 820 (1999)

    Article  Google Scholar 

  22. Kensinger, E., Schacter, D.: Processing emotional pictures and words: effect of valence and arousal. Cogn. Affect. Behav. Neurosci. 6, 110–126 (2006)

    Article  Google Scholar 

  23. Costanzi, M., et al.: The effect of emotional valence and arousal on visuo-spatial working memory: incidental emotional learning and memory for object-location. Frontiers Psychol. (2019)

    Google Scholar 

  24. Cahill, L., McGaugh, J.: A novel demonstration of enhanced memory associated with emotional arousal. Conscious. Cogn. 4(4), 410–421 (1995)

    Article  Google Scholar 

  25. Belk, M., Fidas, C., Germanakos, P., Samaras, G.: Do human cognitive differences in information processing affect preference and performance of CAPTCHA?. Int. J. Hum. Comput. Stud. 84, 1–18 (2015). Elsevier

    Google Scholar 

  26. Constantinides, A., Belk, M., Fidas, C., Pitsillides, A.: An eye gaze-driven metric for estimating the strength of graphical passwords based on image hotspots. In: ACM IUI 2020, ACM Press, pp. 33–37 (2020)

    Google Scholar 

  27. Constantinides, A., Belk, M., Fidas, C., Pitsillides, A.: On the accuracy of eye gaze-driven classifiers for predicting image content familiarity in graphical passwords. In: ACM UMAP 2019, ACM Press, pp. 201–205 (2019)

    Google Scholar 

  28. Schmeichel, B., Demaree, H.: Working memory capacity and spontaneous emotion regulation: high capacity predicts self-enhancement in response to negative feedback. Emotion 10, 739–744 (2010)

    Article  Google Scholar 

  29. Oltman, P., Raskin, E., Witkin, H.: A Manual for the Embedded Figures Test. Consulting Psychologists Press, Palo Alto (1971)

    Google Scholar 

  30. Kassner, M., Patera, W., Bulling, A.: Pupil: an open source platform for pervasive eye tracking and mobile gaze-based interaction. In: ACM UbiComp 2014 Adjunct, ACM Press, pp. 1151–1160 (2014)

    Google Scholar 

  31. Emotiv Epoc+ (2021). https://www.emotiv.com/epoc

  32. Katsini, C., Fidas, C., Raptis, G., Belk, M., Samaras, G., Avouris, N.: Influences of human cognition and visual behavior on password security during picture password composition. In: ACM Human Factors in Computing Systems (CHI 2018), ACM Press, p. 87 (2018)

    Google Scholar 

  33. Kurdi, B., Lozano, S., Banaji, M.R.: Introducing the Open Affective Standardized Image Set (OASIS). Behav. Res. Methods 49(2), 457–470 (2016). https://doi.org/10.3758/s13428-016-0715-3

    Article  Google Scholar 

  34. Raptis, G., Katsini, C., Belk, M., Fidas, C., Samaras, G., Avouris, N.: Using eye gaze data and visual activities to infer human cognitive styles: method and feasibility studies. In: Conference on User Modeling, Adaptation and Personalization, pp. 164–173 (2017)

    Google Scholar 

  35. Cardaci, M., Di Gesù, V., Petrou, M., Tabacchi, M.: A fuzzy approach to the evaluation of image complexity. Fuzzy Sets Syst. 160(10), 1474–1484 (2009)

    Article  MathSciNet  Google Scholar 

  36. SciKit Image (2021). https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.shannon_entropy

  37. Krejtz, K., et al.: Gaze Transition Entropy. ACM Trans. Appl. Percept. 13, 1, article 4 (2015)

    Google Scholar 

  38. Fidas, C., Belk, M., Hadjidemetriou, G., Pitsillides, A.: Influences of mixed reality and human cognition on picture passwords: an eye tracking study. In: Lamas, D., Loizides, F., Nacke, L., Petrie, H., Winckler, M., Zaphiris, P. (eds.) INTERACT 2019. LNCS, vol. 11747, pp. 304–313. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29384-0_19

    Chapter  Google Scholar 

  39. Costi, A., Belk, M., Fidas, C., Constantinides, A., Pitsillides, A. : CogniKit: an extensible tool for human cognitive modeling based on eye gaze analysis. ACM Intelligent User Interfaces (IUI Companion 2020), ACM Press, pp. 130–131 (2020)

    Google Scholar 

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Acknowledgements

This research has been partially supported by the EU Horizon 2020 Grant 826278 “Securing Medical Data in Smart Patient-Centric Healthcare Systems” (Serums), the Research and Innovation Foundation (Project DiversePass: COMPLEMENTARY/0916/0182), and the European project TRUSTID - Intelligent and Continuous Online Student Identity Management for Improving Security and Trust in European Higher Education Institutions (Grant Agreement No: 2020–1-EL01-KA226-HE-094869), which is funded by the European Commission within the Erasmus+ 2020 Programme and the Greek State Scholarships Foundation I.K.Y.

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Correspondence to Marios Belk .

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Fidas, C., Belk, M., Constantinides, C., Constantinides, A., Pitsillides, A. (2021). A Field Dependence-Independence Perspective on Eye Gaze Behavior within Affective Activities. In: Ardito, C., et al. Human-Computer Interaction – INTERACT 2021. INTERACT 2021. Lecture Notes in Computer Science(), vol 12932. Springer, Cham. https://doi.org/10.1007/978-3-030-85623-6_6

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  • DOI: https://doi.org/10.1007/978-3-030-85623-6_6

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