Skip to main content

Modulation of Beta Power as a Function of Attachment Style and Feedback Valence

  • Conference paper
  • First Online:
Brain Informatics (BI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13974))

Included in the following conference series:

Abstract

Attachment theory is concerned with the basic level of social connection associated with approach and withdrawal mechanisms. Consistent patterns of attachment may be divided into two major categories: secure and insecure. As secure and insecure attachment style individuals vary in terms of their responses to affective stimuli and negatively valanced cues, the goal of this study was to examine whether there are differences in Beta power activation between secure and insecure individuals to feedback given while performing the arrow flanker task. An interaction emerged between Attachment style (secure or insecure) and Feedback type (success or failure) has shown differences in Beta power as a function of both independent factors. These results corroborate previous findings indicating that secure and insecure individuals differently process affective stimuli.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
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. Fearon, R.P., Roisman, G.I.: Attachment theory: progress and future directions. Curr. Opin. Psychol. 15, 131–136 (2017)

    Article  Google Scholar 

  2. Hazan, C., Shaver, P.: Romantic love conceptualized as an attachment process. J. Pers. Soc. Psychol. 52, 511 (1987)

    Article  Google Scholar 

  3. Cassidy, J., Shaver, P.R.: Handbook of Attachment: Theory, Research, and Clinical Applications. Rough Guides (2002)

    Google Scholar 

  4. Freeman, H., Brown, B.B.: Primary attachment to parents and peers during adolescence: differences by attachment style. J. Youth Adolesc. 30, 653–674 (2001)

    Article  Google Scholar 

  5. Farina, B., et al.: Della: memories of attachment hamper EEG cortical connectivity in dissociative patients. Eur. Arch. Psychiatry Clin. Neurosci. 264, 449–458 (2014)

    Article  Google Scholar 

  6. Nasiriavanaki, Z., et al.: Anxious attachment is associated with heightened responsivity of a parietofrontal cortical network that monitors peri-personal space. NeuroImage Clin. 30, 102585 (2021). AD

    Google Scholar 

  7. Fraley, R.C., Waller, N.G., Brennan, K.A.: An item response theory analysis of self-report measures of adult attachment. J. Personal. Soc. Psychol. 78, 350 (2000)

    Article  Google Scholar 

  8. Ridderinkhof, K.R., Wylie, S.A., van den Wildenberg, W.P.M., Bashore, T.R., van der Molen, M.W.: The arrow of time: advancing insights into action control from the arrow version of the Eriksen flanker task. Attention, Percept. Psychophys. 83, 700–721 (2021)

    Article  Google Scholar 

  9. Jain, Anil K.: Data clustering: 50 years beyond K-means. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008. LNCS (LNAI), vol. 5211, pp. 3–4. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87479-9_3

    Chapter  Google Scholar 

  10. Kodinariya, T.M.: Review on determining number of cluster in K-means clustering. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 1, 90–95 (2013)

    Google Scholar 

  11. Magai, C., Cohen, C., Milburn, N., Thorpe, B., McPherson, R., Peralta, D.: Attachment styles in older European American and African American adults. J. Gerontol. Ser. B Psychol. Sci. Soc. Sci. 56, S28–S35 (2001)

    Google Scholar 

  12. Brunetti, M., Zappasodi, F., Croce, P., Di Matteo, R.: Parsing the Flanker task to reveal behavioral and oscillatory correlates of unattended conflict interference. Sci. Rep. 9, 1–11 (2019)

    Article  Google Scholar 

  13. Renard, Y., et al.: Openvibe: an open-source software platform to design, test, and use brain–computer interfaces in real and virtual environments. Presence Teleoperators Virtual Environ. 19, 35–53 (2010)

    Article  Google Scholar 

  14. Mizrahi, D., Laufer, I., Zuckerman, I.: Topographic analysis of cognitive load in tacit coordination games based on electrophysiological measurements. In: Davis, F.D., Riedl, R., vom Brocke, J., Léger, P.-M., Randolph, A.B., Müller-Putz, G. (eds.) NeuroIS 2021. LNISO, vol. 52, pp. 162–171. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-88900-5_18

    Chapter  Google Scholar 

  15. Gartner, M., Grimm, S., Bajbouj, M.: Frontal midline theta oscillations during mental arithmetic: effects of stress. Front. Behav. Neurosci. 9, 1–8 (2015)

    Article  Google Scholar 

  16. Boudewyn, M., Roberts, B.M., Mizrak, E., Ranganath, C., Carter, C.S.: Prefrontal transcranial direct current stimulation (tDCS) enhances behavioral and EEG markers of proactive control. Cogn. Neurosci. 10, 57–65 (2019)

    Article  Google Scholar 

  17. Laufer, I., Mizrahi, D., Zuckerman, I.: An electrophysiological model for assessing cognitive load in tacit coordination games. Sensors. 22, 477 (2022)

    Article  Google Scholar 

  18. Mizrahi, D., Zuckerman, I., Laufer, I.: the effect of social value orientation on theta to alpha ratio in resource allocation games. Information 14, 146 (2023)

    Article  Google Scholar 

  19. Jensen, A., la Cour-Harbo, A.: Ripples in Mathematics: The Discrete Wavelet Transform. Springer, Heidelberg (2001)

    Book  MATH  Google Scholar 

  20. Rioul, O., Duhamel, P.: Fast algorithms for discrete and continuous wavelet transforms. IEEE Trans. Inf. theory. 38, 569–586 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  21. Shensa, M.J.: The discrete wavelet transform: wedding the a trous and Mallat algorithms. IEEE Trans. Signal Process. 40(10), 2464–2482 (1992)

    Article  MATH  Google Scholar 

  22. Mizrahi, D., Zuckerman, I., Laufer, I.: Analysis of Alpha Band Decomposition in Different Level-k Scenarios with Semantic Processing. In: Mahmud, M., He, J., Vassanelli, S., van Zundert, A., Zhong, N. (eds.) Brain Informatics. BI 2022. LNCS, vol. 13406, pp. 65–73. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-15037-1_6

  23. Grecucci, A., Theuninck, A., Frederickson, J., Job, R.: Mechanisms of social emotion regulation: From neuroscience to psychotherapy. In: Handbook of Emotion Regulation. Nova Publishers (2015)

    Google Scholar 

  24. Békés, V., Aafjes-van Doorn, K., Spina, D., Talia, A., Starrs, C.J., Perry, J.C.: The relationship between defense mechanisms and attachment as measured by observer-rated methods in a sample of depressed patients: a pilot study. Front. Psychol. 4152 (2021)

    Google Scholar 

  25. Zuckerman, I., Mizrahi, D., Laufer, I.: EEG pattern classification of picking and coordination using anonymous random walks. Algorithms. 15, 114 (2022)

    Article  Google Scholar 

  26. Al-Fahoum, A.S., Al-Fraihat, A.A.: Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains. ISRN Neurosci (2014)

    Google Scholar 

  27. Mizrahi, D., Laufer, I., Zuckerman, I.: Level-K classification from EEG signals using transfer learning. Sensors. 21, 7908 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dor Mizrahi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 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

Mizrahi, D., Laufer, I., Zuckerman, I. (2023). Modulation of Beta Power as a Function of Attachment Style and Feedback Valence. In: Liu, F., Zhang, Y., Kuai, H., Stephen, E.P., Wang, H. (eds) Brain Informatics. BI 2023. Lecture Notes in Computer Science(), vol 13974. Springer, Cham. https://doi.org/10.1007/978-3-031-43075-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43075-6_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43074-9

  • Online ISBN: 978-3-031-43075-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics