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.
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References
Fearon, R.P., Roisman, G.I.: Attachment theory: progress and future directions. Curr. Opin. Psychol. 15, 131–136 (2017)
Hazan, C., Shaver, P.: Romantic love conceptualized as an attachment process. J. Pers. Soc. Psychol. 52, 511 (1987)
Cassidy, J., Shaver, P.R.: Handbook of Attachment: Theory, Research, and Clinical Applications. Rough Guides (2002)
Freeman, H., Brown, B.B.: Primary attachment to parents and peers during adolescence: differences by attachment style. J. Youth Adolesc. 30, 653–674 (2001)
Farina, B., et al.: Della: memories of attachment hamper EEG cortical connectivity in dissociative patients. Eur. Arch. Psychiatry Clin. Neurosci. 264, 449–458 (2014)
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
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)
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)
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
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)
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)
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)
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)
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
Gartner, M., Grimm, S., Bajbouj, M.: Frontal midline theta oscillations during mental arithmetic: effects of stress. Front. Behav. Neurosci. 9, 1–8 (2015)
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)
Laufer, I., Mizrahi, D., Zuckerman, I.: An electrophysiological model for assessing cognitive load in tacit coordination games. Sensors. 22, 477 (2022)
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)
Jensen, A., la Cour-Harbo, A.: Ripples in Mathematics: The Discrete Wavelet Transform. Springer, Heidelberg (2001)
Rioul, O., Duhamel, P.: Fast algorithms for discrete and continuous wavelet transforms. IEEE Trans. Inf. theory. 38, 569–586 (1992)
Shensa, M.J.: The discrete wavelet transform: wedding the a trous and Mallat algorithms. IEEE Trans. Signal Process. 40(10), 2464–2482 (1992)
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
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)
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)
Zuckerman, I., Mizrahi, D., Laufer, I.: EEG pattern classification of picking and coordination using anonymous random walks. Algorithms. 15, 114 (2022)
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)
Mizrahi, D., Laufer, I., Zuckerman, I.: Level-K classification from EEG signals using transfer learning. Sensors. 21, 7908 (2021)
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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
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