IDEAS home Printed from https://ideas.repec.org/a/ibn/ijpsjl/v11y2019i3p46.html
   My bibliography  Save this article

The Predictability of Synchronicity Experience: Results from a Survey of Jungian Analysts

Author

Listed:
  • Robert G. Sacco

Abstract

Fibonacci time patterns may predict future synchronicity experiences (SEs) by forecasting nonlinear dynamical interactions. This study examined if there were differences between observed distributions of SEs matching Fibonacci time patterns compared to expected distributions based on chance. An online survey link was e-mailed to a random sample of Jungian analysts drawn from membership lists of the International Association for Analytical Psychology (IAAP). Two experiments tested the hypothesis that Fibonacci algorithms would predict increased SEs compared to chance. The two Fibonacci algorithms studied were a golden section model (GSM) and harmonic model (HM). Participants reported a total of 41 synchronicities. Statistical analysis showed a significant difference (p < .10) between observed synchronicity matches and expected frequencies based on chance for the HM algorithm, and no significant difference in matches predicted by the GSM algorithm. Synchronicity dynamics showed a predictability range between ±34 days. The article discusses, among other issues, what these findings might mean for theoretical explanations of synchronicity and clinical practice.

Suggested Citation

  • Robert G. Sacco, 2019. "The Predictability of Synchronicity Experience: Results from a Survey of Jungian Analysts," International Journal of Psychological Studies, Canadian Center of Science and Education, vol. 11(3), pages 1-46, September.
    • Handle: RePEc:ibn:ijpsjl:v:11:y:2019:i:3:p:46
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/ijps/article/download/0/0/40461/43129
    Download Restriction: no
    File URL: https://ccsenet.org/journal/index.php/ijps/article/view/0/40461
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dirk Witthaut & Sandro Wimberger & Raffaella Burioni & Marc Timme, 2017. "Classical synchronization indicates persistent entanglement in isolated quantum systems," Nature Communications, Nature, vol. 8(1), pages 1-7, April.
    2. Manfred G Kitzbichler & Marie L Smith & Søren R Christensen & Ed Bullmore, 2009. "Broadband Criticality of Human Brain Network Synchronization," PLOS Computational Biology, Public Library of Science, vol. 5(3), pages 1-13, March.
    3. Leon Glass, 2001. "Synchronization and rhythmic processes in physiology," Nature, Nature, vol. 410(6825), pages 277-284, March.
    4. Bartolo Luque & Lucas Lacasa & Fernando J Ballesteros & Alberto Robledo, 2011. "Feigenbaum Graphs: A Complex Network Perspective of Chaos," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-8, September.
    5. Madeleine Castro & Roger Burrows & Robin Wooffitt, 2014. "The Paranormal is (Still) Normal: The Sociological Implications of a Survey of Paranormal Experiences in Great Britain," Sociological Research Online, , vol. 19(3), pages 30-44, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zueva Marina V, 2018. "A New Look at Stimulation Therapy with Complex-Structured Stimuli in Traumatic Brain Injuries," Global Journal of Addiction & Rehabilitation Medicine, Juniper Publishers Inc., vol. 5(1), pages 12-16, January.
    2. Jorge Calero-Sanz, 2022. "On the Degree Distribution of Haros Graphs," Mathematics, MDPI, vol. 11(1), pages 1-15, December.
    3. Bezsudnov, I.V. & Snarskii, A.A., 2014. "From the time series to the complex networks: The parametric natural visibility graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 53-60.
    4. Federico Malizia & Santiago Lamata-Otín & Mattia Frasca & Vito Latora & Jesús Gómez-Gardeñes, 2025. "Hyperedge overlap drives explosive transitions in systems with higher-order interactions," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    5. Werner, Gerhard, 2013. "Consciousness viewed in the framework of brain phase space dynamics, criticality, and the Renormalization Group," Chaos, Solitons & Fractals, Elsevier, vol. 55(C), pages 3-12.
    6. Ricardo Bioni Liberalquino & Maurizio Monge & Stefano Galatolo & Luigi Marangio, 2018. "Chaotic Itinerancy in Random Dynamical System Related to Associative Memory Models," Mathematics, MDPI, vol. 6(3), pages 1-10, March.
    7. Alexey V. Rusakov & Dmitry A. Tikhonov & Nailya I. Nurieva & Alexander B. Medvinsky, 2021. "Emergence of Self-Organized Dynamical Domains in a Ring of Coupled Population Oscillators," Mathematics, MDPI, vol. 9(6), pages 1-13, March.
    8. Christian Meisel & Alexander Storch & Susanne Hallmeyer-Elgner & Ed Bullmore & Thilo Gross, 2012. "Failure of Adaptive Self-Organized Criticality during Epileptic Seizure Attacks," PLOS Computational Biology, Public Library of Science, vol. 8(1), pages 1-8, January.
    9. Meo, Marcos M. & Iaconis, Francisco R. & Del Punta, Jessica A. & Delrieux, Claudio A. & Gasaneo, Gustavo, 2024. "Multifractal information on reading eye tracking data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    10. King, Annika & Smith, Dallas & Webb, Benjamin, 2024. "Minimal specialization: Coevolution of network structure and dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 651(C).
    11. Nuño, Juan Carlos & Muñoz, Francisco J., 2020. "The partial visibility curve of the Feigenbaum cascade to chaos," Chaos, Solitons & Fractals, Elsevier, vol. 131(C).
    12. Todd Zorick & Mark A Mandelkern, 2013. "Multifractal Detrended Fluctuation Analysis of Human EEG: Preliminary Investigation and Comparison with the Wavelet Transform Modulus Maxima Technique," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-7, July.
    13. Reis, A.S. & Brugnago, E.L. & Viana, R.L. & Batista, A.M. & Iarosz, K.C. & Ferrari, F.A.S. & Caldas, I.L., 2023. "The role of the fitness model in the suppression of neuronal synchronous behavior with three-stage switching control in clustered networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    14. Fingelkurts, Andrew A. & Fingelkurts, Alexander A. & Neves, Carlos F.H., 2013. "Consciousness as a phenomenon in the operational architectonics of brain organization: Criticality and self-organization considerations," Chaos, Solitons & Fractals, Elsevier, vol. 55(C), pages 13-31.
    15. Gois, Sandra R.F.S.M. & Savi, Marcelo A., 2009. "An analysis of heart rhythm dynamics using a three-coupled oscillator model," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2553-2565.
    16. de Godoy, Isabelle Bueno Silva & McGrane-Corrigan, Blake & Mason, Oliver & Moral, Rafael de Andrade & Godoy, Wesley Augusto Conde, 2023. "Plant-host shift, spatial persistence, and the viability of an invasive insect population," Ecological Modelling, Elsevier, vol. 475(C).
    17. Martinez-Saito, Mario, 2022. "Discrete scaling and criticality in a chain of adaptive excitable integrators," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    18. Ausloos, Marcel & Nedic, Olgica & Dekanski, Aleksandar, 2016. "Day of the week effect in paper submission/acceptance/rejection to/in/by peer review journals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 197-203.
    19. Ausloos, Marcel & Nedic, Olgica & Dekanski, Aleksandar & Mrowinski, Maciej J. & Fronczak, Piotr & Fronczak, Agata, 2017. "Day of the week effect in paper submission/acceptance/rejection to/in/by peer review journals. II. An ARCH econometric-like modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 462-474.
    20. Piassi, V.S.M. & Colli, E. & Tufaile, A. & Sartorelli, J.C., 2009. "Arnold family in acoustically forced air bubble formation," Chaos, Solitons & Fractals, Elsevier, vol. 41(3), pages 1041-1049.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibn:ijpsjl:v:11:y:2019:i:3:p:46. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.