Here you will be able to find all references for the original pieces, literature thesis and articles published in the ABC Journal from issue 10 onward.
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Dikker, S., Montgomery, S., & Tunca, S. (2019). Using Synchrony-Based Neurofeedback in Search of Human Connectedness. Brain Art, 161–206. doi:10.1007/978-3-030-14323-7_6
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http://www.suzannedikker.net/mutualwavemachine#mutualwavemachine
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Lai, M. C., Lombardo, M. V., Pasdo, G., Ruigrok, A.N.V., Wheelwright, S.J. & Sadek, S.A. (2011). A Behavioral Comparison of Male and Female Adults with High Functioning Autism Spectrum Conditions. PLoS ONE 6(6): e20835.
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Paul, R., Shriberg, L. D., McSweeny, J., Cicchetti, D., Klin, A. & Volkmar, F. (2005). Brief report: Relations between prosodic performance and communication and socialization ratings in high functioning speakers with autism spectrum disorders. Journal of Autism and Developmental Disorders. 861–869.
Peppé, S., McCann, J., Gibbon, F. E., O’Hare, A., & Rutherford, M. (2006) Assessing prosodic and pragmatic ability in children with high-functioning autism. QMU Speech Science Research Centre Working Papers, WP-4.
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Young, E.C., Diehl, J.J., Morris, D., Hyman, S.L. & Bennetto, L. (2005). The use of two language tests to identify pragmatic language problems in children with autism spectrum disorders. Language, Speech, and Hearing Services in Schools. 62–72.
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Capó, M. À., Cela-Conde, C. J., Munar, E., Rosselló, J., & Nadal, M. (2008). Towards a framework for the study of the neural correlates of aesthetic preference. Spatial Vision, 21(3–5), 379–396. https://doi.org/10.1163/156856808784532653
Cela-Conde, C. J., Marty, G., Munar, E., Nadal, M., & Burges, L. (2002). The “Style Scheme” Grounds Perception of Paintings. Perceptual and Motor Skills, 95(1), 91–100. https://doi.org/10.2466/pms.2002.95.1.91
Dougherty, D. D., Shin, L. M., Alpert, N. M., Pitman, R. K., Orr, S. P., Lasko, M., Macklin, M. L., Fischman, A. J., & Rauch, S. L. (1999). Anger in healthy men: a PET study using script-driven imagery. Biological Psychiatry, 46(4), 466–472. https://doi.org/10.1016/s0006-3223(99)00063
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Jacobs, R. H. A. H., Renken, R., & Cornelissen, F. W. (2012). Neural Correlates of Visual Aesthetics – Beauty as the Coalescence of Stimulus and Internal State. PLoS ONE, 7(2), e31248. https://doi.org/10.1371/journal.pone.0031248
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The neurobiology of beauty | Semir Zeki | TEDxUCL. (2012, July 2). [Video]. YouTube. https://www.youtube.com/watch?v=NlzanAw0RP4
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Zeki, S., Chén, O. Y., & Romaya, J. P. (2018). The Biological Basis of Mathematical Beauty. Frontiers in Human Neuroscience, 12, 23–50. https://doi.org/10.3389/fnhum.2018.00467
Zeki, S., Romaya, J. P., Benincasa, D. M. T., & Atiyah, M. F. (2014). The experience of mathematical beauty and its neural correlates. Frontiers in Human Neuroscience, 8, 34–55. https://doi.org/10.3389/fnhum.2014.00068
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Blewett, A. E. (1992). Abnormal subjective time experience in depression. British Journal of Psychiatry, 161(AUG.), 195–200. https://doi.org/10.1192/bjp.161.2.195
Blom, J. D. (2016). Alice in Wonderland syndrome A systematic review. Neurology: Clinical Practice. Lippincott Williams and Wilkins. https://doi.org/10.1212/CPJ.0000000000000251
Bschor, T., Ising, M., Bauer, M., Lewitzka, U., Skerstupeit, M., Müller-Oerlinghausen, B., & Baethge, C. (2004). Time experience and time judgment in major depression, mania and healthy subjects. A controlled study of 93 subjects. Acta Psychiatrica Scandinavica, 109(3), 222–229. https://doi.org/10.1046/j.0001-690X.2003.00244.x
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Davalos, D. B., Kisley, M. A., & Ross, R. G. (2003). Effects of interval duration on temporal processing in schizophrenia. Brain and Cognition, 52(3), 295–301. https://doi.org/10.1016/S0278-2626(03)00157-X
Elvevåg, B., McCormack, T., Gilbert, A., Brown, G. D. A., Weinberger, D. R., & Goldberg, T. E. (2003). Duration judgements in patients with schizophrenia. Psychological Medicine, 33(7), 1249–1261. https://doi.org/10.1017/S0033291703008122
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Naarden, T., Ter Meulen, B. C., Van Der Weele, S. I., & Blom, J. Di. (2019). Alice in wonderland syndrome as a presenting manifestation of Creutzfeldt-Jakob disease. Frontiers in Neurology, 10(MAY), 1–6. https://doi.org/10.3389/fneur.2019.00473
O’Toole, P., & Modestino, E. J. (2017). Alice in Wonderland Syndrome: A real life version of Lewis Carroll’s novel. Brain and Development, 39(6), 470–474. https://doi.org/10.1016/j.braindev.2017.01.004
Perdices, M. (2018). The Alice in Worderland Syndrome. Neuropsychological Rehabilitation, 28(2), 189–198. https://doi.org/10.1080/09602011.2016.1224191
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Shammas, M. K. (2020). The Curious Case of the Fast Feelers: A Reflection on Alice in Wonderland Syndrome. Pediatric Neurology. https://doi.org/10.1016/j.pediatrneurol.2020.06.004
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Image reference:
An instance of Open Science principles. This specific set of principles guides the Open Traits Network — a global initiative for sharing and integrating trait data across organisms. Adapted with permission from “Open Science principles for accelerating trait-based science across the Tree of Life,” by Gallagher, R. V., Falster, D. S., Maitner, B. et al., 2020, Nature ecology & evolution, 4(3), 294-303.
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1.1 – Single trial power spectra
Single trial power spectra are in general quite noisy. Especially in this research, since there were limited time points due to the task design. Even though, the model was still able to fit the data with an average error of 0.237 over the participants that exhibit theta power. Furthermore, the error is not significantly different between the baseline period or the retention period, nor between good and poor performance, excluding that this is driving the results. Supplementary figure 1 & 2 show the fit of six single trials from a random participant for the retention period and the baseline period. The top row shows the three worst fit (highest error), and the bottom row the three best fits (lowest error). The error is calculated over the whole frequency range 2 to 40 Hz, and thus is not very informative to determine whether the model was able to capture a peak in the theta frequency range (4 – 7 Hz), since the theta frequency range is such a small portion of the whole frequency range. This becomes clear when comparing the top and bottom rows (supplementary figure 1 & 2).
Supplementary figure 1: Six single trials of good performance during the baseline period. These six single trials are from one random participant. The top row are the three worst fits, and in the bottom row are the three best fits. The black line is the single trial power spectrum. The blue line is the aperiodic fit and in red is the full model fit.
Supplementary figure 2: Six single trials of good performance during the retention period. These six single trials are from one random participant. The top row are the three worst fits, and in the bottom row are the three best fits. The black line is the single trial power spectrum. The blue line is the aperiodic fit and in red is the full model fit.
1.2 – Theta frequency ranges
As stated in the discussion (section 4.3), theta power has been described in a variety of different frequency ranges (Adam et al., 2015, 2018; Brzezicka et al., 2015; Jensen & Tesche, 2002). Using a frequency range from 4 to 7 Hz for finding peaks with FOOOF has a harder cut-off frequency than when using a band width filter with the same range. Because filters will pick up power from neighboring frequencies as well, whereas this is not the case with FOOOF. When using a range of 4 to 7 Hz with FOOOF, there was no significant difference in relative theta power between good and poor performance. However, expanding the theta range 1 Hz (4 – 8 Hz) gives different results (supplementary figure 3). Here, the relative power is higher for good performance, compared to poor performance (W = 122, p = 0.031, d = 0.595). Also, relative power is significantly increased from baseline during good performance (t(16) = 2.549, p = 0.021, d = 0.618), but not during poor performance. Thus, it seems most of the theta activity that explains behavior is in the higher frequencies within that range. So perhaps it would be better to define a frequency band based on the data (Jensen & Tesche, 2002).
Supplementary figure 3: Theta power measured between 4 and 8 Hz predicts performance. Participants in this group all exhibited some degree of theta power. A) Power spectra of the baseline period and good and poor performance during the retention period. B) Relative theta power is significantly increased compared to baseline. And is significantly higher than during poor performance.
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