Welcome to the reference page!
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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Table of Contents
Issue 14
Wearable (Neuro)Technology: Promises and challenges
Ahn, J. W., Ku, Y., & Kim, H. C. (2019). A novel wearable EEG and ECG recording system for stress assessment. Sensors (Switzerland), 19(9). https://doi.org/10.3390/S19091991
Bagot, K. S., Matthews, S. A., Mason, M., Squeglia, L. M., Fowler, J., Gray, K., Herting, M., May, A., Colrain, I., Godino, J., Tapert, S., Brown, S., & Patrick, K. (2018). Current, future and potential use of mobile and wearable technologies and social media data in the ABCD study to increase understanding of contributors to child health. Developmental Cognitive Neuroscience, 32, 121–129. https://doi.org/10.1016/J.DCN.2018.03.008
Billeci, L., Tonacci, A., Tartarisco, G., Narzisi, A., Palma, S. Di, Corda, D., Baldus, G., Cruciani, F., Anzalone, S. M., Calderoni, S., Pioggia, G., & Muratori, F. (2016). An integrated approach for the monitoring of brain and autonomic response of children with Autism Spectrum Disorders during treatment by wearable technologies. Frontiers in Neuroscience, 10(JUN), 276. https://doi.org/10.3389/FNINS.2016.00276/BIBTEX
Cannard, C., Wahbeh, H., & Delorme, A. (2021). Electroencephalography Correlates of Well-Being Using a Low-Cost Wearable System. Frontiers in Human Neuroscience, 15, 736. https://doi.org/10.3389/FNHUM.2021.745135/BIBTEX
Cartocci, G., Rossi, D., Modica, E., Maglione, A. G., Martinez Levy, A. C., Cherubino, P., Canettieri, P., Combi, M., Rea, R., Gatti, L., & Babiloni, F. (2021). NeuroDante: Poetry Mentally Engages More Experts but Moves More Non-Experts, and for Both the Cerebral Approach Tendency Goes Hand in Hand with the Cerebral Effort. Brain Sciences, 11(3), 1–25. https://doi.org/10.3390/BRAINSCI11030281
Coates McCall, I., Lau, C., Minielly, N., & Illes, J. (2019). Owning Ethical Innovation: Claims about Commercial Wearable Brain Technologies. Neuron, 102(4), 728–731. https://doi.org/10.1016/J.NEURON.2019.03.026
Frijia, E. M., Billing, A., Lloyd-Fox, S., Vidal Rosas, E., Collins-Jones, L., Crespo-Llado, M. M., Amadó, M. P., Austin, T., Edwards, A., Dunne, L., Smith, G., Nixon-Hill, R., Powell, S., Everdell, N. L., & Cooper, R. J. (2021). Functional imaging of the developing brain with wearable high-density diffuse optical tomography: A new benchmark for infant neuroimaging outside the scanner environment. NeuroImage, 225, 117490. https://doi.org/10.1016/J.NEUROIMAGE.2020.117490
Goldstein-Piekarski, A. N., Holt-Gosselin, B., O’Hora, K., & Williams, L. M. (2019). Integrating sleep, neuroimaging, and computational approaches for precision psychiatry. Neuropsychopharmacology 2019 45:1, 45(1), 192–204. https://doi.org/10.1038/s41386-019-0483-8
Hielscher, A. H., Bluestone, A. Y., Abdoulaev, G. S., Klose, A. D., Lasker, J., Stewart, M., Netz, U., & Beuthan, J. (2002). Near-infrared diffuse optical tomography. Disease Markers, 18(5–6), 313–337. https://doi.org/10.1155/2002/164252
Jonasdottir, S. S., Minor, K., & Lehmann, S. (2021). Gender differences in nighttime sleep patterns and variability across the adult lifespan: a global-scale wearables study. Sleep, 44(2), 1–16. https://doi.org/10.1093/SLEEP/ZSAA169
Mota-Rolim, S. A., Pavlou, A., Nascimento, G. C., Fontenele-Araujo, J., & Ribeiro, S. (2019). Portable devices to induce lucid dreams-are they reliable? Frontiers in Neuroscience, 13(MAY), 428. https://doi.org/10.3389/FNINS.2019.00428/BIBTEX
Salchow-Hömmen, C., Skrobot, M., Jochner, M. C. E., Schauer, T., Kühn, A. A., & Wenger, N. (2022). Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders. Frontiers in Human Neuroscience, 16, 11. https://doi.org/10.3389/FNHUM.2022.768575/BIBTEX
Schofield, D. (2021, January 5). Lucid dreamers are using unproven tech to hack their sleep. Wired UK. https://www.wired.co.uk/article/lucid-dreaming-tech
The Wearable Market Will See 344.9 Million Shipments in 2022 With Sports, Fitness, and Wellness Trackers Leading the Way. (2022, January 27). ABI Research. https://www.abiresearch.com/press/the-wearable-market-will-see-3449-million-shipments-in-2022-with-sports-fitness-and-wellness-trackers-leading-the-way/
Wearable tech set to become a $54bn industry by 2023. (2019, August 7). GlobalData. https://www.globaldata.com/wearable-tech-set-to-become-a-54bn-industry-by-2023/
Wexler, A., & Reiner, P. B. (2019). Oversight of direct-to-consumer neurotechnologies: Efficacy of products is far from clear. Science (New York, N.Y.), 363(6424), 234. https://doi.org/10.1126/SCIENCE.AAV0223
Wexler, A., & Thibault, R. (2018). Mind-Reading or Misleading? Assessing Direct-to-Consumer Electroencephalography (EEG) Devices Marketed for Wellness and Their Ethical and Regulatory Implications. Journal of Cognitive Enhancement 2018 3:1, 3(1), 131–137. https://doi.org/10.1007/S41465-018-0091-2
Wheelock, M. D., Culver, J. P., & Eggebrecht, A. T. (2019). High-density diffuse optical tomography for imaging human brain function. Review of Scientific Instruments, 90(5), 051101. https://doi.org/10.1063/1.5086809
Experimental Amimals in Neuroscience
Ahn, J. W., Ku, Y., & Kim, H. C. (2019). A novel wearable EEG and ECG recording system for stress assessment. Sensors (Switzerland), 19(9). https://doi.org/10.3390/S19091991
Bagot, K. S., Matthews, S. A., Mason, M., Squeglia, L. M., Fowler, J., Gray, K., Herting, M., May, A., Colrain, I., Godino, J., Tapert, S., Brown, S., & Patrick, K. (2018). Current, future and potential use of mobile and wearable technologies and social media data in the ABCD study to increase understanding of contributors to child health. Developmental Cognitive Neuroscience, 32, 121–129. https://doi.org/10.1016/J.DCN.2018.03.008
Billeci, L., Tonacci, A., Tartarisco, G., Narzisi, A., Palma, S. Di, Corda, D., Baldus, G., Cruciani, F., Anzalone, S. M., Calderoni, S., Pioggia, G., & Muratori, F. (2016). An integrated approach for the monitoring of brain and autonomic response of children with Autism Spectrum Disorders during treatment by wearable technologies. Frontiers in Neuroscience, 10(JUN), 276. https://doi.org/10.3389/FNINS.2016.00276/BIBTEX
Cannard, C., Wahbeh, H., & Delorme, A. (2021). Electroencephalography Correlates of Well-Being Using a Low-Cost Wearable System. Frontiers in Human Neuroscience, 15, 736. https://doi.org/10.3389/FNHUM.2021.745135/BIBTEX
Cartocci, G., Rossi, D., Modica, E., Maglione, A. G., Martinez Levy, A. C., Cherubino, P., Canettieri, P., Combi, M., Rea, R., Gatti, L., & Babiloni, F. (2021). NeuroDante: Poetry Mentally Engages More Experts but Moves More Non-Experts, and for Both the Cerebral Approach Tendency Goes Hand in Hand with the Cerebral Effort. Brain Sciences, 11(3), 1–25. https://doi.org/10.3390/BRAINSCI11030281
Coates McCall, I., Lau, C., Minielly, N., & Illes, J. (2019). Owning Ethical Innovation: Claims about Commercial Wearable Brain Technologies. Neuron, 102(4), 728–731. https://doi.org/10.1016/J.NEURON.2019.03.026
Frijia, E. M., Billing, A., Lloyd-Fox, S., Vidal Rosas, E., Collins-Jones, L., Crespo-Llado, M. M., Amadó, M. P., Austin, T., Edwards, A., Dunne, L., Smith, G., Nixon-Hill, R., Powell, S., Everdell, N. L., & Cooper, R. J. (2021). Functional imaging of the developing brain with wearable high-density diffuse optical tomography: A new benchmark for infant neuroimaging outside the scanner environment. NeuroImage, 225, 117490. https://doi.org/10.1016/J.NEUROIMAGE.2020.117490
Goldstein-Piekarski, A. N., Holt-Gosselin, B., O’Hora, K., & Williams, L. M. (2019). Integrating sleep, neuroimaging, and computational approaches for precision psychiatry. Neuropsychopharmacology 2019 45:1, 45(1), 192–204. https://doi.org/10.1038/s41386-019-0483-8
Hielscher, A. H., Bluestone, A. Y., Abdoulaev, G. S., Klose, A. D., Lasker, J., Stewart, M., Netz, U., & Beuthan, J. (2002). Near-infrared diffuse optical tomography. Disease Markers, 18(5–6), 313–337. https://doi.org/10.1155/2002/164252
Jonasdottir, S. S., Minor, K., & Lehmann, S. (2021). Gender differences in nighttime sleep patterns and variability across the adult lifespan: a global-scale wearables study. Sleep, 44(2), 1–16. https://doi.org/10.1093/SLEEP/ZSAA169
Mota-Rolim, S. A., Pavlou, A., Nascimento, G. C., Fontenele-Araujo, J., & Ribeiro, S. (2019). Portable devices to induce lucid dreams-are they reliable? Frontiers in Neuroscience, 13(MAY), 428. https://doi.org/10.3389/FNINS.2019.00428/BIBTEX
Salchow-Hömmen, C., Skrobot, M., Jochner, M. C. E., Schauer, T., Kühn, A. A., & Wenger, N. (2022). Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders. Frontiers in Human Neuroscience, 16, 11. https://doi.org/10.3389/FNHUM.2022.768575/BIBTEX
Schofield, D. (2021, January 5). Lucid dreamers are using unproven tech to hack their sleep. Wired UK. https://www.wired.co.uk/article/lucid-dreaming-tech
The Wearable Market Will See 344.9 Million Shipments in 2022 With Sports, Fitness, and Wellness Trackers Leading the Way. (2022, January 27). ABI Research. https://www.abiresearch.com/press/the-wearable-market-will-see-3449-million-shipments-in-2022-with-sports-fitness-and-wellness-trackers-leading-the-way/
Wearable tech set to become a $54bn industry by 2023. (2019, August 7). GlobalData. https://www.globaldata.com/wearable-tech-set-to-become-a-54bn-industry-by-2023/
Wexler, A., & Reiner, P. B. (2019). Oversight of direct-to-consumer neurotechnologies: Efficacy of products is far from clear. Science (New York, N.Y.), 363(6424), 234. https://doi.org/10.1126/SCIENCE.AAV0223
Wexler, A., & Thibault, R. (2018). Mind-Reading or Misleading? Assessing Direct-to-Consumer Electroencephalography (EEG) Devices Marketed for Wellness and Their Ethical and Regulatory Implications. Journal of Cognitive Enhancement 2018 3:1, 3(1), 131–137. https://doi.org/10.1007/S41465-018-0091-2
Wheelock, M. D., Culver, J. P., & Eggebrecht, A. T. (2019). High-density diffuse optical tomography for imaging human brain function. Review of Scientific Instruments, 90(5), 051101. https://doi.org/10.1063/1.5086809
Aerts, L., Miccoli, B., Delahanty, A., Witters, H., Verstraelen, S., De Strooper, B., Braeken, D., & Verstreken, P. (2022). Do we still need animals? Surveying the role of animal-free models in Alzheimer’s and Parkinson’s disease research. The EMBO Journal, 41(6), e110002. https://doi.org/10.15252/embj.2021110002
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Categorising Mental Disorders: Phenomenology as an Alternative Approach
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The Ethics behind Brain-Computer Interfaces
Ahn, H., Sorkpor, S., Miao, H., Zhong, C., Jorge, R., Park, L., Abdi, S., & Cho, R. Y. (2019). Home-based self-administered transcranial direct current stimulation in older adults with knee osteoarthritis pain: An open-label study. Journal of clinical neuroscience: official journal of the Neurosurgical Society of Australasia, 66, 61–65. https://doi.org/10.1016/j.jocn.2019.05.023
Burwell, S., Sample, M. & Racine, E. (2017). Ethical aspects of brain computer interfaces: a scoping review. BMC Med Ethics 18, 60. https://doi.org/10.1186/s12910-017-0220-y
Drew L. (2019). The ethics of brain–computer interfaces. Nature 571, S19-S21. https://doi.org/10.1038/d41586-019-02214-2
Feng, Z., Sun, Y., Qian, L., Qi, Y., Wang, Y., Guan, C., & Sun, Y. (2021). Design a novel BCI for neurorehabilitation using concurrent LFP and EEG features: A case study. IEEE Transactions on Biomedical Engineering, 69(5), 1554-1563.
Forum on Neuroscience and Nervous System Disorders; Board on Health Sciences Policy; Institute of Medicine; The National Academies of Sciences, Engineering, and Medicine. Non-Invasive Neuromodulation of the Central Nervous System: Opportunities and Challenges: Workshop Summary (2015). Washington (DC): National Academies Press (US); Ethical, Legal, and Social Issues. Available from: https://www.ncbi.nlm.nih.gov/books/NBK332930/
Lu, Y., Lyu, H., Richardson, A. G., Lucas, T. H., & Kuzum, D. (2016). Flexible neural electrode array based-on porous graphene for cortical microstimulation and sensing. Scientific reports, 6(1), 1-9.
Mattei, T. A. (2014). How graphene is expected to impact neurotherapeutics in the near future. Expert Review of Neurotherapeutics, 14(8), 845-847.
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Moore, A.D. Privacy, Neuroscience, and Neuro-Surveillance. (2017). Res Publica 23, 159–177. https://doi.org/10.1007/s11158-016-9341-2
Naseer, N., & Hong, K. S. (2015). fNIRS-based brain-computer interfaces: a review. Frontiers in human neuroscience, 9, 3.
Rainey, S., Maslen H., & Savulescu, J. (2020). When Thinking is Doing: Responsibility for BCI-Mediated Action, AJOB Neuroscience, 11:1, 46-58. https://10.1080/21507740.2019.1704918
Shead, S. (2021). Elon Musk says his start-up Neuralink has wired up a monkey to play video games using its mind. Tech. Available from: https://www.cnbc.com/2021/02/01/elon-musk-neuralink-wires-up-monkey-to-play-video-games-using-mind.html
Smith, S. (2021). Elon Musk’s Neuralink is being outperformed by this Spanish graphene startup. Sifted. Available from: https://sifted.eu/articles/neuralink-competitor-inbrain/
Steyrl, D., Kobler, R. J., & Müller-Putz, G. R. (2016). On similarities and differences of invasive and non-invasive electrical brain signals in brain-computer interfacing. Journal of biomedical science and engineering, 9(08), 393.
Tangermann, M., Krauledat, M., Grzeska, K., Sagebaum, M., Blankertz, B., Vidaurre, C., & Müller, K. R. (2008, December). Playing pinball with non-invasive BCI. In NIPS (pp. 1641-1648).
Zuk, P., Torgerson, L., Sierra-Mercado, D. & Lázaro-Muñoz, G. (2018). Neuroethics of Neuromodulation: An Update. Curr Opin Biomed Eng. https://doi.org/10.1016/j.cobme.2018.10.003
Neuroscience - a science of the cis-gendered male
Ahn, H., Sorkpor, S., Miao, H., Zhong, C., Jorge, R., Park, L., Abdi, S., & Cho, R. Y. (2019). Home-based self-administered transcranial direct current stimulation in older adults with knee osteoarthritis pain: An open-label study. Journal of clinical neuroscience: official journal of the Neurosurgical Society of Australasia, 66, 61–65. https://doi.org/10.1016/j.jocn.2019.05.023
Burwell, S., Sample, M. & Racine, E. (2017). Ethical aspects of brain computer interfaces: a scoping review. BMC Med Ethics 18, 60. https://doi.org/10.1186/s12910-017-0220-y
Drew L. (2019). The ethics of brain–computer interfaces. Nature 571, S19-S21. https://doi.org/10.1038/d41586-019-02214-2
Feng, Z., Sun, Y., Qian, L., Qi, Y., Wang, Y., Guan, C., & Sun, Y. (2021). Design a novel BCI for neurorehabilitation using concurrent LFP and EEG features: A case study. IEEE Transactions on Biomedical Engineering, 69(5), 1554-1563.
Forum on Neuroscience and Nervous System Disorders; Board on Health Sciences Policy; Institute of Medicine; The National Academies of Sciences, Engineering, and Medicine. Non-Invasive Neuromodulation of the Central Nervous System: Opportunities and Challenges: Workshop Summary (2015). Washington (DC): National Academies Press (US); Ethical, Legal, and Social Issues. Available from: https://www.ncbi.nlm.nih.gov/books/NBK332930/
Lu, Y., Lyu, H., Richardson, A. G., Lucas, T. H., & Kuzum, D. (2016). Flexible neural electrode array based-on porous graphene for cortical microstimulation and sensing. Scientific reports, 6(1), 1-9.
Mattei, T. A. (2014). How graphene is expected to impact neurotherapeutics in the near future. Expert Review of Neurotherapeutics, 14(8), 845-847.
Maynard, E. M., Nordhausen, C. T., & Normann, R. A. (1997). The Utah intracortical electrode array: a recording structure for potential brain-computer interfaces. Electroencephalography and clinical neurophysiology, 102(3), 228-239.
Moore, A.D. Privacy, Neuroscience, and Neuro-Surveillance. (2017). Res Publica 23, 159–177. https://doi.org/10.1007/s11158-016-9341-2
Naseer, N., & Hong, K. S. (2015). fNIRS-based brain-computer interfaces: a review. Frontiers in human neuroscience, 9, 3.
Rainey, S., Maslen H., & Savulescu, J. (2020). When Thinking is Doing: Responsibility for BCI-Mediated Action, AJOB Neuroscience, 11:1, 46-58. https://10.1080/21507740.2019.1704918
Shead, S. (2021). Elon Musk says his start-up Neuralink has wired up a monkey to play video games using its mind. Tech. Available from: https://www.cnbc.com/2021/02/01/elon-musk-neuralink-wires-up-monkey-to-play-video-games-using-mind.html
Smith, S. (2021). Elon Musk’s Neuralink is being outperformed by this Spanish graphene startup. Sifted. Available from: https://sifted.eu/articles/neuralink-competitor-inbrain/
Steyrl, D., Kobler, R. J., & Müller-Putz, G. R. (2016). On similarities and differences of invasive and non-invasive electrical brain signals in brain-computer interfacing. Journal of biomedical science and engineering, 9(08), 393.
Tangermann, M., Krauledat, M., Grzeska, K., Sagebaum, M., Blankertz, B., Vidaurre, C., & Müller, K. R. (2008, December). Playing pinball with non-invasive BCI. In NIPS (pp. 1641-1648).
Zuk, P., Torgerson, L., Sierra-Mercado, D. & Lázaro-Muñoz, G. (2018). Neuroethics of Neuromodulation: An Update. Curr Opin Biomed Eng. https://doi.org/10.1016/j.cobme.2018.10.003
Issue 13
Cognitive Flexibility in Times of COVID-19
Afshari, A., Hashemikamangar, S., & Hashemikamangar, S. S. (2021). The correlation of perceived stress and professional concerns during COVID-19 pandemic among Iranian dentists: the mediating role of cognitive flexibility. Dentistry 3000, 9(1). https://doi.org/10.5195/D3000.2021.119
Aytur, S. A., Ray, K. L., Meier, S. K., Campbell, J., Gendron, B., Waller, N., & Robin, D. A. (2021). Neural mechanisms of acceptance and commitment therapy for chronic pain: A network based fMRI approach. Frontiers in human neuroscience, 15, 28
Bogliacino, F., Codagnone, C., Montealegre, F., Folkvord, F., Gómez, C., Charris, R., Liva, G., Lupiáñez-Villanueva, F., & Veltri, G. A. (2021). Negative shocks predict change in cognitive function and preferences: assessing the negative affect and stress hypothesis. Scientific Reports 2021 11:1, 11(1), 1–10. https://doi.org/10.1038/s41598-021-83089-0
Bond, F. W., Hayes, S. C., Baer, R. A., Carpenter, K. M., Guenole, N., Orcutt, H. K., Waltz, T., & Zettle, R. D. (2011). Preliminary psychometric properties of the Acceptance and Action Questionnaire-II: a revised measure of psychological inflexibility and experiential avoidance. Behavior therapy, 42(4), 676–688. https://doi.org/10.1016/j.beth.2011.03.007
Borders, A. (2020). Rumination and Related Constructs: Causes, Consequences, and Treatment of Thinking Too Much. (pp. 279-311) Academic Press
Brashear, C. A., & Thomas, N. (2020). Core competencies for combatting crisis: fusing ethics, cultural competence, and cognitive flexibility in counseling. Counselling Psychology Quarterly, 1–15. https://doi.org/10.1080/09515070.2020.1768362
Cambaz, H. Z., & Ünal, G. (2021). Does Student’s Cognitive Flexibility Decrease During Pandemic? A New Approach to Measure Cognitive Flexibility. International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 9(1), 13–22. https://doi.org/10.23947/2334-8496-2021-9-1-13-22
Chahal, R., Kirshenbaum, J. S., Miller, J. G., Ho, T. C., & Gotlib, I. H. (2021). Higher executive control network coherence buffers against puberty-related increases in internalizing symptoms during the COVID-19 pandemic. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6(1), 79-88. https://doi.org/10.1016/j.bpsc.2020.08.010
Dajani, D. R., & Uddin, L. Q. (2015). Demystifying cognitive flexibility: Implications for clinical and developmental neuroscience. Trends in neurosciences, 38(9), 571-578. https://doi.org/10.1016/j.tins.2015.07.003
Daks, J. S., Peltz, J. S., & Rogge, R. D. (2020). Psychological flexibility and inflexibility as sources of resiliency and risk during a pandemic: Modeling the cascade of COVID-19 stress on family systems with a contextual behavioral science lens. Journal of Contextual Behavioral Science, 18, 16–27. https://doi.org/10.1016/J.JCBS.2020.08.003
Dawson, D. L., & Golijani-Moghaddam, N. (2020). COVID-19: Psychological flexibility, coping, mental health, and wellbeing in the UK during the pandemic. Journal of contextual behavioral science, 17, 126–134. https://doi.org/10.1016/j.jcbs.2020.07.010
Derrfuss, J., Brass, M., Neumann, J., & von Cramon, D. Y. (2005). Involvement of the inferior frontal junction in cognitive control: Meta‐analyses of switching and Stroop studies. Human brain mapping, 25(1), 22-34
Diamond, A. (2013). Executive functions. Annual review of psychology, 64, 135-168. https://doi.org/10.1146/annurev-psych-113011-143750
Dodangeh, Z., Malek Hosseini, E., & Dehkordi, P. S. (2021). The effect of parents attitudes to play in green space on childrens cognitive flexibility during Covid-19 home quarantine. Shenakht Journal of Psychology & Psychiatry , 8(2), 100–112. http://shenakht.muk.ac.ir/article-1-1169-en.html
Francis, A. W., Dawson, D. L., & Golijani-Moghaddam, N. (2016). The development and validation of the Comprehensive assessment of Acceptance and Commitment Therapy processes (CompACT). Journal of Contextual Behavioral Science, 5 (3), 134-145. https://doi.org/10.1016/j.jcbs.2016.05.003
Gabrys, R. L., Tabri, N., Anisman, H., & Matheson, K. (2018). Cognitive control and flexibility in the context of stress and depressive symptoms: The cognitive control and flexibility questionnaire. Frontiers in Psychology, 9, 2219. https://doi.org/10.3389/fpsyg.2018.02219
Gozzi, N., Tizzani, M., Starnini, M., Ciulla, F., Paolotti, D., Panisson, A., & Perra, N. (2020). Collective Response to Media Coverage of the COVID-19 Pandemic on Reddit and Wikipedia: Mixed-Methods Analysis. Journal of Medical Internet Research, 22(10). https://doi.org/10.2196/21597
Hayes, S. C., Luoma, J. B., Bond, F. B., Masuda, A. & Lillis, J. (2006). Acceptance and Commitment Therapy: Model, processes and outcomes. Behaviour Research and Therapy, 44(1), 1-25. https://doi.org/10.1016/j.brat.2005.06.006
Hayes, S. C., Villatte, M., Levin, M. & Hildebrandt, M. (2011). Open, aware, and active: Contextual approaches as an emerging trend in the behavioral and cognitive therapies. Annual Review of Clinical Psychology, 7(4), 141-168. https://doi.org/10.1146/annurev-clinpsy-032210-104449
Kroska, E. B., Roche, A. I., Adamowicz, J. L. & Stegall, M. S. (2020). Psychological flexibility in the context of COVID-19 adversity: Associations with distress. Journal of Contextual Behavioral Science, 18, 28-33. https://doi.org/10.1016/j.jcbs.2020.07.011
Luijten, M. A., van Muilekom, M. M., Teela, L., Polderman, T. J., Terwee, C. B., Zijlmans, J., … & Haverman, L. (2021). The impact of lockdown during the COVID-19 pandemic on mental and social health of children and adolescents. Quality of Life Research, 1-10
Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: a network model of insula function. Brain structure and function, 214(5-6), 655-667. https://doi.org/10.1007%2Fs00429-010-0262-0
Ong, C. W., Pierce, B. G., Petersen, J. M., Barney, J. L., Fruge, J. E., Levin, M. E. & Twohig, M. P. (2020). A psychometric comparison of psychological inflexibility measures: Discriminant validity and item performance. Journal of Contextual Behavioral Science, 34 – 47. https://doi.org/10.1016/j.jcbs.2020.08.007
Seiter, J. S., & Curran, T. (2021). Social-distancing fatigue during the COVID-19 pandemic: a mediation analysis of cognitive flexibility, fatigue, depression, and adherence to CDC guidelines. Communication Research Reports, 38(1), 68–78. https://doi.org/10.1080/08824096.2021.1880385
Sibel Demirtas, A. (2021). Predictive roles of state hope and cognitive control/flexibility in state anxiety during COVID-19 outbreak in Turkey. Turkey. Dusunen Adam The Journal of Psychiatry and Neurological Sciences, 34, 89–96. https://doi.org/10.14744/DAJPNS.2020.00124
Uddin, L. Q. (2021). Cognitive and behavioural flexibility: neural mechanisms and clinical considerations. Nature Reviews Neuroscience, 22(3), 167-179. https://doi.org/10.1038/s41583-021-00428-w
van der Velden, P.G., Hyland, P., Contino, C., von Gaudecker, H.M., Muffels, R. and Das, M. (2021). Anxiety and depression symptoms, the recovery from symptoms, and loneliness before and after the COVID-19 outbreak among the general population: Findings from a Dutch population-based longitudinal study. PloS one, 16(1), p.e0245057
Wąsowicz, G., Mizak, S., Krawiec, J. & Białaszek, W., (2021). Mental Health, Well-Being, and Psychological Flexibility in the Stressful Times of the COVID-19 Pandemic. Frontiers in Psychology, 12:647975. https://doi.org/10.3389/fpsyg.2021.647975
Wu, X., Wang, Z., Zhang, H., Yuan, P., Yu, Q., Zhou, Z., & Zhao, Q. (2021). Effects of Internet Language Related to COVID-19 on Mental Health in College Students: The Mediating Effect of Cognitive Flexibility. Frontiers in Psychology, 12. https://doi.org/10.3389/FPSYG.2021.600268
Jafari, A. (2020). Comparing Cognitive Flexibility, Psychological Capital and Coping Strategies with Pain between Individuals with COVID-19 Responding and Non-Responding to Home Treatment. Journal of Counseling Research, 19(74), 4–35. https://doi.org/10.29252/JCR.19.74.4
Effects of methylphenidate on default mode network resting-state fMRI connectivity and their relationship with attention in medication-naïve children and adults with ADHD
Supplementary Figures
Supplementary figure 1. Illustration of the graph theory measures used. (A) Modularity – Reflects the extent to which a network is divided into modules. Modules are clusters of nodes with denser links among themselves than among the rest of the network, displayed in grey. (B) Module degree – Measure of the number of connections a module (grey area) has with the rest of the network. Here, the module circled in red has a higher number of connections to the rest of the network than the green module, and therefore has a higher module degree. (C) Participation coefficient – Measures the diversity of connections between modules. Here, the red node is connected to all three modules, whereas the green node only has connections to nodes within its own module. As such, the red node has a higher participation coefficient. (D) Eigenvector centrality – Reflects the extent to which a node is connected to other highly connected nodes. Here, the green nodes both have a high number of connections. Therefore, the red node, which is connected to both of these highly connected nodes, has high eigenvector centrality.
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Letter-colour consistency but not conscious awareness of synesthetic colours is necessary when defining grapheme-colour synesthesia
Supplementary Material
Appendix A (Dutch questionnaire) Appendix A.1 (Experience with picking colours for letters)
1. Tijdens het uitvoeren van het experiment wist ik zeker welke kleur een letter zou moeten hebben.
2. De kleuren die ik met letters associeer zijn generieke kleuren (bijv. “rood, maar om het even welk type rood”) en niet een specifieke kleurtint (bijv. “deze exacte kleur rood”).
3. Als ik mij een letter probeer voor te stellen in een andere kleur dan de ‘juiste’ kleur, kan ik niet anders dan tegelijkertijd ook aan die ‘juiste’ kleur denken.
4. De kleur associaties die ik heb bij letters voelen meer als ‘weten’ dan als ‘zien’.
5. Ik ervaar kleuren met letters, zelfs als ik er niet aan denk (bijvoorbeeld tijdens het lezen van een boek).
6. De kleuren die ik met letters associeer gebruik ik niet doelbewust (bijvoorbeeld om een boodschappenlijstje te onthouden).
7. Tijdens het experiment had ik het gevoel dat ik gokte welke kleur een letter zou moeten hebben.
8. De kleuren die ik aan letters associeer zijn specifieke kleurschakeringen (bijv. “deze exacte kleur bruin”), niet slechts een generieke kleurcategorie (bijv. “bruin, om het even welk type bruin”).
9. Ik kan gemakkelijk een letter in elke mogelijke kleur voorstellen, zonder interferentie te ervaren van de ‘juiste’ kleur.
10. Mijn ervaring van kleuren bij de letters voelt als ‘zien’ (in plaats van enkel ervaren als ‘weten’).
11. Ik ervaar de geassocieerde kleuren bij letters alleen als ik doelbewust nadenk over de kleur die ze hebben.
12. Ik gebruik de kleuren die ik met letters associeer doelbewust (bijvoorbeeld om iemands naam te onthouden).
Appendix A.2 (Grapheme-colour synesthesia; Eagleman et al., 2007)
1. Bepaalde letters hebben voor mij altijd een bepaalde kleur (bijvoorbeeld, de letter “J” is oranje)
Appendix A.3 (PA questionnaire; Rouw & Scholte, 2007)
1. Wanneer ik naar een bepaalde letter kijk, dan zie ik een specifieke kleur.
2. Wanneer ik naar een bepaalde letter kijk, verschijnt de bijbehorende kleur alleen in mijn gedachten en niet ergens buiten mijn hoofd (zoals op het papier).
3. Wanneer ik naar een bepaalde letter kijk, komt daarvan de bijbehorende synesthetische kleur in mijn gedachten maar op het papier verschijnt enkel de kleur waarin de letter gedrukt is (bijv. een zwarte letter tegen een witte achtergrond).
4. Het is alsof de kleur zich daadwerkelijk op het papier bevindt waarop de letter gedrukt staat.
5. De figuur zelf heeft geen kleur maar ik ben ervan bewust dat deze geassocieerd is met een specifieke kleur.
6. De kleur is als het ware geprojecteerd op de letter.
7. Ik zie letters niet letterlijk in een kleur maar heb een sterk gevoel dat ik weet welke kleur bij een bepaalde letter hoort.
8. De kleur bevindt zich niet op het papier maar zweeft in de ruimte.
9. De kleur heeft dezelfde vorm als de letter.
10. Ik zie de kleur van een letter alleen in mijn hoofd.
11. Ik zie de synesthetische kleur heel duidelijk in nabijheid van de stimulus (bijv. erop of erachter of er overheen).
12. Wanneer ik naar een bepaalde letter kijk, verschijnt de bijbehorende kleur ergens buiten mijn hoofd (zoals op het papier).
Appendix A.4 (Difference synesthetic colours with “real” colours)
1. De synesthetische kleurervaring lijkt sterk op een echte waarneming.
2. Een letter kan een bepaalde synesthetische kleur hebben, maar ik denk nooit per ongeluk dat die letter ook echt die kleur heeft.
3. Het waarnemen van een synesthetische kleur is duidelijk anders dan het waarnemen van een echte kleur (in de buitenwereld).
4. Door de synesthetische ervaring kan ik me soms vergissen, ik denk dan even dat het in de werkelijke buitenwereld aanwezig is.
Appendix A.5 (Brighter, sharper, more powerful)
1. Wat is meer helder, een synesthetische kleur of een echte kleur?
2. Wat is scherper, een synesthetische kleur of een echte kleur?
3. Wat is een krachtiger ervaring, een synesthetische kleurervaring of een echte kleur zien?
Appendix A.6 (CLAN; Rothen, Tsakanikos, Meier, & Ward, 2013)
1. Ik ervaar zelfs synesthetische kleuren wanneer ik niet specifiek aandacht aan hen besteed (bijvoorbeeld als ik een boek lees).
2. Ik zie de synesthetische kleuren op het computerscherm (of heel dichtbij het scherm).
3. Het voelt alsof ik de kleuren actief moet opbrengen, in plaats van dat de kleuren vanzelf komen.
4. Ik ervaar de synesthetische kleuren op verschillende locaties tegelijkertijd (bijvoorbeeld zowel op het scherm als letterlijk in mijn hoofd, of een andere combinatie).
5. Ik ervaar alleen de synesthetische kleuren van letters als ik denk aan hoe ze een kleur hebben.
6. Wanneer ik snel naar de pagina van een boek kijk verschijnen de synesthetische kleuren voordat ik doorheb wat de letters/woorden zijn.
7. Mijn synesthetische kleuren waren sterker in het verleden (d.w.z. jaren geleden).
8. Ik probeer om mijn synesthetische kleuren doelbewust (opzettelijk) te gebruiken in mijn dagelijks leven.
9. De synesthetische kleuren verschijnen automatisch zonder dat ik daar moeite voor hoef te doen.
10. Ik kan wijzen naar de locatie van de synesthetische kleuren.
11. Mijn synesthetische kleuren zijn niet in intensiteit veranderd over de jaren heen.
12. Ik gebruik mijn synesthetische kleuren doelbewust voor het onthouden van reeksen van getallen (bijvoorbeeld pincodes of telefoonnummers).
13. Ik “zie” geen kleuren wanneer ik naar letters kijk.
14. Ik gebruik mijn synesthetische kleuren om datums te onthouden en afspraken te plannen (bijvoorbeeld 28.05.2020).
15. Mijn synesthetische kleuren waren zwakker in het verleden (d.w.z. jaren geleden).
16. De kleur lijkt op het scherm te zijn, waar de letter geprint is.
Appendix A.7 (Different forms of synesthesia)
1. Nummers hebben voor mij een kleur (bijv. ‘de 3 is geel’)
2. Letters hebben voor mij een kleur (bijv. de C is blauw)
3. Letters hebben een geslacht (bijv. ‘B is vrouwelijk’)
4. Letters hebben een persoonlijkheid (bijv. ‘G is vriendelijk en sociaal’)
5. Geluiden roepen een kleur op (bijv. ‘Vioolmuziek is oranje’, of ‘jouw stem is geel’)
6. Dagen, maanden of jaartallen hebben een locatie in de ruimte (bijv. ‘januari staat diagonaal rechts van februari’)
7. Nummers hebben een locatie in de ruimte (bijv. ‘de 3 staat vlak achter de 4’)
8. Dagen van de week, maanden of jaartallen hebben een kleur (bijv. ‘februari is donkerpaars’)
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Issue 12
Is That Child Friends... with a Robot? Taking a Look from the Uncanny Valley
— Original Piece
Barr, R. (2013). Memory Constraints on Infant Learning From Picture Books, Television, and Touchscreens. Child Development Perspectives, 7(4), 205–210. https://doi.org/10.1111/cdep.12041
Bedford, R., Saez de Urabain, I. R., Cheung, C. H. M., Karmiloff-Smith, A., & Smith, T. J. (2016). Toddlers’ Fine Motor Milestone Achievement Is Associated with Early Touchscreen Scrolling. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.01108
Brink, K. A., Gray, K., & Wellman, H. M. (2019). Creepiness Creeps In: Uncanny Valley Feelings Are Acquired in Childhood. Child Development, 90(4), 1202–1214. https://doi.org/10.1111/cdev.12999
COUNCIL ON COMMUNICATIONS AND MEDIA. (2016). Media and Young Minds. Pediatrics, 138(5), e20162591. https://doi.org/10.1542/peds.2016-2591
Cristia, A., & Seidl, A. (2015). Parental Reports on Touch Screen Use in Early Childhood. PLOS ONE, 10(6), e0128338. https://doi.org/10.1371/journal.pone.0128338
MacDorman, K. F., & Ishiguro, H. (2006). The uncanny advantage of using androids in cognitive and social science research. Interaction Studies. Social Behaviour and Communication in Biological and Artificial Systems, 7(3), 297–337. https://doi.org/10.1075/is.7.3.03mac
Mejías, C. S., Echevarría, C., Nuñez, P., Manso, L., Bustos, P., Leal, S., & Parra, C. (2013). Ursus: A robotic assistant for training of children with motor impairments. In Converging Clinical and Engineering Research on Neurorehabilitation (pp. 249-253). Springer, Berlin, Heidelberg.
Milligan, K., Astington, J. W., & Dack, L. A. (2007). Language and Theory of Mind: Meta-Analysis of the Relation Between Language Ability and False-belief Understanding. Child Development, 78(2), 622–646. https://doi.org/10.1111/j.1467-8624.2007.01018.x
Movellan, J., Eckhardt, M., Virnes, M., & Rodriguez, A. (2009). Sociable robot improves toddler vocabulary skills. Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction – HRI ’09, 307. https://doi.org/10.1145/1514095.1514189
Obaid, M., Baykal, G. E., Yantaç, A. E., & Barendregt, W. (2018). Developing a Prototyping Method for Involving Children in the Design of Classroom Robots. International Journal of Social Robotics, 10(2), 279–291. https://doi.org/10.1007/s12369-017-0450-7
Ricks, D. J., & Colton, M. B. (2010). Trends and considerations in robot-assisted autism therapy. 2010 IEEE International Conference on Robotics and Automation, 4354–4359. https://doi.org/10.1109/ROBOT.2010.5509327
Rideout, V. (2013). Zero to Eight: Children’s Media Use in America 2013; Common Sense Media. Available at: https://www.commonsensemedia.org/research/zero-to-eight- childrens-media-use-in-america-2013 (accessed May 18, 2021).
Serholt, S., Barendregt, W., Vasalou, A., Alves-Oliveira, P., Jones, A., Petisca, S., & Paiva, A. (2017). The case of classroom robots: Teachers’ deliberations on the ethical tensions. AI & SOCIETY, 32(4), 613–631. https://doi.org/10.1007/s00146-016-0667-2
Steckenfinger, S. A., & Ghazanfar, A. A. (2009). Monkey visual behavior falls into the uncanny valley. Proceedings of the National Academy of Sciences, 106(43), 18362–18366. https://doi.org/10.1073/pnas.0910063106
Stein, J.-P., & Ohler, P. (2017). Venturing into the uncanny valley of mind—The influence of mind attribution on the acceptance of human-like characters in a virtual reality setting. Cognition, 160, 43–50. https://doi.org/10.1016/j.cognition.2016.12.010
Strasburger, V. C., Jordan, A. B., & Donnerstein, E. (2012). Children, Adolescents, and the Media: Pediatric Clinics of North America, 59(3), 533–587. https://doi.org/10.1016/j.pcl.2012.03.025
Wellman, H. M. (2014). Making minds: How theory of mind develops. Oxford University Press.
‘My depression, your depression - same name, different story’: The use of digital storytelling in mental health science.
— Original Piece
Coleman, L., Ramm, J., & Cooke, R. (2010). The effectiveness of an innovative intervention aimed at reducing binge drinking among young people: Results from a pilot study. Drugs: Education, Prevention and Policy, 17(4), 413–430. https://doi.org/10.3109/09687630802572599
Cunsolo Willox, A., Harper, S. L., Edge, V. L., Storytelling, ‘My Word’, Lab, D. M., & Government, R. I. C. (2013). Storytelling in a digital age: Digital storytelling as an emerging narrative method for preserving and promoting indigenous oral wisdom. Qualitative Research, 13(2), 127–147. https://doi.org/10.1177/1468794112446105
De Vecchi, N., Kenny, A., Dickson‐Swift, V., & Kidd, S. (2016). How digital storytelling is used in mental health: A scoping review. International Journal of Mental Health Nursing, 25(3), 183–193. https://doi.org/10.1111/inm.12206
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Lambert, J., & Hessler, B. (2018). Digital Storytelling: Capturing Lives, Creating Community. Routledge.
Lenette, C., & Boddy, J. (2013). Visual ethnography and refugee women: Nuanced understandings of lived experiences. Qualitative Research Journal. https://doi.org/10.1108/14439881311314621
Matthews, N., & Sunderland, N. (2013). Digital life-story narratives as data for policy makers and practitioners: Thinking through methodologies for large-scale multimedia qualitative datasets. Journal of Broadcasting & Electronic Media, 57(1), 97–114. https://doi.org/10.1080/08838151.2012.761703
Rice, C., Chandler, E., Harrison, E., Liddiard, K., & Ferrari, M. (2015). Project Re• Vision: Disability at the edges of representation. Disability & Society, 30(4), 513–527. https://doi.org/10.1080/09687599.2015.1037950
Richards, C. (2010). Opinion: Them and us in mental health services. The Psychologist, 23, 40–41.
Sawyer, C. B., & Willis, J. M. (2011). Introducing digital storytelling to influence the behavior of children and adolescents. Journal of Creativity in Mental Health, 6(4), 274–283. https://doi.org/10.1080/15401383.2011.630308
Sljivic, H., Sutherland, I., Stannard, C., Ioppolo, C., & Morrisby, C. (2021). Changing attitudes towards older adults: Eliciting empathy through digital storytelling. Gerontology & Geriatrics Education, 1–14. https://doi.org/10.1080/02701960.2021.1900838
Spector, R., Smojkis, M., & Chilton, E. H. (2011). Service user involvement in a ward staff training project: Participants’ experiences of making digital stories. Clinical Psychology Forum, 220, 49–53.
Stenhouse, R., Tait, J., Hardy, P., & Sumner, T. (2013). Dangling conversations: Reflections on the process of creating digital stories during a workshop with people with early-stage dementia. Journal of Psychiatric and Mental Health Nursing, 20(2), 134–141. https://doi.org/10.1111/j.1365-2850.2012.01900.x
Wexler, L., Gubrium, A., Griffin, M., & DiFulvio, G. (2013). Promoting positive youth development and highlighting reasons for living in Northwest Alaska through digital storytelling. Health Promotion Practice, 14(4), 617–623. https://doi.org/10.1177/1524839912462390
Willis, N., Frewin, L., Miller, A., Dziwa, C., Mavhu, W., & Cowan, F. (2014). “My story”—HIV positive adolescents tell their story through film. Children and Youth Services Review, 45, 129–136. https://doi.org/10.1016/j.childyouth.2014.03.029
Replication, stability and extension of the psychopathy symptomsymptom network: The core characteristics depend on whom you ask
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The Female Experience of Autism
— Original Piece
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The impact of motivated reasoning on (un)sustainable food
decisions and sketching new pathways for effective
— Literature Thesis
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Appendix A
Details of Graca et al. (2014), discussed in 2.2.2 Moral disengagement and loss aversion in sustainable food decision making.
During these group interviews, participants discussed the impacts of meat production and consumption, and the possibility of changing behaviour (Graca et al., 2014). Furthermore, using moral disengagement was expected to minimise the willingness to consider a change of habits (Graca et al., 2014).
Appendix B
Details of Verplanken and Roy (2016), discussed in 3.1.1 Behaviour change interventions.
After controlling for past behaviour, habit strength, personal norms, and intentions, the intervention promoting sustainable behaviours was more effective among the group of participants that recently relocated (Verplanken & Roy, 2016). Probably, due to old habits that were disturbed, participants became more sensitive to new information when recently relocated (Verplanken & Roy, 2016).
Appendix C
Details of Hanss and Bohm (2013), discussed in 3.1.2 Informational interventions.
The intervention consisted of four steps: the first step increased awareness of environmental and socio-economic problems; the second taught participants about human actions as the leading causes for problems; the third was aiming to strengthen self-efficacy concerning contributing to sustainable development directly; and the final part focused on strengthening self-efficacy with regard to indirectly contributing to sustainable development (Hanss & Bohm, 2013).
Appendix D
Details of Demarque et al. (2015), discussed in 3.2.1.1 Use of social norms.
An example of a weak descriptive norm used is: “For your information, 9% of previous participants purchased one ecological product’. A strong descriptive norm was either: “For your information, 70% of previous participants purchased at least one ecological product” or “For your information, on average, previous participants purchased at least two ecological products.” (Demarque et al., 2015).
Appendix E
Details of Campbell-Arvai et al. (2014), discussed in 3.2.1.2 Increase ease and convenience.
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You’re (Not) What They Think You Are The Sense and Nonsense of Personality Tests
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Issue 11
At the Intersection of Art and Neuroscience
— Original Piece
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
Dikker, Suzanne; Michalareas, Georgios; Oostrik, Matthias; Serafimaki, Amalia; Kahraman, Hasibe Melda; Struiksma, Marijn E.; Poeppel, David (2020). Crowdsourcing neuroscience: inter-brain coupling during face-to-face interactions outside the laboratory. NeuroImage, (), 117436–.doi:10.1016/j.neuroimage.2020.117436
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Neuroaesthetics: Grounded in Science or Merely Aesthetically Pleasing?
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— Literature Thesis
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Painting Hypotheses, Cooking Methods, and Composing Results: A Review on “Proust was a Neuroscientist”
— Original Piece
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Time Distortions: A Review
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Better Research Practices: Own Your Research Decisions
— Original Piece
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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Dissociating contributions of periodic and aperiodic neural activity in human visual working memory.
— Research Project
1 - Supplementary Material
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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Issue 10
Structural Magnetic Resonance Imaging (MRI) and neuropsychological correlates in an elderly non-Western immigrant population
— Research Project
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Effective connectivity from the Nucleus accumbens towards the frontal cortex during attentional focus
— Research Project
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What happens to a troll in daylight? Interpersonal affect regulation and empathic
characteristics in internet trolling
— Research Project
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What are the neuropsychological consequences of complying to the delivery of an order?
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