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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 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

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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.

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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

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‘My depression, your depression - same name, different story’: The use of digital storytelling in mental health science.

— Original Piece

Baker, F. A., Metcalf, O., Varker, T., & O’Donnell, M. (2018). A systematic review of the efficacy of creative arts therapies in the treatment of adults with PTSD. Psychological Trauma: Theory, Research, Practice, and Policy, 10(6), 643. https://doi.org/10.1037/tra0000353

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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.

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Shafir, T., Orkibi, H., Baker, F. A., Gussak, D., & Kaimal, G. (2020). Editorial: The State of the Art in Creative Arts Therapies. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.00068

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

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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

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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 Use of social norms.

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Appendix E

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Disparities of People of Color in Psychedelic Medicine

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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






Use of prosody to mark information structure in autistic female and male adults with high-level language ability. 

— Research Article

Attwood, T. (2007). The complete guide to Asperger’s syndrome. London, UK: Jessica Kingsley Publishers.

Baron-Cohen, S. (2002) The extreme male brain theory of autism. Trends Cogn Sci 6: 248–254.

Baron-Cohen, S., & Wheelwright, S. (2004). The empathy quotient: An investigation of adults with Asperger syndrome or high functioning autism, and normal sex differences. J Autism Dev Discord, 34. 163-175.

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Chen, A. (2011). Tuning information packaging: Intonational realization of topic and focus in child Dutch. Journal of Child Language, 38, 1055–1083.

DePape, A. M., Chen, A., Hall, G. B., & Trainor, L. J. (2012). Use of prosody and information structure in high functioning adults with autism in relation to language ability. Frontiers in psychology, 3, 72.

Diehl, J. J., Bennetto, L., Watson, D., Gunlogson, C., & McDonough, J. (2008). Resolving ambiguity: A psycholinguistic approach to understanding prosody processing in high-functioning autism. Brain and Language, 106, 144–152.

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Elie, B & Chardon, G. (2018). Glottal/Supraglottal Source Separation in Fricatives Based on Non- Stationary Signal Subspace Estimation.

Gotham, K., Risi, S., Pickles, A., & Lord, C. (2006). The Autism Diagnostic Observation Schedule (ADOS). Journal of Autism and Developmental Disorders.

Green, H., & Tobin, Y. (2009). Prosodic analysis is difficult. . .but worth it: a study in high functioning autism. Int. J. Speech Lang. Pathol. 11, 308–315.

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Paul, R., Augustyn, A., Klin, A. & Volkmar, F. R. (2005). Perception and production of prosody by speakers with autism spectrum disorders. Journal of Autism and Developmental Disorders. 205–220.

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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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Wichmann, A., Dehé, N. & Barth-Weingarten, D., (2009). Where prosody meets pragmatics: research at the interface. Where Prosody meets Pragmatics. Bingley: Emerald, pp. 1-20.

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Wynn, C. J., Borrie, S. A., & Sellers, T. P. (2018). Speech Rate Entrainment in Children and Adults With and Without Autism Spectrum Disorder. American Journal of Speech-Language Pathology, 27(3), 965.

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. 

Neuroaesthetics: Grounded in Science or Merely Aesthetically Pleasing? 

— Original Piece

Armony, J., & Dolan, R. J. (2002). Modulation of spatial attention by fear-conditioned stimuli: an event-related fMRI study. Neuropsychologia, 40(7), 817–826. https://doi.org/10.1016/s0028-3932(01)00178-6

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

 Gallace, A., & Spence, C. (2011). Tactile aesthetics: towards a definition of its characteristics and neural correlates. Social Semiotics, 21(4), 569–589. https://doi.org/10.1080/10350330.2011.591998

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

Kawabata, H., & Zeki, S. (2004). Neural Correlates of Beauty. Journal of Neurophysiology, 91(4), 1699–1705. https://doi.org/10.1152/jn.00696.2003

Kedia, G., Mussweiler, T., Mullins, P., & Linden, D. E. J. (2013). The neural correlates of beauty comparison. Social Cognitive and Affective Neuroscience, 9(5), 681–688. https://doi.org/10.1093/scan/nst026

Monet, C. (1900). The Artist’s Garden at Giverny [Painting]. https://www.claude-monet.com/the-artists-garden-at-giverny.jsp#prettyPhoto[image2]/0/

Nadal, M., Marty, G., & Munar, E. (2006). The Search for Objective Measures of Aesthetic Judgment: The Case of Memory Traces. Empirical Studies of the Arts, 24(1), 95–106. https://doi.org/10.2190/5nj2-7f9j-487p-dcpw

The neurobiology of beauty | Semir Zeki | TEDxUCL. (2012, July 2). [Video]. YouTube. https://www.youtube.com/watch?v=NlzanAw0RP4

Zeki, S., & Chén, O. Y. (2020). The Bayesian‐Laplacian brain. European Journal of Neuroscience, 51(6), 1441–1462. https://doi.org/10.1111/ejn.14540

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

Psychedelics and the predictive mind: A review of the potential mechanisms that underpin the efficacy of psilocybin to treat depressive disorders. 

— Literature Thesis

Barrett, F.S., Johnson, M.W., Griffiths, R.R., 2015. Validation of the revised Mystical Experience Questionnaire in experimental sessions with psilocybin. J Psychopharmacol (Oxford) 29, 1182–1190. doi:10.1177/0269881115609019

Barrett, F.S., Robbins, H., Smooke, D., Brown, J.L., Griffiths, R.R., 2017. Qualitative and Quantitative Features of Music Reported to Support Peak Mystical Experiences during Psychedelic Therapy Sessions. Front. Psychol. 8, 1238. doi:10.3389/fpsyg.2017.01238

Bastos, A.M., Usrey, W.M., Adams, R.A., Mangun, G.R., Fries, P., Friston, K.J., 2012. Canonical microcircuits for predictive coding. Neuron 76, 695–711. doi:10.1016/j.neuron.2012.10.038

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Berkovich-Ohana, A., Dor-Ziderman, Y., Glicksohn, J., Goldstein, A., 2013. Alterations in the sense of time, space, and body in the mindfulness-trained brain: a neurophenomenologically-guided MEG study. Front. Psychol. 4, 912. doi:10.3389/fpsyg.2013.00912

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Bishop, S.R., 2002. What do we really know about mindfulness-based stress reduction? Psychosom. Med. 64, 71–83. doi:10.1097/00006842-200201000-00010

Borsboom, D., 2017. A network theory of mental disorders. World Psychiatry 16, 5–13. doi:10.1002/wps.20375

Borsboom, D., Cramer, A.O.J., 2013. Network analysis: an integrative approach to the structure of psychopathology. Annu. Rev. Clin. Psychol. 9, 91–121. doi:10.1146/annurev-clinpsy-050212-185608

Borsboom, D., Cramer, A.O.J., Schmittmann, V.D., Epskamp, S., Waldorp, L.J., 2011. The small world of psychopathology. PLoS ONE 6, e27407. doi:10.1371/journal.pone.0027407

Borsboom, D., Cramer, A., Kalis, A., 2019. Brain disorders? Not really… Why network structures block reductionism in psychopathology research. Behav. Brain Sci. 1–54. doi:10.1017/S0140525X17002266

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Brewer, J.A., Garrison, K.A., Whitfield-Gabrieli, S., 2013. What about the “Self” is Processed in the Posterior Cingulate Cortex? Front. Hum. Neurosci. 7, 647. doi:10.3389/fnhum.2013.00647

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Carhart-Harris, R.L., 2018. The entropic brain – revisited. Neuropharmacology 142, 167–178. doi:10.1016/j.neuropharm.2018.03.010

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Carhart-Harris, R.L., Bolstridge, M., Rucker, J., Day, C.M.J., Erritzoe, D., Kaelen, M., Bloomfield, M., Rickard, J.A., Forbes, B., Feilding, A., Taylor, D., Pilling, S., Curran, V.H., Nutt, D.J., 2016a. Psilocybin with psychological support for treatment-resistant depression: an open-label feasibility study. Lancet Psychiatry 3, 619–627. doi:10.1016/S2215-0366(16)30065-7

Carhart-Harris, R.L., Erritzoe, D., Williams, T., Stone, J.M., Reed, L.J., Colasanti, A., Tyacke, R.J., Leech, R., Malizia, A.L., Murphy, K., Hobden, P., Evans, J., Feilding, A., Wise, R.G., Nutt, D.J., 2012. Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin. Proc Natl Acad Sci USA 109, 2138–2143. doi:10.1073/pnas.1119598109

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Carhart-Harris, R.L., Friston, K.J., 2019. REBUS and the anarchic brain: toward a unified model of the brain action of psychedelics. Pharmacol. Rev. 71, 316–344. doi:10.1124/pr.118.017160

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Painting Hypotheses, Cooking Methods, and Composing Results: A Review on “Proust was a Neuroscientist” 

— Original Piece

Lehrer, J. (2012). Proust was a neuroscientist. Edinburgh: Canongate.

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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Haller, M., Donoghue, T., Peterson, E., Varma, P., & Sebastian, P. (2018). Parameterizing neural power spectra. BioRxiv. http://dx.doi.org/10.1101/299859

He, B. J. (2015). Scale-free brain activity: past, present and future Biyu. Trends Cogn Sci., 18(9), 480–487. https://doi.org/10.1016/j.tics.2014.04.003.Scale-free

Herrmann, C. S., Strüber, D., Helfrich, R. F., & Engel, A. K. (2016). EEG oscillations : From correlation to causality. International Journal of Psychophysiology, 103, 12–21. https://doi.org/10.1016/j.ijpsycho.2015.02.003

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Jensen, O., & Tesche, C. D. (2002). Frontal theta activity in humans increases with memory load in a working memory task: Frontal theta increases with memory load. European Journal of Neuroscience, 15(8), 1395–1399. https://doi.org/10.1046/j.1460-9568.2002.01975.x

Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance : a review and analysis. Brain Research Reviews, 29, 169–195.

Leemburg, S., Gao, B., Cam, E., Sarnthein, J., & Bassetti, C. L. (2018). Power spectrum slope is related to motor function after focal cerebral ischemia in the rat. (August), 1–12. https://doi.org/10.1093/sleep/zsy132

Lombardi, F., Herrmann, H. J., & de Arcangelis, L. (2017). Balance of excitation and inhibition determines 1/f power spectrum in neuronal networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27(4), 047402. https://doi.org/10.1063/1.4979043

Peterson, E. J., Rosen, B. Q., Campbell, A. M., Belger, A., & Voytek, B. (2017). 1 / f neural noise is a better predictor of schizophrenia than neural oscillations. BioRxiv. http://dx.doi.org/10.1101/113449

Pritchard, W. S. (1992). The Brain in Fractal Time : 1 / F-Like Power Spectrum Scaling of the Human Electroencephalogram. International Journal of Neuroscience, 1–2(66). https://doi.org/10.3109/00207459208999796

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Sadaghiani, S., & Kleinschmidt, A. (2016). Brain Networks and α-Oscillations: Structural and Functional Foundations of Cognitive Control. Trends in Cognitive Sciences, 20(11), 805–817. https://doi.org/10.1016/j.tics.2016.09.004

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Voytek, X. B., Kramer, M. A., Case, J., Lepage, K. Q., Tempesta, Z. R., Knight, R. T., & Gazzaley, A. (2015). Age-Related Changes in 1 / f Neural Electrophysiological Noise. The Journal of Neuroscience, 35(38), 13257–13265. https://doi.org/10.1523/JNEUROSCI.2332-14.2015

Waschke, L., Wöstmann, M., & Obleser, J. (2017). States and traits of neural irregularity in the age-varying human brain. Scientific Reports, 7(1), 17381. https://doi.org/10.1038/s41598-017-17766-4

Wen, H., & Liu, Z. (2016). Separating Fractal and Oscillatory Components in the Power Spectrum of Neurophysiological Signal. Brain Topography, 29(1), 13–26. https://doi.org/10.1007/s10548-015-0448-0

Issue 10

Structural Magnetic Resonance Imaging (MRI) and neuropsychological correlates in an elderly non-Western immigrant population 

— Research Project

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Katz, M. J., Lipton, R. B., Hall, C. B., Zimmerman, M. E., Sanders, A. E., Verghese, J., Dickson, D.W., & Derby, C. A. (2012). Age and sex specific prevalence and incidence of mild cognitive impairment, dementia and Alzheimer’s dementia in blacks and whites: A report from the Einstein Aging Study. Alzheimer disease and associated disorders, 26(4), 335. 

Adelman, S., Blanchard, M., Rait, G., Leavey, G., & Livingston, G. (2011). Prevalence of dementia in African–Caribbean compared with UK-born White older people: two-stage cross-sectional study. The British Journal of Psychiatry, 199(2), 119-125. 

Livingston, G., Leavey, G., Kitchen, G., Manela, M., Sembhi, S., & Katona, C. (2001). Mental health of migrant elders—the Islington study. The British Journal of Psychiatry, 179(4), 361-366. 

Parlevliet, J. L., Uysal‐Bozkir, Ö., Goudsmit, M., Campen, J. P., Kok, R. M., Riet, G., … & Rooij, S. E. (2016). Prevalence of mild cognitive impairment and dementia in older non‐western immigrants in the Netherlands: a cross‐sectional study. International journal of geriatric psychiatry, 31(9), 1040-1049. 

Goudsmit, M., Uysal-Bozkir, Ö., Parlevliet, J. L., van Campen, J. P., de Rooij, S. E., & Schmand, B. (2017). The Cross-Cultural Dementia Screening (CCD): A new neuropsychological screening instrument for dementia in elderly immigrants. Journal of clinical and experimental neuropsychology, 39(2), 163-172. 

Uitewaal, P. J. M., Manna, D. R., Bruijnzeels, M. A., Hoes, A. W., & Thomas, S. (2004). Prevalence of type 2 diabetes mellitus, other cardiovascular risk factors, and cardiovascular disease in Turkish and Moroccan immigrants in North West Europe: a systematic review. Preventive medicine, 39(6), 1068-1076. 

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Zahodne, B. L., Manly J., Narkhede, A., Griffith, E. Y., DeCarli, C., Schupf, S. N., Mayeux, R. & Brickman, A. M. (2015). Structural MRI predictors of late-life cognition differ across African Americans, Hispanics, and Whites. Current Alzheimer Research, 12(7), 632-639. 

Jack Jr, C. R., Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Weiner, M. W., … & Trojanowski, J. Q. (2010). Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. The Lancet Neurology, 9(1), 119-128. 

Mungas, D., Reed, B. R., Farias, S. T., & DeCarli, C. (2009). Age and education effects on relationships of cognitive test scores with brain structure in demographically diverse older persons. Psychology and aging, 24(1), 116. 

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Bindraban, N. R., van Valkengoed, I. G., Mairuhu, G., Holleman, F., Hoekstra, J. B., Michels, B. P., Koopmans, R.P., & Stronks, K. (2008). Prevalence of diabetes mellitus and the performance of a risk score among Hindustani Surinamese, African Surinamese and ethnic Dutch: a cross-sectional population-based study. BMC public health, 8(1), 271. 

Agyemang, C., van Oeffelen, A. A., Norredam, M., Kappelle, L. J., Klijn, C. J., Bots, M.      L., Stronks K., & Vaartjes, I. (2014). Socioeconomic inequalities in stroke incidence among migrant groups: analysis of nationwide data. Stroke, 45(8), 2397-2403. 

Agyemang, C., Bindraban, N., Mairuhu, G., Van Montfrans, G., Koopmans, R., & Stronks, K. (2005). Prevalence, awareness, treatment, and control of hypertension among Black Surinamese, South Asian Surinamese and White Dutch in Amsterdam, The Netherlands: The SUNSET study. J Hypertens, 23(11), 1971-1977. 

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Goudsmit, M., Uysal-Bozkir, Ö., Parlevliet, J. L., van Campen, J. P., de Rooij, S. E., & Schmand, B. (2017). The Cross-Cultural Dementia Screening (CCD): A new neuropsychological screening instrument for dementia in elderly immigrants. Journal of clinical and experimental neuropsychology, 39(2), 163-172. 

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Jokinen, H., Kalska, H., Mäntylä, R., Ylikoski, R., Hietanen, M., Pohjasvaara, T., … & Erkinjuntti, T. (2005). White matter hyperintensities as a predictor of neuropsychological deficits post-stroke. Journal of Neurology, Neurosurgery & Psychiatry, 76(9), 1229-1233. 

Shim, Y. S., Youn, Y. C., Na, D. L., Kim, S. Y., Cheong, H. K., Moon, S. Y., … & Kang, H. (2011). Effects of medial temporal atrophy and white matter hyperintensities on the cognitive functions in patients with Alzheimer’s disease. European neurology, 66(2), 75- 82. 

Rami, L., Solé‐Padullés, C., Fortea, J., Bosch, B., Lladó, A., Antonell, A., … & Molinuevo, J. L. (2012). Applying the new research diagnostic criteria: MRI findings and neuropsychological correlations of prodromal AD. International journal of geriatric psychiatry, 27(2), 127-134. 

Pantel, J., Schönknecht, P., Essig, M., & Schröder, J. (2004). Distribution of cerebral atrophy assessed by magnetic resonance imaging reflects patterns of neuropsychological deficits in Alzheimer’s dementia. Neuroscience letters, 361(1-3), 17-20. 

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Papp, K. V., Kaplan, R. F., Springate, B., Moscufo, N., Wakefield, D. B., Guttmann, C. R., & Wolfson, L. (2014). Processing speed in normal aging: Effects of white matter hyperintensities and hippocampal volume loss. Aging, Neuropsychology, and Cognition, 21(2), 197-213. 

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Gorelick, P. B., Scuteri, A., Black, S. E., DeCarli, C., Greenberg, S. M., Iadecola, C., … & Petersen, R. C. (2011). Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 42(9), 2672-2713. 

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Möller, C., van der Flier, W. M., Versteeg, A., Benedictus, M. R., Wattjes, M. P., Koedam, E. L., … & Vrenken, H. (2014). Quantitative regional validation of the visual rating scale for posterior cortical atrophy. European radiology, 24(2), 397-404. 

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Effective connectivity from the Nucleus accumbens towards the frontal cortex during attentional focus 

— Research Project

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Anticevic, A., Hu, S., Zhang, S., Savic, A., Billingslea, E., Wasylink, S., … & Bloch, M. H. (2014). Global resting-state functional magnetic resonance imaging analysis identifies frontal cortex, striatal, and cerebellar dysconnectivity in obsessive-compulsive disorder. Biological psychiatry, 75(8), 595-605. 

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Denys, D., Mantione, M., Figee, M., van den Munckhof, P., Koerselman, F., Westenberg, H., … & Schuurman, R. (2010). Deep brain stimulation of the nucleus accumbens for treatment-refractory obsessive-compulsive disorder. Archives of general psychiatry, 67(10), 1061-1068. 

Feusner, J. D., Moody, T., Hembacher, E., Townsend, J., McKinley, M., Moller, H., & Bookheimer, S. (2010). Abnormalities of visual processing and frontostriatal systems in body dysmorphic disorder. Archives of general psychiatry, 67(2), 197-205. 

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Göttlich, M., Krämer, U. M., Kordon, A., Hohagen, F., & Zurowski, B. (2014). Decreased limbic and increased fronto‐ parietal connectivity in unmedicated patients with obsessive‐compulsive disorder. Human brain mapping, 35(11), 5617- 5632. 

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Helfrich, R. F., Huang, M., Wilson, G., & Knight, R. T. (2017). Prefrontal cortex modulates posterior alpha oscillations during top-down guided visual perception. Proceedings of the National Academy of Sciences, 114(35), 9457-9462. 

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Liu, Y., Bengson, J., Huang, H., Mangun, G. R., & Ding, M. (2014). Top-down modulation of neural activity in anticipatory visual attention: control mechanisms revealed by simultaneous EEG-fMRI. Cerebral cortex, 26(2), 517- 529. 

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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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Buckels, E. E., Trapnell, P. D., & Paulhus, D. L. (2014). Trolls just want to have fun. Personality and individual Differences67, 97-102.

Calkins, S. D., & Keane, S. P. (2009). Developmental origins of early antisocial behavior. Development and psychopathology21(4), 1095-1109.

Carrillo, M., Migliorati, F., Bruls, R., Han, Y., Heinemans, M., Pruis, I., … & Keysers, C. (2015). Repeated witnessing of conspecifics in pain: effects on emotional contagion. PloS one10(9)

Davis, M. H. (1983). Measuring individual differences in empathy: Evidence for a multidimensional approach. Journal of Personality and Social Psychology, 44(1), 113-126.

De Vignemont, F., & Singer, T. (2006). The empathic brain: how, when and why?. Trends in cognitive sciences10(10), 435-441.

Decety, J., Bartal, I. B., Uzefovsky, F., & Knafo-Noam, A. (2016). Empathy as a driver of prosocial behaviour: highly conserved neurobehavioural mechanisms across species. Philosophical transactions of the Royal Society of London. Series B, Biological sciences371(1686), 20150077.

Decety, J., & Lamm, C. (2006). Human empathy through the lens of social neuroscience. The scientific World journal6, 1146-1163.

Dynel, M. (2016). “Trolling is not stupid”: Internet trolling as the art of deception serving entertainment. Intercultural Pragmatics13(3), 353-381.

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Fichman, P., & Sanfilippo, M. R. (2015). The bad boys and girls of cyberspace: How gender and context impact perception of and reaction to trolling. Social science computer review33(2), 163-180.

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Lamm, C., Decety, J., & Singer, T. (2011). Meta-analytic evidence for common and distinct neural networks associated with directly experienced pain and empathy for pain. Neuroimage54(3), 2492-2502.

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Lockwood, P. L., Apps, M. A., Valton, V., Viding, E., & Roiser, J. P. (2016). Neurocomputational mechanisms of prosocial learning and links to empathy. Proceedings of the National Academy of Sciences113(35), 9763-9768.

Lockwood, P. L., Seara-Cardoso, A., & Viding, E. (2014). Emotion regulation moderates the association between empathy and prosocial behavior. PloS one9(5), e96555.

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Mitsopoulou, E., & Giovazolias, T. (2015). Personality traits, empathy and bullying behaviour: A meta-analytic approach. Aggression and Violent Behavior, 21, 61–72. 

Niven, K., Totterdell, P., & Holman, D. (2009). A classification of controlled interpersonal affect regulation strategies. Emotion9(4), 498.

Niven, K., Totterdell, P., Stride, C. B., & Holman, D. (2011). Emotion Regulation of Others and Self (EROS): The development and validation of a new individual difference measure. Current Psychology30(1), 53-73.

Phillips, W. (2011). LOLing at tragedy: Facebook trolls, memorial pages and resistance to grief online. First Monday, 16(12), 1–14.

Phillips, W. (2015). This is why we can’t have nice things: Mapping the relationship between online trolling and mainstream culture. Cambridge, MA: MIT Press.

Reidy, D. E., Zeichner, A., & Seibert, L. A. (2011). Unprovoked aggression: Effects of psychopathic traits and sadism. Journal of personality79(1), 75-100.

Sest, N., & March, E. (2017). Constructing the cyber-troll: Psychopathy, sadism, and empathy. Personality and Individual Differences119, 69-72.

Singer, T., & Lamm, C. (2009). The social neuroscience of empathy. Annals of the New York Academy of Sciences1156(1), 81-96.

Singer, T., Seymour, B., O’doherty, J., Kaube, H., Dolan, R. J., & Frith, C. D. (2004). Empathy for pain involves the affective but not sensory components of pain. Science303(5661), 1157-1162.

Suler, J. (2004). The online disinhibition effect. Cyberpsychology & behavior7(3), 321-326.

What are the neuropsychological consequences of complying to the delivery of an order? 

— Research Project

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Bandura, A. (2006). Toward a psychology of human agency. Perspectives on psychological science 1 (2), 164-180. 

Caspar, Ioumpa, Keysers, & Gazzola (under revision) Obeying orders reduces vicarious brain activation towards victim’s pain 

Caspar, E. A., Cleeremans, A., & Haggard, P. (2018). Only giving orders? An experimental study of the sense of agency when giving or receiving commands. PloS one, 13(9), e0204027. 

Caspar, E. A., Christensen, J. F., Cleeremans, A., & Haggard, P. (2016). Coercion changes the sense of agency in the human brain. Current biology, 26(5), 585-592. 

Davis, M. H. (1980). A multidimensional approach to individual differences in empathy. JSAS Catalog of Selected Documents in Psychology, 10, 8 

Decety, J., & Lamm, C. (2006). Human empathy through the lens of social neuroscience. The scientific World journal, 6, 1146-1163. 

Dewey, J. A., & Knoblich, G. (2014). Do implicit and explicit measures of the sense of agency measure the same thing?. PloS one , 9 (10), e110118. 

Dunwoody, P. T., & Funke, F. (2016). The Aggression-Submission-Conventionalism Scale: Testing a new three factor measure of authoritarianism. 

Engbert, K., Wohlschläger, A., & Haggard, P. (2008). Who is causing what? The sense of agency is relational and efferent-triggered. Cognition , 107 (2), 693-704. 

Graham, J., Haidt, J., & Nosek, B. (2008). Moral Foundations Questionnaire, MFQ 30 revised in July 2008. Extracted, 8, 2008. 

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Hesp, C., Steenbeek, H. W., & Van Geert, P. L. (2019). Socio-emotional concern dynamics in a model of real-time dyadic interaction: parent-child play in autism. Frontiers in psychology , 10 , 1635. 

Hinne, M., Gronau, Q. F., van den Bergh, D., & Wagenmakers, E. J. (2019). A conceptual introduction to Bayesian Model Averaging. 

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Lamm, C., & Majdandžić, J. (2015). The role of shared neural activations, mirror neurons, and morality in empathy–A critical comment. Neuroscience Research, 90, 15-24. 

Lee, M. D., & Wagenmakers, E. J. (2014). Bayesian cognitive modeling: A practical course . Cambridge university press. 

Lepron, E., Causse, M., & Farrer, C. (2015). Responsibility and the sense of agency enhance empathy for pain. Proceedings of the Royal Society B: Biological Sciences, 282(1799), 20142288. 

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Singer, T., & Lamm, C. (2009). The social neuroscience of empathy. Annals of the New York Academy of Sciences , 1156 (1), 81-96. 

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And that's the end of the references 🙂