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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 12
Is That Child Friends... with a Robot? Taking a Look from hte 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
Ferrari, M., Rice, C., & McKenzie, K. (2015). ACE Pathways Project: Therapeutic catharsis in digital storytelling. Psychiatric Services (Washington, DC), 66(5), 556–556. https://doi.org/10.1176/appi.ps.660505
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
— Research Project
Blair, J. (1995). A cognitive developmental approach to morality: Investigating the psychopath. Cognition, 57(1), 1-29.
Blair, J., Mitchell, D., & Blair, K. (2005). The Psychopath: Emotion and the Brain. Blackwell Publishing.
Borsboom, D., & Cramer, A. (2013). Network analysis: an integrative approach to the structure of psychopathology. Annu Rev Clin Psychol, 9, 91-121.
Boschloo, L., van Borkulo, C. D., Rhemtulla, M., Keyes, K. M., Borsboom, D., & Schoevers, R. A. (2015). The Network Structure of Symptoms of the Diagnostic and Statistical Manual of Mental Disorders. PLoS One, 10(9), e0137621. doi:10.1371/journal.pone.0137621
Bringmann, L. F., Lemmens, L. H., Huibers, M. J., Borsboom, D., & Tuerlinckx, F. (2015). Revealing the dynamic network structure of the Beck Depression Inventory-II. Psychol Med, 45(4), 747-757. doi:10.1017/S0033291714001809
Christ, L. (2016). Towards a Network Model of Psychopathy: Lack of Empathy is Central to the Psychopathy Network in Criminal Offenders (Master’s thesis). University of Amsterdam, Amsterdam, the Netherlands.
Cleckley, H. (1941). The mask of sanity; an attempt to reinterpret the so-called psychopathic personality. JAMA, 117(6), 493. doi:10.1001/jama.1941.02820320085028
Coid, J., Yang, M., Ullrich, S., Roberts, A., & Hare, R. D. (2009). Prevalence and correlates of psychopathic traits in the household population of Great Britain. Int J Law Psychiatry, 32(2), 65-73. doi:10.1016/j.ijlp.2009.01.002
Costantini, G., Epskamp, S., Borsboom, D., Perugini, M., Mõttus, R., Waldorp, L. J., & Cramer, A. (2015). State of the aRt personality research: A tutorial on network analysis of personality data in R. J Res Pers, 54, 13-29. doi:10.1016/j.jrp.2014.07.003
Cramer, A., Borsboom, D., Aggen, S., & Kendler, K. (2012). The pathoplasticity of dysphoric episodes: differential impact of stressful life events on the pattern of depressive symptom inter-correlations. Psychol Med, 42(05), 957-965.
Dawel, A., O’Kearney, R., McKone, E., & Palermo, R. (2012). Not just fear and sadness: meta-analytic evidence of pervasive emotion recognition deficits for facial and vocal expressions in psychopathy. Neurosci Biobehav Rev, 36(10), 2288-2304.
Epskamp, S. (2015). bootnet: Bootstrap methods for various network estimation routines. Retrieved from https://cran.r-project.org/web/packages/bootnet/bootnet.pdf
Epskamp, S., Borsboom, D., & Fried, E. (2016). Estimating Psychological Networks and their Stability: a Tutorial Paper. arXiv preprint arXiv:1604.08462.
Epskamp, S., Cramer, A., Waldorp, L., Schmittmann, V., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. J Stat Softw, 48(4), 1-18.
Frick, P. J. (2004). The inventory of callous-unemotional traits. Unpublished rating scale.
Frick, P. J., Ray, J. V., Thornton, L. C., & Kahn, R. E. (2014). Can callous-unemotional traits enhance the understanding, diagnosis, and treatment of serious conduct problems in children and adolescents? A comprehensive review. Psychol Bull, 140(1), 1-57. doi:10.1037/a0033076
Fried, E. I., Epskamp, S., Nesse, R. M., Tuerlinckx, F., & Borsboom, D. (2016). What are ‘good’ depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. J Affect Disord, 189, 314-320. doi:10.1016/j.jad.2015.09.005
Fruchterman, T. M., & Reingold, E. M. (1991). Graph drawing by force‐ directed placement. Softw Pract Exp, 21(11), 1129-1164.
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Hare, R. D. (1991). The Hare Psychopathy Checklist—Revised. Toronto: Multi-Health Systems.
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Hare, R. D., & Neumann, G. S. (2005). The PCL-R Assessment of Psychopathy: Development, Structural Properties, and New Directions. In C. J. Patrick (Ed.), Handbook of psychopathy (pp. 58-88). Guilford Press.
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Jolliffe, D., & Farrington, D. P. (2004). Empathy and offending: A systematic review and meta-analysis. Aggress Violent Behav, 9(5), 441-476.
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Lilienfeld, S. O., & Fowler, K. A. (2005). The Self-Report Assessment of Psychopathy: Problems, Pitfalls, and Promises. In C. J. Patrick (Ed.), Handbook of psychopathy (pp. 107–133). Guilford Press. Lilienfeld, S. O., & Widows, M. R. (2005). PPI-R: Psychopathic personality inventory revised: Professional
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Miller, J. D., Jones, S. E., & Lynam, D. R. (2011). Psychopathic traits from the perspective of self and informant reports: Is there evidence for a lack of insight? J Abnorm Psychol, 120(3), 758.
Montagne, B., van Honk, J., Kessels, R. P., Frigerio, E., Burt, M., van Zandvoort, M. J., … & de Haan, E. H. (2005). Reduced efficiency in recognising fear in subjects scoring high on psychopathic personality characteristics. Pers Individ Dif, 38(1), 5-11.
Murphy, B., Lilienfeld, S., Skeem, J., & Edens, J. (2016, February 11). Are Fearless Dominance Traits Superfluous in Operationalizing Psychopathy? Incremental Validity and Sex Differences. Psychol Assess. Advance online publication. http://dx.doi.org/10.1037/pas0000288
Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Soc Networks, 32(3), 245-251.
Patrick, C. J. (2005). Back to the Future: Cleckley as a Guide to the Next Generation of Psychopathy Research. In C. J. Patrick (Ed.), Handbook of psychopathy (pp. 605-617). Guilford Press.
Poythress, N. G., Lilienfeld, S. O., Skeem, J. L., Douglas, K. S., Edens, J. F., Epstein, M., & Patrick, C. J. (2010). Using the PCL-R to help estimate the validity of two self-report measures of psychopathy with offenders. Assess, 17(2), 206-219. doi:10.1177/1073191109351715
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The Female Experience of Autism
— Original Piece
American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders: Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Arlington, VA: American Psychiatric Association, (2013).
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Baron-Cohen, S. (2010). Empathizing, systemizing, and the extreme male brain theory of autism. Sex Differences In The Human Brain, Their Underpinnings And Implications, 167-175. doi: 10.1016/b978-0-444-53630-3.00011-7
Baron-Cohen, S., Bowen, D., Holt, R., Allison, C., Auyeung, B., & Lombardo, M. et al. (2015). The “Reading the Mind in the Eyes” Test: Complete Absence of Typical Sex Difference in ~400 Men and Women with Autism. PLOS ONE, 10(8), e0136521. doi: 10.1371/journal.pone.0136521
Bishop, D. (2010). Overlaps Between Autism and Language Impairment: Phenomimicry or Shared Etiology?. Behavior Genetics, 40(5), 618-629. doi: 10.1007/s10519-010-9381-x
Cage, E., & Troxell-Whitman, Z. (2019). Understanding the Reasons, Contexts and Costs of Camouflaging for Autistic Adults. Journal Of Autism And Developmental Disorders, 49(5), 1899-1911. doi: 10.1007/s10803-018-03878-x
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The impact of motivated reasoning on (un)sustainable food
decisions and sketching new pathways for effective
— Literature Thesis
Abrahamse, W., Steg, L., Vlek, C., & Rothengatter, T. (2007). The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents. Journal of Environmental Psychology, 27(4),
265-276. https://doi.org/10.1016/j.jenvp.2007.08.002
Ang, C.-S., Chan, N.-N., & Singh, L. (2019). A comparison study of meat eaters and nonmeat
eaters on mind attribution and moral disengagement of animals. Appetite, 136 80-
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Baekgaard, M., Christensen, J., Dahlmann, C. M., Mathiasen, A., & Petersen, N. B. G.
(2019). The role of evidence in politics: Motivated reasoning and persuasion among
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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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— 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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Effective connectivity from the Nucleus accumbens towards the frontal cortex during attentional focus
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What happens to a troll in daylight? Interpersonal affect regulation and empathic
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— Research Project
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