Comparative Analysis of Sentiments in Children with Neurodevelopmental Disorders
Abstract In-group favoritism is the tendency of people where, individuals tend to punish transgressors with varying intensity based on whether they belong to their own group or not. In this cross-sectional analytical study, we examine matched samples of children with developmental disorders, observing their perspectives on punishment after watching two videos in which rules are broken. Data (video 1) shows a football player from the viewer’s country scoring a handball goal, while in data (video 2), a foreign player replicates the same action against the host nation. Every contestant viewed both videos, and their responses were then compared. Our proposed methods compare and analyze the data to determine player’s opinions using artificial intelligence-based machine learning such as text analysis and opinion, extract on- favorable, unfavorable, neutral feelings, or emotions. In both sets of data, the autism spectrum disorder (ASD) group displayed negative emotions for both video 1 (M = −.1; CI 90% −.41 to .21) and video 2 (t (7) = 1.54, p =.12; M = -.42; CI 90% 76 to -.08). On the contrary, the groups with attention deficit hyperactivity disorder (ADHD), learning disabilities (LD), and intellectual disability (ID) had a favorable reaction to video1 but an unfavorable reaction to video 2. Children diagnosed with ASD typically display a consistent adherence to rules, even when those breaking the rules are not part of their group. This behavior may be linked to lower levels of empathy.
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Morales-Hidalgo, P., Voltas-Moreso, N., Hernández-Martínez, C., & Canals-Sans, J. (2023). Emotional problems in preschool and school-aged children with neurodevelopmental disorders in Spain: EPINED epidemiological project. Research in Developmental Disabilities, 135, 104454. https://doi.org/10.1016/j.ridd.2023.104454
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Buttelmann, D., & Böhm, R. (2014). The Ontogeny of the Motivation That Underlies In-Group Bias. Psychological Science, 25(4), 921–927. https://doi.org/10.1177/0956797613516802
Corcoran, C. M., Carrillo, F., Fernández-Slezak, D., Bedi, G., Klim, C., Javitt, D. C., Bearden, C. E., & Cecchi, G. A. (2018). Prediction of psychosis across protocols and risk cohorts using automated language analysis. World Psychiatry, 17(1), 67–75. https://doi.org/10.1002/wps.20491
Descioli, P., & Kurzban, R. (2013). A solution to the mysteries of morality. Psychological Bulletin, 139(2), 477–496. https://doi.org/10.1037/a0029065
Edmiston, E. K., Merkle, K., & Corbett, B. A. (2014). Neural and cortisol responses during play with human and computer partners in children with autism. Social Cognitive and Affective Neuroscience, 10(8), 1074–1083. https://doi.org/10.1093/SCAN/NSU159
Elster, J. (2011). Norms. https://academic.oup.com/edited-volume/38173/chapter/333033124
Gardner-Hoag, J., Novack, M., Parlett-Pelleriti, C., Stevens, E., Dixon, D., & Linstead, E. (2021). Unsupervised Machine Learning for Identifying Challenging Behavior Profiles to Explore Cluster-Based Treatment Efficacy in Children With Autism Spectrum Disorder: Retrospective Data Analysis Study. JMIR Med Inform 2021, 9(6), E27793. https://doi.org/10.2196/27793
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Hemalatha, R., & Monicka, M. B. (2018). Sentiment analysis on myocardial infarction using tweets data. Int J Comput Sci Technol, 9(4), 61–65. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Hemalatha%2C+R.%2C+%26+Monicka%2C+M.+B.+%282018%29.+Sentiment+analysis+on+myo-+cardial+infarction+using+tweets+data.+International+Journal+of+Com-+puter+Science+and+Technology%2C+9%284%29%2C+61–65.+Retrieved+from+http%3A%2F%2F+www.ijcst.com%2Fvol9%2Fissue4%2F12-m-b-monicka.pdf.&btnG=
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Hernandez-Suarez, A., Sanchez-Perez, G., Toscano-Medina, K., Martinez-Hernandez, V., Perez-Meana, H., Olivares-Mercado, J., & Sanchez, V. (2018). Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization. Sensors, 18(5), 1380. https://doi.org/10.3390/s18051380
Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University - Engineering Sciences, 30(4), 330–338. https://doi.org/10.1016/J.JKSUES.2016.04.002
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Knoch, D., Gianotti, L. R. R., Baumgartner, T., & Fehr, E. (2010). A neural marker of costly punishment behavior. Psychological Science, 21(3), 337–342. https://doi.org/10.1177/0956797609360750
Knoch, D., Pascual-Leone, A., Meyer, K., Treyer, V., & Fehr, E. (2006). Diminishing reciprocal fairness by disrupting the right prefrontal cortex. Science, 314(5800), 829–832. https://doi.org/10.1126/science.1129156
Korkiakangas, T., Dindar, K., Laitila, A., & Kärnä, E. (2016). The Sally–Anne test: an interactional analysis of a dyadic assessment. International Journal of Language and Communication Disorders, 51(6), 685–702. https://doi.org/10.1111/1460-6984.12240
Krueger, F., & Hofftman, M. (2016). The Emerging Neuroscience of Third-Party Punishment. Trends In Neurosciences, 39(8), 499–501. https://doi.org/10.1016/j.tins.2016.06.004
Krupp, D. B., Barclay, P., Daly, M., Kiyonari, T., Dingle, G., & Wilson, M. (2005). Let’s add some psychology (and maybe even some evolution) to the mix. Behavioral and Brain Sciences, 28(6), 828–829. https://doi.org/10.1017/S0140525X0535014X
Le, C. H. H. (2016). The prevalence of anemia and moderate-severe anemia in the US population (NHANES 2003-2012). PLoS ONE, 11(11), 1–14. https://doi.org/10.1371/journal.pone.0166635
Liu, X., Hu, X., Shi, K., & Mai, X. (2018) Empathy modulates the evaluation processing of altruistic outcomes. Frontiers In Psychology, 9. https://doi.org/10.3389/fpsyg.2018.00407
Löytömäki, J., Laakso, M. L., & Huttunen, K. (2022). Social-Emotional and Behavioural Difficulties in Children with Neurodevelopmental Disorders: Emotion Perception in Daily Life and in a Formal Assessment Context. Journal of Autism and Developmental Disorders, 53, 4744–4758. https://doi.org/10.1007/s10803-022-05768-9
Manthorpe, N. S. J. (2001). Responding to students’ mental health needs: Impermeable systems and diverse users. Journal of Mental Health, 10(1), 41–52. https://doi.org/10.1080/2-09638230020023606
Mäntylä, M. V., Graziotin, D., & Kuutila, M. (2018). The evolution of sentiment analysis—A review of research topics, venues, and top cited papers. Computer Science Review, 27, 16–32. https://doi.org/10.1016/J.COSREV.2017.10.002
Morales-Hidalgo, P., Voltas-Moreso, N., Hernández-Martínez, C., & Canals-Sans, J. (2023). Emotional problems in preschool and school-aged children with neurodevelopmental disorders in Spain: EPINED epidemiological project. Research in Developmental Disabilities, 135, 104454. https://doi.org/10.1016/j.ridd.2023.104454
Na, S. D., & Burns, T. G. (2016). Wechsler Intelligence Scale for Children-V: Test Review. Applied Neuropsychology: Child, 5(2), 156–160. https://doi.org/10.1080/21622965.2015.1015337
Petersen, N. H. T., Olsen, O. D., Groth-Pedersen, L., Ellegaard, A. M., Bilgin, M., Redmer, S., Ostenfeld, M. S., Ulanet, D., Dovmark, T. H., Lønborg, A., Vindeløv, S. D., Hanahan, D., Arenz, C., Ejsing, C. S., Kirkegaard, T., Rohde, M., Nylandsted, J., & Jäättelä, M. (2013). Transformation-Associated Changes in Sphingolipid Metabolism Sensitize Cells to Lysosomal Cell Death Induced by Inhibitors of Acid Sphingomyelinase. Cancer Cell, 24(3), 379–393. https://doi.org/10.1016/j.ccr.2013.08.003
Pollard, R., Salter, I., Sanders, R., Lucas, M., Nature, C. M., et al. (2009). Southern Ocean deep-water carbon export enhanced by natural iron fertilization. Nature, 457, 577–580. https://doi.org/10.1038/nature07716
Ramani, R. G., & Sivaselvi, K. (2017). Autism spectrum disorder identification using data mining techniques. International journal of pure and applied mathematics, 117(16), 427–436. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Ramani%2C+R.+G.%2C+%26+Sivaselvi%2C+K.+%282017%29.+Autism+spectrum+disorder+identification+using+data++++mining+techniques.+International+Journal+of+Pure+and+Applied+Mathematics%2C+117%2816%29%2C+427-436.&btnG=
Schiller, B., Baumgartner, T., & Knoch, D. (2014). Intergroup bias in third-party punishment stems from both ingroup favoritism and outgroup discrimination. Evolution and Human Behavior, 35(3), 169–175. https://doi.org/10.1016/j.evolhumbehav.2013.12.006
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