Machine Learning based Psychology: Advocating for A Data-Driven Approach
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Jorge I. Vélez
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Vélez, J. I. . (2021). Machine Learning based Psychology: Advocating for A Data-Driven Approach. International Journal of Psychological Research, 14(1). https://doi.org/10.21500/20112084.5365
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The editorial board reserves the right of amendments deemed necessary in the application of the rules of publication.
To give up copyright, the authors allow that, International Journal of Psychological Research, distribute the work more broadly, check for the reuse by others and take care of the necessary procedures for the registration and administration of copyright; at the same time, our editorial board represents the interests of the author and allows authors to re-use his work in various forms. In response to the above, authors transfer copyright to the journal, International Journal of Psychological Research. This transfer does not imply other rights which are not those of authorship (for example those that concern about patents). Likewise, preserves the authors rights to use the work integral or partially in lectures, books and courses, as well as make copies for educational purposes. Finally, the authors may use freely the tables and figures in its future work, wherever make explicit reference to the previous publication in International Journal of Psychological Research. The assignment of copyright includes both virtual rights and forms of the article to allow the editorial to disseminate the work in the manner which it deems appropriate.
The editorial board reserves the right of amendments deemed necessary in the application of the rules of publication.
Abstract
Since its beginnings, Psychology has been prone to both data generation and understanding of human behavior through data analysis. Back in 1879, Dr. Wilheim Wundt opened the first experimental psychology lab at the University of Leipzig to study reaction times. To many, this is considered the start of Psychology as a separate scientific discipline and the use of data analysis for data-driven decision making in the field (Flis, 2019; Tweney, 2003). In this Editorial, we briefly discuss how Psychology students, clinicians, and researchers may take part of the data revolution and help transforming Psychology, as we know it, into Machine Learning Psychology.
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References
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Gudivada, V., Irfan, M., Fathi, E., & Rao, D. (2016). Cognitive Analytics: Going Beyond Big Data Analytics and Machine Learning. Handbook of Statistics, 35, 169–205. https://doi.org/10.1016/bs.host.2016.07.010.
Arcos-Burgos, M., Vélez, J., Martinez, A., Ribasés, M., Ramos-Quiroga, J., Sánchez-Mora, C., Richarte, V., Roncero, C., Cormand, B., Fernández-Castillo, N., Casas, M., Lopera, F., Pineda, D., Palacio, J., Acosta-López, J., Cervantes-Henriquez, M., Sánchez-Rojas, M., Puentes-Rozo, P., Molina, B., & Muenke, M. (2019). ADGRL3 (LPHN3) Variants Predict Substance Use Disorder. Translational Psychiatry, 9 (1), e42. https://doi.org/10.1038/s41398-019-0396-7.
Bachli, M. B., Sedeño, L., Ochab, J. K., Piguet, O., Kumfor, F., Reyes, P., Torralva, T., Roca, M., Cardona, J. F., Campo, C. G., Herrera, E., Slachevsky, A., Matallana, D., Manes, F., García, A. M., Ibáñez, A., & Chialvo, D. R. (2020). Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach. NeuroImage, 208, 116456. https://doi.org/10.1016/j.neuroimage.2019.116456.
Bell, M. A., & Cuevas, K. (2012). Using EEG to Study Cognitive Development: Issues and Practices. Journal of cognition and development: official journal of the Cognitive Development Society. 13 (3). 281–294. https://doi.org/10.1080/15248372.2012.691143.
Bezanson, J., Karpinski, S., Shah, V., & Edelman, A. (2012). WhyWe Created Julia. https://julialang.org/blog/2012/02/why-we-created-julia/.
Bone, D., Goodwin, M. S., Black, M. P., Lee, C. C., Audhkhasi, K., & Narayanan, S. (2015). Applying machine learning to facilitate autism diagnostics: Pitfalls and promises. Journal of autism and developmental disorders, 45 (5), 1121–1136. https://doi.org/10.1007/s10803-014-2268-6.
Bragazzi, N. L. (2013). Rethinking psychiatrywithOMICS science in the age of personalized P5 medicine: ready for psychiatome? Philosophy, ethics, and humanities in medicine: PEHM, 8, Article 4. https://doi.org/10.1186/1747-5341-8-4.
Breiman, L. (2001). Random forests. Machine Learning, 45 (1), 5–32. https://doi.org/10.1023/A:1010933404324.
Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. (1984). Classification andRegression Trees.Routledge.
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16) (pp.785–794). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785.
Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20 (3), 273–297. https://doi.org/10.1023/A:1022627411411.
Cuartas Arias, J. M. (2017). Big data for use in psychological research. International Journal of Psychological Research, 10 (1), 6–7. https://doi.org/10.21500/20112084.2828.
Cuartas Arias, J. M. (2019). Homo digitalis and Contemporary Psychology. International journal of psychological research, 12 (2), 6–7. https://doi.org/10.21500/20112084.4260.
de Mello, F. L., & de Souza, S. A. (2019). Psychotherapy and Artificial Intelligence: A Proposal for Alignment. Frontiers in psychology, 10, 263. https://doi.org/10.3389/fpsyg.2019.00263.
Dey, A. (2016). Machine Learning Algorithms: A Review. International Journal of Computer Science and Information Technologies, 7 (3), 1174–1179.
Dhall, D., Kaur, R., & Juneja,M. (2020). Machine Learning: A Review of the Algorithms and Its Applications, In P. Singh, A. Kar, Y. Singh, M. Kolekar,&S.Tanwar (eds), Proceedings of ICRIC 2019. Lecture Notes in Electrical Engineering (pp. 47–63). Springer. https://doi.org/10.1007/978-3-030-29407-6_5.
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018). Machine Learning Approaches for Clinical Psychology and Psychiatry. Annual review of clinical psychology, 14, 91–118. https://doi.org/10.1146/annurev-clinpsy-032816-045037.
Elia, J., Arcos-Burgos, M., Bolton, K. L., Ambrosini, P. J., Berrettini, W., &Muenke, M. (2009). ADHD latent class clusters: DSM-IV subtypes and comorbidity. Psychiatry research, 170 (2–3), 192–198. https://doi.org/10.1016/j.psychres.2008.10.008.
Flis, I. (2019). Psychologists psychologizing scientific psychology: An epistemological reading of the replication crisis. Theory & Psychology, 29 (2), 158–181. https://doi.org/10.1177/0959354319835322.
Fröhlich, H., Balling, R., Beerenwinkel, N., Kohlbacher, O., Kumar, S., Lengauer, T., Maathuis, M. H., Moreau, Y., Murphy, S. A., Przytycka, T. M., Rebhan, M., Röst, H., Schuppert, A., Schwab, M., Spang, R., Stekhoven, D., Sun, J., Weber, A., Ziemek, D., & Zupan, B. (2018). From hype to reality: Data science enabling personalized medicine. BMC medicine, 16 (1), 150. https://doi.org/10.1186/s12916-018-1122-7.
Gudivada, V., Irfan, M., Fathi, E., & Rao, D. (2016). Cognitive Analytics: Going Beyond Big Data Analytics and Machine Learning. Handbook of Statistics, 35, 169–205. https://doi.org/10.1016/bs.host.2016.07.010.
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