Machine Learning based Psychology: Advocating for A Data-Driven Approach
PDF

Keywords

Machine Learning Psychology

How to Cite

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

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.

https://doi.org/10.21500/20112084.5365
PDF

References

Acosta, M. T., Vélez, J. I., Bustamante, M. L., Balog, J. Z., Arcos-Burgos, M., & Muenke, M. (2011). A two-locus genetic interaction between LPHN3 and 11q predicts ADHD severity and long-term outcome. Translational psychiatry, 1 (7), e17. https://doi.org/10.1038/tp.2011.14.

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.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.