Integrating Machine Learning Into The Behavioral Sciences

The first article is about integrating machine learning into the behavioral sciences. The biological, biomedical, and behavioral sciences are currently gathering more data than ever before, driven by ground-breaking technological advancements. To improve human health, it is imperative to develop time- and money-effective ways for analyzing and interpreting these data. A unique opportunity in this regard is provided by the recent development of machine learning as a potent method for integrating multimodality, and multi-fidelity data, and exposing connections between related events. Machine learning alone, however, disregards the fundamental rules of physics and may provide ill-posed issues or illogical answers. Finding processes that explain the genesis of function through the use of multiscale modeling is an effective method for integrating multiscale data (Alber et al., 2019). Multiscale modeling, however, frequently falls short of effectively combining huge datasets from many sources and resolution levels. In this instance, the study shows how machine learning and multiscale modeling can naturally complement one another to provide reliable prediction models that incorporate the underlying physics to handle poorly-posed issues and explore vast design spaces. Ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches are four overarching topical areas where authors review the current literature, highlight applications, and opportunities, address open questions, and talk about potential challenges and limitations.

Researchers use their knowledge of applied math, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experiments, and medicine to achieve these objectives. Understanding systems for which the underlying data are insufficient and the physics are not yet fully known is a significant difficulty in the biological, biomedical, and behavioral sciences. In other words, scientists could use machine learning to explore design spaces and find correlations with a full collection of high-resolution data. Researchers could use multiscale modeling to forecast system dynamics and find causality with a validated and calibrated set of physics equations and material characteristics (Alber et al., 2019). They can maximize the potential of both machine learning and multiscale modeling by combining them, with the ultimate aim of offering quantitative predictive insight into biological systems. The biological, medicinal, and behavioral sciences frequently use standard differential equations in time. This is largely because observations and data collection are relatively simple at the molecular, cellular, organ, or population scales without taking into consideration geographical heterogeneity, which is frequently more challenging to obtain.

The second article, which was retrieved from the Bellevue University Library, is about behavioral science related to personality in the context of machine learning and big data. I was able to identify this article by using the search system in the online library. I have typed the relevant keywords and looked for the articles that suited my topic. I used Bellevue University’s online library to search for the article. If I was expected to write a 10-page paper on the topic that is covered in this article, some university resources like libraries, professors’ assistance, and study places would be available for me to complete the paper. If a peer in one of my courses was having difficulty identifying a research topic, I would go for a consultation with the professor of the course.

The chosen article is based on the analysis of fourteen empirical studies. In summary, those articles offer a preview of what our discipline could be able to do in the future when researchers want to use multimodal behavioral sensing, big data, machine learning, or artificial intelligence approaches. They also contain several cautions and warnings, and they inform us of the best ways to take advantage of the numerous chances while also navigating the numerous difficulties scientists can encounter. In the years to come, algorithm-based approaches to personality research and assessment will probably continue to grow in popularity and attract more attention and applications. A more behavioral science of personality in the era of multimodal sensing methods, big data, machine learning, and artificial intelligence is explored in the current special issue (Rauthmann, 2020). The paper emphasizes that despite the value of multidisciplinary and interdisciplinary study and collaboration, researchers must never lose sight of our interest in or use of psychological variables when concluding.

Among the observations made by the article, it was stated that it’s crucial to use strict guidelines for rigorous psychometric testing (such as evaluating various types of reliability and validity) when extracting algorithm-based trait or state scores from observed behavioral and large data. For instance, when analyzing convergences between self-reported trait scores and algorithm-based trait scores (e.g., retrieved from digital footprints), less focus was previously given on also demonstrating discriminant validity. Even though some types of reliability might be simpler to get due to ever-more-accurate measurements (such as via sensing signals and wearables), validity is a major challenge. Next, it was observed that the three main objectives of psychology in general—description, explanation, and prediction—are sometimes conflated or not clearly distinguished from one another. According to the author, some types of personality psychology may be prone to some confusion, “but they become even more pressing when using sensed behavioral and big data approaches.” (Rauthmann, 2020, p. 595). It may be debatable whether scientists can identify these three in psychology at all, given that they may meld together and even serve as one another.


Alber, M., Buganza Tepole, A., Cannon, W. R., De, S., Dura-Bernal, S., Garikipati, K., Karniadakis, G., Lytton, W. W., Perdikaris, P., Petzold, L. & Kuhl, E. (2019). Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. NPJ digital medicine, 2(1), 1-11.

Rauthmann, J. F. (2020). A (more) behavioural science of personality in the age of multi–modal sensing, big data, machine learning, and artificial intelligence. European Journal of Personality, 34(5), 593-598.