Working paper · No. 1 · Preprint · not yet peer reviewed (be kind) · Home · Industry version →

Studying people as they change: methods for behavior over time

Xiao Yang, Ph.D.1

1 Independent Researcher · Quantitative Psychology & Data Science

Reviewer 2 suggests more feedback loops. Accepted.

Abstract. Human behavior is a moving target. I build methods from dynamic systems, networks, control theory, and machine learning to study how people change, and to design help that fits the individual. I apply them to depression, digital well-being, and everyday phone use.

1Why the individual?

Pool everyone together and a trend can point one way, while within every single person it points the other. Try it.

Yes, this really happens. Constantly.
Simulated scatterplot of four people. Pooled together, more phone time goes with lower mood. Within each person, more phone time goes with higher mood. time on phone → mood →
Figure 1. Same simulated data, opposite conclusions. The dashed line is the pooled trend; colored lines are each person's own. Studying individuals is the whole idea.

2Training and appointments

Note the U-turn from control systems to psychology. Not a detour.
Table 1. From control systems to human systems.
YearsRole · Institution
2024–nowIndependent researcher · digital well-being research with ASU, sensing apps, AI screen-time helper
2023–24Assistant Professor · Northeastern University (Health Sciences & Computer Sciences)
2021–23Sr. Data Scientist, Passive Sensing Research Lead · Mindstrong Health
2020–21Postdoctoral Researcher · Stanford University, Psychology
2015–20Ph.D., Quantitative Psychology (Human Development & Family Studies) · Penn State, with Nilam Ram
2011–15Research Associate · Beijing Academy of Educational Sciences
2009–11Quantitative Analyst · Bayview Asset Management
2007–09M.A., Statistics · Columbia University
2004–07M.S., Computer Science · Tsinghua University
2000–04B.S., Control Systems Engineering · Tsinghua University

3Findings, briefly

  1. Phones are sensors. Passive phone behavior tracks depressive symptoms when within- and between-person effects are separated.[2]
  2. Emotions form networks. Feedback loops among feelings and behaviors relate to depression and anxiety.[4][8]
  3. Systems can be nudged. Boolean networks reveal the smallest push that flips a system toward a healthier state.[3][1]
  4. Screens leave a trail. Sequences of screenshots show how color, text, and sentiment predict task switching.[5][6][7]

4Software and materials

No laptops were harmed.

POMPOM simulates a nudge and traces how it spreads through a person's emotion network.

# R
install.packages("pompom")
library(pompom)

POMPOM on CRAN · SelfTrack (Android app linking phone habits and mood) · Tutorials: Boolean networks, impulse response, multilevel models.

5References

  1. Yang, X., Albert, R., Molloy, L. E., & Ram, N. (2024). Modeling and managing behavior change in groups: A Boolean network method. advances.in/psychology. Link
  2. Yang, X., Knights, J., Bangieva, V., & Kambhampati, S. (2023). Association between the severity of depressive symptoms and human-smartphone interactions: Longitudinal study. JMIR Formative Research. Link
  3. Yang, X., Ram, N., Molenaar, P. C. M., & Cole, P. M. (2021). Describing and controlling multivariate nonlinear dynamics: A Boolean network approach. Multivariate Behavioral Research. Link · Preprint
  4. Yang, X., Ram, N., Lougheed, J. P., Molenaar, P. C. M., & Hollenstein, T. (2019). Adolescents' emotion system dynamics: Network-based analysis of physiological and emotional experience. Developmental Psychology. Publisher
  5. Yang, X., Ram, N., Robinson, T., & Reeves, B. (2019). Using screenshots to predict task switching on smartphones. CHI '19 Late Breaking Work. Publisher
  6. Ram, N., Yang, X., Cho, M. J., Brinberg, M., Muirhead, F., Reeves, B., & Robinson, T. (2019). Screenomics: A new approach for observing and studying individuals' digital lives. Journal of Adolescent Research. Link
  7. Reeves, B., Ram, N., Robinson, T., et al. (2019). Screenomics: A framework to capture and analyze personal life experiences and the ways that technology shapes them. Human-Computer Interaction. Link
  8. Yang, X., Ram, N., Gest, S., Lydon-Staley, D., Conroy, D. E., Pincus, A. L., & Molenaar, P. C. M. (2018). Socioemotional dynamics of emotion regulation and depressive symptoms: A person-specific network approach. Complexity. Publisher

6Correspondence

Email
vwendy@gmail.com
Elsewhere
LinkedIn
Collaboration
Open to research partnerships and data-science projects.
Funding
Curiosity.
Conflicts of interest
An unreasonable fondness for feedback loops.