Research Overview

My research focuses on the dynamics of decision-making, motivation, fatigue, and stress, and how these processes affect performance and mental health. I use computational, statistical, and mathematical models to understand how complex processes evolve and interact over time, and apply this knowledge to help design work environments that promote happier, healthier, and more effective work.

Practical applications have included evidence-based recommendations on alternative work concepts such as the four-day work week, enhancing the capacity and safety of unmanned aerial systems operations, providing tools to forecast fatigue in safety-critical work environments, and improving climate change communication practices.

My research program has received over $5 million in funding from the Australian Research Council and industry and has been recognised with prestigious DECRA and Future Fellowships from the Australian Research Council and research excellence awards from the Australian Psychological Society, the Australian Mathematical Psychology Society, and the University of Queensland.

My work has been published in journals such as Psychological Review, Journal of Applied Psychology, The Leadership Quarterly, Annual Review of Organizational Psychology and Organizational Behavior, Organizational Research Methods, Behavior Research Methods, Advances in Methods and Practices in Psychological Science, Psychological Science, Journal of Neuroscience, and Global Environmental Change.

My major scientific contributions have been in two areas:

01
Pioneering new methods for analysing complex human data

A major contribution of my work has been the use of cutting-edge computational, mathematical, and statistical models for generating novel insights about human behaviour from messy data. This work has led to advances in how we analyse change in people and processes over time, and how we assess heterogeneity across individuals and groups.

Ballard, T., Palada, H., Griffin, M., & Neal, A. (2021). An integrated approach to testing dynamic, multilevel theory: Using computational models to connect theory, model, and data.
Organizational Research Methods, 24(2), 251–284
Ballard, T., Luckman, A., & Konstantinidis, E. (2023). A systematic investigation into the reliability of inter-temporal choice model parameters.
Psychonomic Bulletin & Review, 30, 1294–1322
Ballard, T., Palada, H., & Neal, A. (2023). Fitting computational models to data: A tutorial.
In Computational Modeling for Industrial-Organizational Psychologists (pp. 255–296). Taylor & Francis
Knight, E., Neal, A., Palada, H., & Ballard, T. (2023). A tutorial on Bayesian modeling of change across time, individuals, and groups.
Computational Brain & Behavior, 6, 697–718
Neal, A., Ballard, T., & Palada, H. (2023). How to publish and review a computational model.
In Computational Modeling for Industrial-Organizational Psychologists (pp. 297–319). Taylor & Francis
Ballard, T., Evans, N. J., Fisher, G., & Sewell, D. K. (2024). Using mixture modeling to examine differences in perceptual decision-making as a function of the time and method of participant recruitment.
Behavior Research Methods, 56(3), 2194–2212
02
Uncovering new insights about decision-making, self-regulation, and fatigue

My work has also improved our understanding of how people manage competing demands on their time, effort, and other resources, and the implications these decisions have on motivation, fatigue, and mental health. My team has developed computational models to simulate the way people make task prioritisation decisions when under time pressure, how rostering and work patterns influence long-term fatigue trajectories among shift workers in safety-critical environments, and how the number of hours people work shapes their mental health over time.

Ballard, T., Yeo, G., Loft, S., Vancouver, J. B., & Neal, A. (2016). An integrative formal model of motivation and decision making: The MGPM*.
Journal of Applied Psychology, 101(9), 1240–1265
Neal, A., Ballard, T., & Vancouver, J. B. (2017). Dynamic self-regulation and multiple-goal pursuit.
Annual Review of Organizational Psychology and Organizational Behavior, 4(1), 401–423
Ballard, T., Vancouver, J. B., & Neal, A. (2018). On the pursuit of multiple goals with different deadlines.
Journal of Applied Psychology, 103(11), 1242–1264
Wilson, M. K., Ballard, T., Strickland, L., Boeing, A. A., Cham, B., Griffin, M. A., & Jorritsma, K. (2021). Understanding fatigue in a naval submarine: Applying biomathematical models and workload measurement in an intensive longitudinal design.
Applied Ergonomics, 94, 103412
Ballard, T., Neal, A., Farrell, S., Lloyd, E., Lim, J., & Heathcote, A. (2022). A general architecture for modeling the dynamics of goal-directed motivation and decision-making.
Psychological Review, 129(1), 146–174
Wilson, M. K., Strickland, L., Ballard, T., & Griffin, M. A. (2022). The next generation of fatigue prediction models: Evaluating current trends in biomathematical modelling.
Theoretical Issues in Ergonomics Science, 24(1), 21–43
Wilson, M., Strickland, L., & Ballard, T. (2020). FIPS: An R package for biomathematical modelling of human fatigue related impairment.
Journal of Open Source Software, 5, 2340
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